publications

2023

1. APEN

   Risk-aware sizing and transactive control of building portfolios with thermal energy storage

   Yu, Min Gyung, and Pavlak, Gregory S.

   Applied Energy 2023

   Abs Bib HTML

   With the increasing numbers of distributed energy resources are being installed at more distributed locations, it is challenging to operate the electric grid systems efficiently against the demand and supply fluctuations. Moreover, extreme conditions could result in high expenditure and grid unreliability. To address this issue, this work focused on developing a risk-averse stochastic optimal dispatch model for building portfolios with thermal energy storage using conditional value at risk to avoid expected expensive financial loss at the tail of cost distribution. Two simulation case studies were performed to assess the performance of the risk-averse controller depending on the TES sizing and quantify the value of the proposed framework compared to the deterministic approach. The results demonstrated that the risk-averse controller could benefit from larger TES sizing as storing excess energy for the emergent realizations. Overall, 42.4% of cost savings was achieved by applying the risk-averse control strategy.

   @article{YU2023120474,
     abbr = {APEN},
     bibtex_show = {true},
     title = {Risk-aware sizing and transactive control of building portfolios with thermal energy storage},
     journal = {Applied Energy},
     volume = {332},
     pages = {120474},
     year = {2023},
     issn = {0306-2619},
     doi = {https://doi.org/10.1016/j.apenergy.2022.120474},
     html = {https://doi.org/10.1016/j.apenergy.2022.120474},
     url = {https://www.sciencedirect.com/science/article/pii/S0306261922017317},
     author = {Yu, Min Gyung and Pavlak, Gregory S.},
     keywords = {Conditional value at risk, Demand response, Transactive control, Stochastic optimization, Thermal energy storage}
   }

2. ENB

   Evaluating optimal control of active insulation and HVAC systems in residential buildings

   Sepehri, Amin, and Pavlak, Gregory S.

   Energy and Buildings 2023

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   Recently, active insulation systems (AIS) have been conceptualized in building envelopes to optimally modulate thermal resistance in response to changing environmental conditions. Building flexibility can further be improved if the building is also equipped with optimized heating, ventilating, and air conditioning (HVAC) control. In this work, we investigate the annual potential benefits of jointly optimizing AIS and HVAC system controls in both heating and cooling days over all climate zones (CZs) in the U.S. To reduce the computational complexity of applying model predictive control (MPC) to annual operations and detailed whole-building energy models, timeseries clustering was used to identify a set of representative days for optimizing in each climate zone. To isolate the increase in benefits from this joint optimization, we compare the performance to cases where the AIS and HVAC controls are optimized separately. Results indicate savings potential in all CZs, with the largest annual average savings of 9.02% and 4.02% observed in the cooling days with large daily temperature swings and heating days with cold sunny conditions, respectively. Savings patterns across climate zone, day types, and HVAC modes (i.e., heating or cooling) are also discussed along with the implications of important system design variables.

   @article{SEPEHRI2023112728,
     abbr = {ENB},
     bibtex_show = {true},
     title = {Evaluating optimal control of active insulation and HVAC systems in residential buildings},
     journal = {Energy and Buildings},
     volume = {281},
     pages = {112728},
     year = {2023},
     issn = {0378-7788},
     doi = {https://doi.org/10.1016/j.enbuild.2022.112728},
     html = {https://doi.org/10.1016/j.enbuild.2022.112728},
     url = {https://www.sciencedirect.com/science/article/pii/S0378778822008994},
     author = {Sepehri, Amin and Pavlak, Gregory S.},
     keywords = {Active insulation, Model predictive control, Thermal energy storage, Passive thermal storage, Building thermal mass, Optimal control, Grid-interactive buildings}
   }

3. JESB

   Analyzing harmony and discord among optimal building controllers responding to energy, cost, and carbon reduction objectives

   Li, Lily X., and Pavlak, Gregory S.

   ASME Journal of Engineering for Sustainable Buildings and Cities Mar 2023

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   Optimization and control of building thermal energy storage holds great potential for unlocking demand-side flexibility, an asset that is being given much attention in current grid reforms responding to the climate crisis. As greater information regarding grid operations is becoming available, grid-interactive building controls inherently have become a multi-objective problem. Typical multi-objective optimization frameworks often introduce greater complexity and computational burden and are less favorable for achieving widespread adoption. With the overall goal of easing deployment of advanced building controls and aiding the building-to-grid integration, this work aims to evaluate the trade-offs and degrees of sub-optimality introduced by implementing single-objective controllers only. We formulate and apply a detailed single-objective, model predictive control (MPC) framework to individually optimize building thermal storage assets of two types of commercial buildings, informed by future grid scenarios, around energy, economic, environmental, and peak demand objectives. For each day, we compare the building’s performance in every category as if it had been controlled by four separate single-objective model predictive controllers. By comparing the individual controllers for each day, we reveal the level of harmony or discord that exists between these simple single-objective problems. In essence, we quantify the potential loss that would occur in three of the objectives if the optimal control problem were to optimally respond to only one of the grid signals. Results show that on most days, the carbon and energy controllers retained most of the savings in energy, cost, and carbon. Trade-offs were observed between the peak demand controller and the other objectives, and during extreme energy pricing events. These observations are further discussed in terms of their implications for the design of grid-interactive building incentive signals and utility tariffs.

   @article{10.1115/1.4056962,
     abbr = {JESB},
     bibtex_show = {true},
     author = {Li, Lily X. and Pavlak, Gregory S.},
     title = {{Analyzing harmony and discord among optimal building controllers responding to energy, cost, and carbon reduction objectives}},
     journal = {ASME Journal of Engineering for Sustainable Buildings and Cities},
     volume = {4},
     number = {1},
     year = {2023},
     month = mar,
     issn = {2642-6641},
     doi = {10.1115/1.4056962},
     url = {https://doi.org/10.1115/1.4056962},
     html = {https://doi.org/10.1115/1.4056962},
     note = {011002},
     eprint = {https://asmedigitalcollection.asme.org/sustainablebuildings/article-pdf/4/1/011002/6996242/jesbc\_4\_1\_011002.pdf}
   }

4. Solar

   Site suitability analysis for implementing solar PV power plants using GIS and fuzzy MCDM based approach

   Almasad, Abdullah, Pavlak, Gregory, Alquthami, Thamer, and Kumara, Soundar

   Solar Energy 2023

   Abs Bib HTML

   Although Saudi Arabia can benefit from implementing photovoltaic (PV) solar power projects to generate power, there are some environmental, economic and technical challenges which can affect the efficiency and cost effectiveness of these facilities. The goal of this paper is to build a site suitability model to identify the suitable sites for implementing solar PV solar projects in Saudi Arabia. Fuzzy analytical hierarchy process (AHP), as a weighting technique, and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE II) method are combined to appropriately evaluate the suitable sites. 12 factors divided into two criteria (technical and economical) were incorporated to ensure minimization of construction costs while maximizing power output from the PV power plants. The resulting suitability map shows that Saudi Arabia has huge potential for implementing solar PV projects with approximately 376,623 km2 (65.1 %) of the total studied area considered, “most to highly suitable”. In addition, a validation of the model’s predictivity was conducted through an evaluation of the suitability map with respect to the future solar PV projects that Saudi Arabia is developing. The results showed that 90.6 % of the future projects fell within, “most and highly suitable” areas provided by PROMETHEE II suitability map. Furthermore, a sensitivity analysis was carried out by using different preference functions and higher weights for the economic criteria to examine the effect of economic factors toward the suitability results.

   @article{ALMASAD2023642,
     abbr = {Solar},
     bibtex_show = {true},
     title = {Site suitability analysis for implementing solar PV power plants using GIS and fuzzy MCDM based approach},
     journal = {Solar Energy},
     volume = {249},
     pages = {642-650},
     year = {2023},
     issn = {0038-092X},
     doi = {https://doi.org/10.1016/j.solener.2022.11.046},
     html = {https://doi.org/10.1016/j.solener.2022.11.046},
     url = {https://www.sciencedirect.com/science/article/pii/S0038092X22008660},
     author = {Almasad, Abdullah and Pavlak, Gregory and Alquthami, Thamer and Kumara, Soundar},
     keywords = {Geographical Information Systems, Multi-Criteria Decision Making, Fuzzy Analytical Hierarchy Process, Site Suitability Analysis}
   }

2022

1. JESB

   Quantifying the value of stochastic supervisory controller for building thermal energy storage aggregators in two-settlement grid markets

   Yu, Min Gyung, and Pavlak, Gregory S.

   ASME Journal of Engineering for Sustainable Buildings and Cities Nov 2022

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   Smart cities will need collections of buildings that are responsive to the variation in renewable energy generation. However, an unprecedented level of renewable energy being added to the power grid compounds the level of uncertainties in making decisions for reliable grid operation. Making autonomous decisions regarding demand management requires consideration of uncertainty in the information available for planning and executing operations. Thus, this paper aims to quantitatively analyze the performance of supervisory controllers for multiple grid-integrative buildings with thermal energy storage depending on the quality of information available. Day-ahead planning and real-time model predictive controllers were developed and compared across 50 validation scenarios when given perfect information, deterministic forecasts, and stochastic forecasts. Despite the relatively large uncertainty in the stochastic forecasts, marked improvements were observed when a stochastic optimization was solved for both the day-ahead and real-time problems. This observation underscores the need for continued development in the area of stochastic control and decision-making for future grid-interactive buildings and improved energy management of smart cities.

   @article{10.1115/1.4056023,
     abbr = {JESB},
     bibtex_show = {true},
     author = {Yu, Min Gyung and Pavlak, Gregory S.},
     title = {{Quantifying the value of stochastic supervisory controller for building thermal energy storage aggregators in two-settlement grid markets}},
     journal = {ASME Journal of Engineering for Sustainable Buildings and Cities},
     volume = {3},
     number = {3},
     year = {2022},
     month = nov,
     issn = {2642-6641},
     doi = {10.1115/1.4056023},
     html = {https://doi.org/10.1115/1.4056023},
     note = {031002},
     eprint = {https://asmedigitalcollection.asme.org/sustainablebuildings/article-pdf/3/3/031002/6941534/jesbc\_3\_3\_031002.pdf},
     pdf = {PreprintUATXAggregator.pdf}
   }

2. BuildSys

   Constrained differentiable cross-entropy method for safe model-based reinforcement learning

   Mottahedi, Sam, and Pavlak, Gregory S.

   In Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation 2022

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   Reinforcement learning agents must explore their environments to learn optimal policies through trial and error. Due to challenges in simulating the complexities of the real world, there is a growing trend of training reinforcement learning (RL) agents directly in the real world instead of mostly or entirely in simulation. Safety concerns are paramount when training RL agents directly in the real world. This paper proposes MPC-CDCEM, a model-based reinforcement algorithm (RL) that allows the agent to safely interact with the environment and explore without additional assumptions on system dynamics. The algorithm uses a Model Predictive Control (MPC) framework with a differentiable cross-entropy optimizer, which induces a differentiable policy that considers the constraints while addressing the objective mismatch problem in model-based RL algorithms. We evaluate our algorithm in Safety Gym environments and on a practical building energy optimization problem. In addition, we showed that in both experiments, our algorithms have the lowest number of constraint violations and achieve comparable rewards compared to baseline constrained RL algorithms.

   @inproceedings{10.1145/3563357.3564055,
     abbr = {BuildSys},
     bibtex_show = {true},
     pdf = {PreprintCDCEM.pdf},
     author = {Mottahedi, Sam and Pavlak, Gregory S.},
     title = {Constrained differentiable cross-entropy method for safe model-based reinforcement learning},
     year = {2022},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3563357.3564055},
     html = {https://doi.org/10.1145/3563357.3564055},
     doi = {10.1145/3563357.3564055},
     booktitle = {Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation},
     pages = {40–48},
     numpages = {9},
     keywords = {constrained Markov decision process, limited multi-label classification, reinforcement learning, differentiable convex optimization, cross-entropy methods},
     location = {Boston, Massachusetts},
     series = {BuildSys '22}
   }

3. SimAUD

   Dynamic subset sensitivity analysis for design exploration

   Hinkle, Laura, Pavlak, Gregory S., Brown, Nathan, and Curtis, Leland

   In 2022 Annual Modeling and Simulation Conference (ANNSIM) Jul 2022

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   This paper presents a method for dynamically assessing parametric variable importance and likely influence on performance objectives as a large, precomputed design space is filtered down to explore more specific problems. Custom parametric models coupled with performance simulation can support early design, but they can be inflexible and are not always created in practice due to time and other constraints. Large parametric datasets of previously simulated design subproblems could thus make performance-based modeling more accessible, but they can have too much information and fail to focus on supporting design decisions for specific variables and ranges. Using a parametric daylight room model as an example, we first train a linear model tree. As variable bounds are filtered and adjusted by a designer, remaining coefficients are interpolated to provide an adjusted variable importance for the new domain.

   @inproceedings{9859293,
     abbr = {SimAUD},
     bibtex_show = {true},
     pdf = {67_Paper_DYNAMIC-SUBSET-SENSITIVITY-ANALYSIS-FOR-DESIGN-EXPLORATION-2.pdf},
     author = {Hinkle, Laura and Pavlak, Gregory S. and Brown, Nathan and Curtis, Leland},
     booktitle = {2022 Annual Modeling and Simulation Conference (ANNSIM)},
     title = {Dynamic subset sensitivity analysis for design exploration},
     year = {2022},
     month = jul,
     volume = {},
     number = {},
     pages = {581-592},
     doi = {10.23919/ANNSIM55834.2022.9859293},
     html = {https://doi.org/10.23919/ANNSIM55834.2022.9859293}
   }

4. BPAC

   Evaluating the performance of chiller plant efficiency using random forest model: a high-rise building case study

   Rizi, Behzad, Heidarinejad, Mohammad, Pavlak, Gregory, Cushing, Vincent, and Hederman, William

   In 2022 Building Performance Analysis Conference and SimBuild co-organized by ASHRAE and IBPSA-USA Sep 2022

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   Assessing electricity consumption of chilled-water cooling plants is essential for near-optimal operation and carbon emission reduction. The goal of this study is to develop an efficient chiller sequencing control strategy for different building operating conditions. To that end, this study aims to develop three Random Forest (RF) chiller models for predicting chiller power consumption and two more efficient chiller sequencing control strategies for a 1.3 million ft 2 high-rise commercial office building located in New York City. Chiller cooling load, chiller power consumption, and ambient wet bulb temperature were logged at 15-min intervals in May-September 2019, and used to train RF models for analyzing the two more efficient chiller sequencing strategies. The average value of mean absolute percentage error (MAPE) and root mean squared error (RMSE) for all three RF chiller models are 5.3% and 30 kW, respectively, for the validation dataset, which confirms a good agreement between measured and predicted values. Results of this study provide additional insights on how to accurately predict the total chiller power consumption of cooling plants under different chiller sequencing control strategies.

   @inproceedings{simbuild2022_C028,
     abbr = {BPAC},
     bibtex_show = {true},
     pdf = {simbuild2022_C028.pdf},
     html = {https://doi.org/10.26868/25746308.2022.C028},
     url = {https://publications.ibpsa.org/conference/paper/?id=simbuild2022_C028},
     year = {2022},
     month = sep,
     publisher = {ASHRAE/IBPSA-USA},
     author = {Rizi, Behzad and Heidarinejad, Mohammad and Pavlak, Gregory and Cushing, Vincent and Hederman, William},
     title = {Evaluating the performance of chiller plant efficiency using random forest model: a high-rise building case study},
     booktitle = {2022 Building Performance Analysis Conference and SimBuild co-organized by ASHRAE and IBPSA-USA},
     volume = {10},
     isbn = {978-1-955516-21-1},
     address = {Chicago, Illinois},
     series = {ASHRAE/IBPSA-USA Building Simulation Conference},
     language = {en-US},
     pages = {234--242},
     issn = {2574-6308},
     organisation = {ASHRAE/IBPSA-USA},
     editors = {}
   }

5. ASHRAE

   Joint optimization of HVAC and active insulation control strategies in residential buildings

   Sepehri, Amin, and Pavlak, Gregory S.

   In 2022 ASHRAE Annual Conference Jun 2022

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   Increasing building construction thermal resistance has been shown to be an effective traditional way ofimproving building energy efficiency. In this approach, the opaque building envelope is often deemed to be a static system that keeps the indoor environment isolated from the outdoor environment. More recently, active insulation systems (AIS) have been conceptualized to enable active control over building envelope thermal properties, allowing the thermal resistance to be optimally modulated in response to changing environmental conditions. Dynamically tuning the building thermal performance can lead to increased energy savings, reduced operating costs, and reductions in operational carbon emissions. When paired with thermal mass, AIS can also increase building load flexibility for providing demand response and grid services. Building flexibility can further be improved if the building is also equipped with optimized HVAC system controls. In this configuration, the AIS and HVAC systems can be beneficially coordinated to optimally condition the space based on the desired operational objectives. In this work, we quantify the potential benefits ofjointly optimizing AIS and HVAC system controls by applying model predictive control to a detailed whole-building energy model. The example building is an all-electric single-family residential building, satisfying IECC 2012, in Baltimore, MD. To highlight the increase in benefits from jointly optimizing HVAC and dynamic envelope systems, we compare the results to the performance achieved when optimizing HVAC and AIS systems individually. Results showed that the combined optimization of HVAC and AIS control was able to achieve 13% peak demand reduction, while savings 3% energy. This is in contrast to only a 10% peak reduction and 16% increase in energy use for the case with optimized HVAC only. These results ultimately motivate further exploration, integration, and joint optimization ofdynamic envelope and HVAC system components.

   @inproceedings{2022_ASHRAE_Annual_Sepehri,
     abbr = {ASHRAE},
     bibtex_show = {true},
     pdf = {Joint Optimization of HVAC and Active Insulation Control Strategies in Residential Buildings.pdf},
     html = {https://scholarsphere.psu.edu/resources/3d364e94-2b6d-422c-a521-a2093d898937},
     url = {https://scholarsphere.psu.edu/resources/3d364e94-2b6d-422c-a521-a2093d898937},
     year = {2022},
     month = jun,
     publisher = {ASHRAE},
     author = {Sepehri, Amin and Pavlak, Gregory S.},
     title = {Joint optimization of HVAC and active insulation control strategies in residential buildings},
     booktitle = {2022 ASHRAE Annual Conference},
     address = {Toronto, Ontario},
     language = {en-US},
     pages = {1--8},
     organisation = {ASHRAE}
   }

6. JES

   Uncertainty-aware optimal dispatch of building thermal storage portfolios via smoothed variance-reduced accelerated gradient methods

   Yu, Min Gyung, Pavlak, Gregory S., and Shanbhag, Uday V.

   Journal of Energy Storage 2022

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   The rapid penetration of uncertain renewable energy resources into the power grid has made generation planning and real-time power balancing a challenge, prompting the need for advanced control on the demand-side. Although a grid-integrated building portfolio has been studied by coordinating building-level flexible energy resources, to reap further benefits, the need to develop a scalable supervisory control framework with computational efficiency is paramount. In this work, we introduce an uncertainty-aware transactive control framework for a large-scale building portfolio with thermal energy storage (TES) using a decomposition-based approach. We propose a day-ahead decision making framework for the power procurement problem, which is cast as a two-stage stochastic optimization problem. To solve this problem, we propose a smoothed variance-reduced accelerated gradient method. Notably this framework allows for parallelization in computing the sampled gradient. Preliminary numerics demonstrate the scalability and computational efficiency of the proposed algorithm to apply to the control framework with respect to the existing algorithm. Substantial energy cost savings were observed for the stochastic control framework over all the validation scenarios. This study provides further insights into the supervisory controller development for building portfolio-based grid services that can help advance electrification goals through coordination of energy storage assets.

   @article{YU2022104405,
     abbr = {JES},
     bibtex_show = {true},
     title = {Uncertainty-aware optimal dispatch of building thermal storage portfolios via smoothed variance-reduced accelerated gradient methods},
     journal = {Journal of Energy Storage},
     volume = {51},
     pages = {104405},
     year = {2022},
     issn = {2352-152X},
     html = {https://doi.org/10.1016/j.est.2022.104405},
     url = {https://www.sciencedirect.com/science/article/pii/S2352152X22004297},
     author = {Yu, Min Gyung and Pavlak, Gregory S. and Shanbhag, Uday V.},
     keywords = {Grid-interactive buildings, Smoothed variance-reduced accelerated gradient method, Stochastic optimization, Thermal energy storage, Transactive control, Uncertainty}
   }

7. ECM

   An optimization framework for the network design of advanced district thermal energy systems

   Allen, Amy, Henze, Gregor, Baker, Kyri, Pavlak, Gregory, and Murphy, Michael

   Energy Conversion and Management 2022

   Abs Bib HTML

   In this work, a topology optimization framework for district thermal energy systems is presented. The framework seeks to address the questions, for a given district, “What is the best subset of buildings to connect to a district thermal energy system, and by what network should they be connected, to minimize life cycle cost?” A particle swarm optimization approach is validated to address the selection of the subset of buildings, and a graph theory-based heuristic is validated for selection of the network topology for any candidate subset of buildings. The framework is applied to a prototypical urban district for illustrative purposes. Modeling of prototypical districts revealed reductions in source energy use intensity for heating and cooling of 21–25% through the use of advanced district energy systems relative to code-compliant, building level systems. The framework identifies solutions with life cycle cost values 14% to 72% lower than that of base case scenarios based on conventional design approaches, depending on the base case scenario selected. Analysis of the search space indicates that topology optimization facilitates reductions in life cycle cost, source energy use intensity, and carbon emissions.

   @article{ALLEN2022115839,
     abbr = {ECM},
     bibtex_show = {true},
     title = {An optimization framework for the network design of advanced district thermal energy systems},
     journal = {Energy Conversion and Management},
     volume = {266},
     pages = {115839},
     year = {2022},
     issn = {0196-8904},
     html = {https://doi.org/10.1016/j.enconman.2022.115839},
     url = {https://www.sciencedirect.com/science/article/pii/S0196890422006355},
     author = {Allen, Amy and Henze, Gregor and Baker, Kyri and Pavlak, Gregory and Murphy, Michael},
     keywords = {District thermal energy systems, Ambient loops, Topology optimization, Particle swarm optimization}
   }

8. JAE

   Model-based testbed for uncertainty quantification in building control systems with advanced sequences of operation

   Haleem, Shadi Abdel, Pavlak, Gregory S., and Bahnfleth, William P.

   Journal of Architectural Engineering 2022

   Abs Bib HTML

   As advanced control sequences are developed to improve the operational efficiency of buildings, it is important to better understand the implications of uncertainty on system design and specification, and its propagation through system components to various performance measures. This paper describes the detailed development of a testbed for performing uncertainty quantification in heating, ventilating, and air-conditioning (HVAC) system operational parameters, which includes local loop controller dynamics and detailed control sequences at small time scales. The testbed was developed using a Modelica-based building model that allows controllers to be accurately simulated along with the building heat transfer physics. The model is demonstrated by applying it to uncertainty quantification in annual site electricity use and internal zone conditions, due to the inherent inaccuracies in system sensors and actuators. The results and the testbed are intended to aid others in the research community who may need a similar HVAC controls simulation testbed.

   @article{HALEEMJAE2022,
     abbr = {JAE},
     bibtex_show = {true},
     author = {Haleem, Shadi Abdel and Pavlak, Gregory S. and Bahnfleth, William P.},
     title = {Model-based testbed for uncertainty quantification in building control systems with advanced sequences of operation},
     journal = {Journal of Architectural Engineering},
     volume = {28},
     number = {2},
     pages = {04022007},
     year = {2022},
     doi = {10.1061/(ASCE)AE.1943-5568.0000532},
     html = {https://doi.org/10.1061/(ASCE)AE.1943-5568.0000532},
     url = {https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29AE.1943-5568.0000532}
   }

9. Energy

   Extracting interpretable building control rules from multi-objective model predictive control data sets

   Yu, Min Gyung, and Pavlak, Gregory S.

   Energy 2022

   Abs Bib HTML

   Developing intelligent building control strategies is increasingly becoming a multi-objective problem as owners, occupants, and operators seek to balance performance across energy, operating expense, environmental concerns, indoor environmental quality, and electric grid incentives. Implementing multi-objective optimal controls in buildings is challenging and often not tractable due to the complexity of the problem and the computational burden that frequently accompanies such optimization problems. In this work, we extract near-optimal rule sets from a database of non-dominated solutions, created by applying multi-objective model predictive control to detailed EnergyPlus models. We first apply multi-criteria decision analysis to rank the non-dominated solutions and select a subset of consistent and plausible operating strategies that can satisfy operator or occupant preferences. Next, unsupervised clustering is applied to highlight recurring control patterns. In the final step, we build a supervised classification model to identify the right optimal temperature control patterns for a particular day. The performance of the simplified rule sets is then quantified through simulation. Despite the dramatically simpler form, the best rule sets were able to achieve 95-97% of the energy savings and 89-92% of the cost objective savings of the fully detailed model predictive controller, while achieving similar thermal comfort and peak electrical demand.

   @article{YU2022122691,
     title = {Extracting interpretable building control rules from multi-objective model predictive control data sets},
     journal = {Energy},
     abbr = {Energy},
     bibtex_show = {true},
     volume = {240},
     pages = {122691},
     year = {2022},
     issn = {0360-5442},
     doi = {10.1016/j.energy.2021.122691},
     html = {https://doi.org/10.1016/j.energy.2021.122691},
     url = {https://www.sciencedirect.com/science/article/pii/S0360544221029406},
     author = {Yu, Min Gyung and Pavlak, Gregory S.},
     keywords = {Clustering, Classification, Multi-objective optimization, Rule extraction, Model predictive control of buildings}
   }

2021

1. AUTCON

   Impact of control loop performance on energy use, air quality, and thermal comfort in building systems with advanced sequences of operation

   Abdel Haleem, Shadi M., Pavlak, Gregory S., and Bahnfleth, William P.

   Automation in Construction 2021

   Abs Bib HTML

   Maintaining control system performance over the lifespan of a building offers great potential for increasing system operation efficiency. Programming the building monitoring system with control loop performance assessment (CLPA) indices provides a way to identify poorly performing loops. This work further advances building control monitoring by developing an approach to help prioritize control problems based on the severity of their system-level impact. CLPA indices were added to a Modelica-based small office building model programmed with advanced heating, ventilating, and air conditioning control sequences. An extensive set of unique simulations of different levels of loop detuning were implemented to generate a database that contains both system-level performance metrics and CLPA indices. A regression model was then developed that combines individual loop performance to assess the impact on system-level outputs. Loops of the zone with higher heat gains and the air handling unit supply air temperature loop produced the greatest system-level impact.

   @article{ABDELHALEEM2021103837,
     title = {Impact of control loop performance on energy use, air quality, and thermal comfort in building systems with advanced sequences of operation},
     journal = {Automation in Construction},
     abbr = {AUTCON},
     bibtex_show = {true},
     volume = {130},
     pages = {103837},
     year = {2021},
     issn = {0926-5805},
     doi = {10.1016/j.autcon.2021.103837},
     url = {https://www.sciencedirect.com/science/article/pii/S0926580521002880},
     html = {https://doi.org/10.1016/j.autcon.2021.103837},
     author = {Abdel Haleem, Shadi M. and Pavlak, Gregory S. and Bahnfleth, William P.},
     keywords = {ASHRAE guideline 36, Control sequence, Control loop performance assessment (CLPA), Harris index, Modelica simulation, Multiple zone variable air volume (VAV) system}
   }

2. HPBC

   Assessing the value of uncertainty-aware transactive control framework for commercial and residential buildings

   Yu, Min Gyung, and Pavlak, Gregory S.

   In International High Performance Buildings Conference 2021

   Abs Bib PDF

   With the increasing adoption of renewable energy and electric vehicles in the power grid, dealing with uncertainty in both supply and demand is critical to ensuring reliable and efficient operations. In this study, we discuss the value of a two­stage stochastic control framework for an aggregator to address such problems by promoting improved decision­ making and performance despite inherent uncertainty. An uncertainty­aware transactive control framework was de­ veloped to account for uncertainties in future conditions due to occupancy patterns, weather conditions, on­site power generation, and real­time pricing schemes. In the day­ahead period, the aggregator decides the electricity procurement plan considering the possible real­time control strategies for operation of the commercial building thermal energy stor­ age (TES) assets and residential building electric water heaters. During real­time operations, the aggregator modulates controllable loads based on transactive market mechanisms with model predictive control (MPC). In order to evaluate the performance, this study quantified the expected value of perfect information (EVPI) and the value of the stochastic solution (VSS) to analyze the cost of uncertain information and potential benefits of solving the stochastic optimal control problem. This paper demonstrates how the stochastic solution of the developed framework can provide useful information for customers and grid operators in the management of uncertain situations to support grid reliability and sustainability.

   @inproceedings{YuPavlak2021HPBC,
     author = {Yu, Min Gyung and Pavlak, Gregory S.},
     abbr = {HPBC},
     bibtex_show = {true},
     booktitle = {International High Performance Buildings Conference},
     title = {Assessing the value of uncertainty-aware transactive control framework for commercial and residential buildings},
     year = {2021},
     pages = {310018},
     pdf = {https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1379&context=ihpbc},
     url = {https://docs.lib.purdue.edu/ihpbc/380}
   }

3. HPBC

   Pattern analysis of dynamic grid incentives and the implications on optimal control of building thermal energy storage

   Li, Lily X., and Pavlak, Gregory S.

   In International High Performance Buildings Conference 2021

   Abs Bib PDF

   Building thermal energy storage has been utilized for decades for various objectives, such as reducing peak electrical demand, reducing building operating expenses, and increasing the efficiency of systems when charged from waste heat or free cooling. As building thermal storage control strategies become more dynamic, optimization of building performance often considers multiple objectives that aim to improve building performance in energy, economic, en- vironmental, and grid support categories. The dynamics of the incentive signal used for one objective, as well as its relation to signals from other objectives—for instance, whether the signals are “in sync” or are “conflicting”—heavily influence the tradeoffs that may exist among performance objectives. To better understand the degree of alignment that may exist between grid incentive signals, we apply unsupervised learning to a novel grid data set that includes hourly signals for energy price and marginal carbon emissions. Clustering algorithms identify common patterns in the dynamic signals. Overall, Hierarchical Clustering demonstrated the best performance, evaluated by DB index and Silhouette score. While the algorithms did not find distinctive patterns among the carbon signals, they did identify 7 to 9 patterns within the January and July pricing signals. The highly fluctuating nature of the carbon emission signals could lead to a diverse range of tradeoffs between building energy cost and carbon emission reduction objectives, if the signals were used as the basis for a building control optimization problem. This finding iterates the importance of un- derstanding incentive signal dynamics, in both individual and collective contexts, and the implications for development of new control technologies for grid-interactive buildings.

   @inproceedings{LiPavlak2021HPBC,
     author = {Li, Lily X. and Pavlak, Gregory S.},
     abbr = {HPBC},
     bibtex_show = {true},
     booktitle = {International High Performance Buildings Conference},
     title = {Pattern analysis of dynamic grid incentives and the implications on optimal control of building thermal energy storage},
     year = {2021},
     pages = {310089},
     pdf = {https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1390&context=ihpbc},
     url = {https://docs.lib.purdue.edu/ihpbc/391}
   }

4. ASHRAE

   Evaluation of Topology Optimization to Achieve Energy Savings at the Urban District Level

   Allen, Amy, Henze, Gregor, Baker, Kyri, Pavlak, Gregory S., and Murphy, Michael

   In ASHRAE Virtual Winter Conference 2021

   Abs Bib PDF

   Advanced district thermal energy systems have the potential to achieve significant energy savings and facilitate the integration of renewable thermal resources and waste heat, contributing to reductions in carbon emissions. Such systems, also known as fifth generation district heating and cooling (5GDHC) systems, circulate water at temperatures close to ambient, and leverage electrically driven water-source heat pumps located at connected buildings to further temper the water. However, barriers exist to the adoption of 5GDHC systems, including the factorial growth in potential network configurations as a function of the number of considered buildings. Topology optimization, which seeks to answer the questions, “Which is the best subset of buildings, if any, to connect to a district thermal energy system, and by what network should they be connected, to minimize life cycle cost?” can accelerate the adoption of 5GDHC systems. This study is part of an effort to develop a topology optimization framework for district thermal energy systems. In this study, a heuristic for one important aspect of the topology optimization problem—the use of the minimal spanning tree network to connect a given set of buildings at the least life cycle cost—is validated.

   @inproceedings{AAllenASHRAE2021,
     author = {Allen, Amy and Henze, Gregor and Baker, Kyri and Pavlak, Gregory S. and Murphy, Michael},
     abbr = {ASHRAE},
     bibtex_show = {true},
     booktitle = {ASHRAE Virtual Winter Conference},
     title = {Evaluation of Topology Optimization to Achieve Energy Savings at the Urban District Level},
     year = {2021},
     pages = {1--10},
     pdf = {https://www.nrel.gov/docs/fy21osti/77625.pdf},
     url = {https://www.nrel.gov/docs/fy21osti/77625.pdf}
   }

5. APEN

   Assessing the performance of uncertainty-aware transactive controls for building thermal energy storage systems

   Yu, Min Gyung, and Pavlak, Gregory S.

   Applied Energy 2021

   Abs Bib HTML

   Energy storage systems provide a wide range of technological approaches to manage the balance between energy supply and demand in the electric grid. With the increasing uncertainty and variability that comes with wide-spread adoption of grid-scale and behind-the-meter renewable energy, it is imperative to develop stochastic operational planning and control approaches that can account for uncertainty in future conditions. Although, coordination of multiple thermal energy storage resources can support the transition to low carbon energy by enabling valuable system flexibility, few stochastic planning and control approaches have been developed for coordinating building-level thermal energy storage resources. In addition, there is also a need to analyze the potential benefits of an aggregator-level stochastic control framework versus applying stochastic planning and controls at each building individually. This work addresses these needs by developing an uncertainty-aware transactive control (UA-Tx) framework for an aggregator to coordinate the thermal energy storage (TES) assets of multiple buildings. A two-stage stochastic optimization framework is formulated for day-ahead energy procurement that considers uncertainty in building occupancy patterns, weather conditions, and real-time energy prices of the following day. In the second stage, possible recourse decisions through modifying TES operation are also considered. The dispatch of TES operational strategies is implemented through transactive controls, which use market mechanisms and customer preferences to achieve changes in building demand. During real-time operation, a local demand response aggregator determines the transactive clearing prices to dispatch the flexibility enabled by TES. Simulation case studies were conducted to demonstrate the capabilities of the uncertainty-aware aggregator control framework compared to the performance of applying intelligent controllers at each individual building. Up to 3.7% energy cost savings were observed for buildings under the UA-Tx aggregator control framework. Other potential benefits of the control approach are also discussed, along with anticipated future extensions.

   @article{YU2021116103,
     selected = {true},
     abbr = {APEN},
     title = {Assessing the performance of uncertainty-aware transactive controls for building thermal energy storage systems},
     journal = {Applied Energy},
     volume = {282},
     pages = {116103},
     year = {2021},
     doi = {10.1016/j.apenergy.2020.116103},
     url = {http://www.sciencedirect.com/science/article/pii/S0306261920315233},
     html = {https://doi.org/10.1016/j.apenergy.2020.116103},
     author = {Yu, Min Gyung and Pavlak, Gregory S.},
     bibtex_show = {true}
   }

2020

1. B2020

   Development and evaluation of occupancy-aware model predictive control for residential building energy efficiency and occupant comfort

   Turley, Christina, Jacoby, Margarite, Gregor, Henze, and Pavlak, Gregory S.

   In BEYOND 2020 – World Sustainable Built Environment Conference, IOP Conference Series: Earth and Environmental Science Nov 2020

   Abs Bib PDF

   The residential sector accounts for 25% of global primary energy consumption. Two methods have previously been proposed to reduce residential energy use associated with the provision of occupant thermal comfort: 1. Occupancy-based HVAC control, operating systems only during confirmed occupancy, and 2. model predictive control (MPC), harnessing a mathematical model and forecasts to find optimal operating strategies. Previous studies estimate the average energy savings of the two methods individually in the range of 21% and 16%, respectively. The research presented herein was carried out to evaluate the energy savings potential in residential buildings by combining both approaches across different climates, house vintages, and occupancy patterns. Occupancy and eight different physical modalities (e.g. CO2 and VOC) data were collected from five homes for time periods of 4–9 weeks. Collected data sets were used to train occupancy prediction models suggested by an extensive literature survey of occupancy model types. The trained prediction models were combined with MPC and detailed EnergyPlus building simulation models to evaluate residential building performance in terms of annual energy savings and thermal comfort, along with discomfort exceedance metrics. Multiple home types and regions were analyzed to understand regional and climate-dependent potential. Based on actual field data, the occupancy models had a prediction inaccuracy between 8% and 35% across the investigated homes. Average occupancy for the collected data ranged from 56% to 86%, a typical range reported in the literature. Building simulations were conducted for three control scenarios: conventional thermostatic control, occupancy-based, and occupancy-based MPC. The results indicate that all advanced strategies improve upon the conventional control, with some scenarios cutting energy use in half with only occasional incurrence of discomfort. The findings indicate that occupancy-aware model predictive residential building control has the potential to drastically reduce energy use and associated emissions while maintaining occupant comfort for both new and existing buildings.

   @inproceedings{Turley_2020,
     selected = {false},
     abbr = {B2020},
     bibtex_show = {true},
     doi = {10.1088/1755-1315/588/2/022043},
     url = {https://doi.org/10.1088/1755-1315/588/2/022043},
     pdf = {https://iopscience.iop.org/article/10.1088/1755-1315/588/2/022043/pdf},
     year = {2020},
     month = nov,
     publisher = {IOP Publishing},
     volume = {588},
     pages = {022043},
     author = {Turley, Christina and Jacoby, Margarite and Gregor, Henze and Pavlak, Gregory S.},
     title = {Development and evaluation of occupancy-aware model predictive control for residential building energy efficiency and occupant comfort},
     booktitle = {BEYOND 2020 -- World Sustainable Built Environment Conference, IOP Conference Series: Earth and Environmental Science}
   }

2. B2020

   A topology optimization framework to facilitate adoption of advanced district thermal energy systems

   Allen, Amy, Henze, Gregor, Baker, Kyri, Pavlak, Gregory, Long, Nicholas, and Fu, Yangyang

   In BEYOND 2020 – World Sustainable Built Environment Conference, IOP Conference Series: Earth and Environmental Science Nov 2020

   Abs Bib PDF

   Advanced district thermal energy systems, which circulate water at temperatures near ambient conditions, and facilitate the utilization of waste heat and renewable thermal sources, can lower the carbon-intensity of urban districts, advancing the U.N. Sustainable Development Goals. Optimization of the network topology — the selection of the best subset of buildings and the best network to connect them, to minimize life cycle cost — can increase adoption of these system in appropriate applications. The potential “solution space” of the topology optimization problem grows factorially with the number of buildings in the district, motivating the consideration of a design heuristic. In this study, a heuristic for the network selection was evaluated with an exhaustive search, for a prototypical four-building district. For the prototypical district considered, the heuristic was effective in selecting an optimal network topology. Additionally, it was found that, in this case, the selection of the subset of buildings was more influential on the life cycle cost than the selection of the network topology. This work is part of a larger effort to develop a topology optimization framework for district thermal energy systems, which is anticipated to address barriers to adoption of ambient-temperature systems.

   @inproceedings{Allen_2020,
     selected = {false},
     abbr = {B2020},
     bibtex_show = {true},
     doi = {10.1088/1755-1315/588/2/022054},
     url = {https://doi.org/10.1088/1755-1315/588/2/022054},
     pdf = {https://iopscience.iop.org/article/10.1088/1755-1315/588/2/022054/pdf},
     year = {2020},
     month = nov,
     publisher = {{IOP} Publishing},
     volume = {588},
     pages = {022054},
     author = {Allen, Amy and Henze, Gregor and Baker, Kyri and Pavlak, Gregory and Long, Nicholas and Fu, Yangyang},
     title = {A topology optimization framework to facilitate adoption of advanced district thermal energy systems},
     booktitle = {BEYOND 2020 -- World Sustainable Built Environment Conference, IOP Conference Series: Earth and Environmental Science}
   }

3. ECM

   Evaluation of low-exergy heating and cooling systems and topology optimization for deep energy savings at the urban district level

   Allen, Amy, Henze, Gregor, Baker, Kyri, and Pavlak, Gregory

   Energy Conversion and Management 2020

   Abs Bib HTML

   District energy systems have the potential to achieve deep energy savings by leveraging the density and diversity of loads in urban districts. However, planning and adoption of district thermal energy systems is hindered by the analytical burden and high infrastructure costs. It is hypothesized that network topology optimization would enable wider adoption of advanced (ambient temperature) district thermal energy systems, resulting in energy savings. In this study, energy modeling is used to compare the energy performance of “conventional” and “advanced” district thermal energy systems at the urban district level, and a partial exhaustive search is used to evaluate a heuristic for the topology optimization problem. For the prototypical district considered, advanced district thermal energy systems mated with low-exergy building heating and cooling systems achieved a source energy use intensity that was 49% lower than that of conventional systems. The minimal spanning tree heuristic was demonstrated to be effective for the network topology optimization problem in the context of a prototypical district, and contributes to mitigating the problem’s computational complexity. The work presented in this paper demonstrates the potential of advanced district thermal energy systems to achieve deep energy savings, and advances to addressing barriers to their adoption through topology optimization.

   @article{ALLEN2020113106,
     selected = {false},
     abbr = {ECM},
     bibtex_show = {true},
     title = {Evaluation of low-exergy heating and cooling systems and topology optimization for deep energy savings at the urban district level},
     journal = {Energy Conversion and Management},
     volume = {222},
     pages = {113106},
     year = {2020},
     issn = {0196-8904},
     doi = {https://doi.org/10.1016/j.enconman.2020.113106},
     url = {https://www.sciencedirect.com/science/article/pii/S0196890420306506},
     html = {https://doi.org/10.1016/j.enconman.2020.113106},
     author = {Allen, Amy and Henze, Gregor and Baker, Kyri and Pavlak, Gregory},
     keywords = {Fifth-generation district heating and cooling systems, Topology analysis, Hydraulic network modeling}
   }

4. APEN

   Sizing and dispatch of an islanded microgrid with energy flexible buildings

   Swaminathan, Siddharth, Pavlak, Gregory S., and Freihaut, James

   Applied Energy 2020

   Abs Bib HTML

   Deregulation of the electricity sector, the rise of distributed generation, and a growing interest in local resilience have led to increasing attention on microgrids. In this paper, we present an approach for sizing the microgrid components that accounts for the load flexibility available in buildings with model predictive control. As buildings are becoming smarter, the use of building control systems to regulate building load for different strategies, such as peak demand limiting or load shifting, is becoming increasingly prevalent. When sizing microgrid components under islanded operation, it becomes critical to consider the dynamic nature of the building load, since the intelligent control systems can use the building response to help balance energy flows. An optimal sizing and dispatch model of the microgrid with model predictive control is developed. Simulations are carried out for representative days for a building-level microgrid serving a medium-sized commercial building. Results show that savings in first costs and operational costs can be realized if advanced controls are considered during design and component selection.

   @article{SWAMINATHAN2020115355,
     selected = {false},
     abbr = {APEN},
     bibtex_show = {true},
     title = {Sizing and dispatch of an islanded microgrid with energy flexible buildings},
     journal = {Applied Energy},
     volume = {276},
     pages = {115355},
     year = {2020},
     issn = {0306-2619},
     doi = {https://doi.org/10.1016/j.apenergy.2020.115355},
     url = {https://www.sciencedirect.com/science/article/pii/S0306261920308679},
     html = {https://doi.org/10.1016/j.apenergy.2020.115355},
     author = {Swaminathan, Siddharth and Pavlak, Gregory S. and Freihaut, James},
     keywords = {Model predictive control, Microgrid sizing, Optimal sizing and dispatch, Islanded operation, Building load flexibility}
   }

5. ENB

   Performance of advanced control sequences in handling uncertainty in energy use and indoor environmental quality using uncertainty and sensitivity analysis for control components

   Abdel Haleem, Shadi M., Pavlak, Gregory S., and Bahnfleth, William P.

   Energy and Buildings 2020

   Abs Bib HTML

   Control sequences for air distribution and terminal systems in heating, ventilating, and air-conditioning (HVAC) aim to achieve a balance in the system outputs, i.e., maintain thermal comfort and indoor air quality (IAQ) with minimal energy use. ASHRAE Guideline 36 (G36) – High-Performance Sequences of Operation for HVAC Systems, is the result of ASHRAE research project 1455-RP intended to develop standardized sequences of operation to achieve more effective use of existing controls. This paper complements G36 by evaluating the influence of the uncertainty inherent in the control components (e.g. sensors and actuators) on the system outputs of a multiple zone variable air volume (VAV) system. The system outputs under study were zone air temperature, relative humidity, carbon dioxide (CO2) concentration, and site electricity use. To evaluate the effects of uncertainty in HVAC systems with advanced sequences of operation, this work applies a Monte Carlo uncertainty analysis to a detailed Modelica building energy model that has been programmed with G36 control sequences. Uncertainty models were integrated with the deterministic models of the building and the control sequence at small time scales to represent frequencies in which a real-world building automation system (BAS) samples its signals. The impact of uncertainty was quantified using annual simulations. Specification of the accuracy levels in the components of the control system were evaluated by the means of: 1) uncertainty analysis for low, medium, and high severities of accuracy in the components to identify relation between performance requirements and component accuracy, and 2) sensitivity analysis to identify the sensors and actuators where the impact of uncertainty on the system outputs is most influential.

   @article{ABDELHALEEM2020110308,
     selected = {false},
     abbr = {ENB},
     bibtex_show = {true},
     title = {Performance of advanced control sequences in handling uncertainty in energy use and indoor environmental quality using uncertainty and sensitivity analysis for control components},
     journal = {Energy and Buildings},
     volume = {225},
     pages = {110308},
     year = {2020},
     issn = {0378-7788},
     doi = {https://doi.org/10.1016/j.enbuild.2020.110308},
     url = {https://www.sciencedirect.com/science/article/pii/S0378778820306964},
     html = {https://doi.org/10.1016/j.enbuild.2020.110308},
     author = {Abdel Haleem, Shadi M. and Pavlak, Gregory S. and Bahnfleth, William P.},
     keywords = {ASHRAE guideline 36, Control sequences, Modelica buildings library, Monte Carlo, Multiple zone VAV, Sensitivity analysis, Uncertainty analysis}
   }

6. ACC

   Two-stage stochastic planning for control of building thermal energy storage portfolios with transactive controls

   Yu, Min Gyung, and Pavlak, Gregory S.

   In 2020 American Control Conference (ACC) Jul 2020

   Abs Bib HTML

   Building thermal energy storage (TES) can provide value to building owners while helping the electric grid. In this paper, a transactive approach to controlling thermal energy storage is developed for multiple buildings considering electric grid incentives. A two-stage building control framework is proposed to plan day-ahead electricity procurement and real-time TES operation. Day-ahead planning is decided by a two-stage stochastic optimization framework to account for uncertainty in the occupant behavior and weather of the following day. In the real-time operation, a transactive market mechanism is utilized for load flexibility created by TES operation. Real-time operations are based on solving a model predictive control (MPC) problem at the aggregator level to dispatch thermal storage via transactive markets. Simulation case studies were conducted to evaluate the proposed framework by comparing the performance of the stochastic planning and control with the deterministic approach. This paper demonstrates the effectiveness of the developed framework in operating a portfolio of thermal storage resources in consideration of uncertainty.

   @inproceedings{YU9147670,
     selected = {false},
     abbr = {ACC},
     bibtex_show = {true},
     author = {Yu, Min Gyung and Pavlak, Gregory S.},
     booktitle = {2020 American Control Conference (ACC)},
     title = {Two-stage stochastic planning for control of building thermal energy storage portfolios with transactive controls},
     year = {2020},
     pages = {2339-2344},
     doi = {10.23919/ACC45564.2020.9147670},
     url = {https://ieeexplore.ieee.org/abstract/document/9147670},
     html = {https://doi.org/10.23919/ACC45564.2020.9147670},
     issn = {2378-5861},
     month = jul
   }

7. IA2020

   A review on current passive house performance and potentials in different climate zones

   Li, Xinyi, Pavlak, Gregory, and Rim, Donghyun

   In 16th Conference of the International Society of Indoor Air Quality and Climate 2020

   Abs Bib

   Buildings and construction account for 36% of global final energy use. As a solution, the Passive House (PH) standard, first developed by the Passive House Institute (PHI), Germany, prescribed three annual target criteria to reduce building energy consumption. Buildings built according to the PH standard have demonstrated up to an 80% reduction in annual space heating energy consumption and a 50% reduction in annual primary energy consumption (Schnieders & Hermelink, 2006). Since then, adopting these passive design strategies, projects around the world have produced results that provide researchers with valuable insights of the various aspects of performance of a passive house, including energy and thermal comfort. Given this background, the objective of this study is to examine performance of Passive Houses, both certified and uncertified, and identify some future trends of development of the PH standard, especially regarding the necessary adjustments of the standard for different climate zones.

   @inproceedings{ca04d4926a22410c9230c46f814d2f88,
     selected = {false},
     abbr = {IA2020},
     bibtex_show = {true},
     title = {A review on current passive house performance and potentials in different climate zones},
     author = {Li, Xinyi and Pavlak, Gregory and Rim, Donghyun},
     year = {2020},
     publisher = {International Society of Indoor Air Quality and Climate},
     booktitle = {16th Conference of the International Society of Indoor Air Quality and Climate}
   }

8. Energ.

   Development and evaluation of occupancy-aware HVAC control for residential building energy efficiency and occupant comfort

   Turley, Christina, Jacoby, Margarite, Pavlak, Gregory, and Henze, Gregor

   Energies 2020

   Abs Bib PDF

   Occupancy-aware heating, ventilation, and air conditioning (HVAC) control offers the opportunity to reduce energy use without sacrificing thermal comfort. Residential HVAC systems often use manually-adjusted or constant setpoint temperatures, which heat and cool the house regardless of whether it is needed. By incorporating occupancy-awareness into HVAC control, heating and cooling can be used for only those time periods it is needed. Yet, bringing this technology to fruition is dependent on accurately predicting occupancy. Non-probabilistic prediction models offer an opportunity to use collected occupancy data to predict future occupancy profiles. Smart devices, such as a connected thermostat, which already include occupancy sensors, can be used to provide a continually growing collection of data that can then be harnessed for short-term occupancy prediction by compiling and creating a binary occupancy prediction. Real occupancy data from six homes located in Colorado is analyzed and investigated using this occupancy prediction model. Results show that non-probabilistic occupancy models in combination with occupancy sensors can be combined to provide a hybrid HVAC control with savings on average of 5.0% and without degradation of thermal comfort. Model predictive control provides further opportunities, with the ability to adjust the relative importance between thermal comfort and energy savings to achieve savings between 1% and 13.3% depending on the relative weighting between thermal comfort and energy savings. In all cases, occupancy prediction allows the opportunity for a more intelligent and optimized strategy to residential HVAC control.

   @article{en13205396,
     selected = {false},
     abbr = {Energ.},
     bibtex_show = {true},
     author = {Turley, Christina and Jacoby, Margarite and Pavlak, Gregory and Henze, Gregor},
     title = {Development and evaluation of occupancy-aware HVAC control for residential building energy efficiency and occupant comfort},
     journal = {Energies},
     volume = {13},
     year = {2020},
     number = {20},
     article-number = {5396},
     url = {https://www.mdpi.com/1996-1073/13/20/5396},
     issn = {1996-1073},
     doi = {10.3390/en13205396},
     pdf = {https://doi.org/10.3390/en13205396}
   }

2019

1. APEN

   Uncertainty analysis of energy and economic performances of hybrid solar photovoltaic and combined cooling, heating, and power (CCHP+PV) systems using a Monte-Carlo method

   Ahn, Hyeunguk, Rim, Donghyun, Pavlak, Gregory S., and Freihaut, James D.

   Applied Energy 2019

   Abs Bib HTML

   This study examines the impacts of uncertainties in energy demands and solar resources on the energy and economic performances of hybrid solar photovoltaic and combined cooling, heating and power (CCHP+PV) systems with variations in PV penetration levels. This study investigates two models: a deterministic and stochastic model. The deterministic model uses hourly demands of the U.S. Department of Energy (DOE) reference large office building in San Francisco, CA and solar irradiance profiles in the Typical Meteorological Year (TMY) data as the independent variables. The stochastic model accounts for uncertainties in these independent variables using a Monte-Carlo method. The results show that regardless of PV penetration levels, the uncertainties in building energy demands and solar irradiance marginally influence the energy performance of CCHP+PV systems; however, they can notably increase annual operating costs up to 75,000 per year (13%). The annual cost increase is mainly attributed to a significant increase in demand charges (up to 79,000 per year). The demand charges tend to increase with higher uncertainties in the peak demand. The results suggest that in cases of the demand charge being responsible for a large portion in electricity bills (i.e., demand tariffs), a deterministic model tends to underestimate operating costs of CCHP+PV systems or other analogous distributed energy systems compared to a stochastic model. The errors with the deterministic model can become more extreme when demand charges outweigh energy charges.

   @article{AHN2019113753,
     title = {Uncertainty analysis of energy and economic performances of hybrid solar photovoltaic and combined cooling, heating, and power (CCHP+PV) systems using a Monte-Carlo method},
     abbr = {APEN},
     bibtex_show = {true},
     journal = {Applied Energy},
     volume = {255},
     pages = {113753},
     year = {2019},
     issn = {0306-2619},
     doi = {10.1016/j.apenergy.2019.113753},
     url = {https://www.sciencedirect.com/science/article/pii/S0306261919314400},
     html = {https://doi.org/10.1016/j.apenergy.2019.113753},
     author = {Ahn, Hyeunguk and Rim, Donghyun and Pavlak, Gregory S. and Freihaut, James D.},
     keywords = {Combined heat and power, U.S. DOE reference building, Internal combustion engine, Demand charge, Tariff structure, Energy demand}
   }

2. TPEC

   A novel application of modular multi-level converters for partially shaded PV systems

   Khazaei, Javad, Pavlak, Gregory S., Lee, Brandon, and Elsenbaty, Mohamed

   In 2019 IEEE Texas Power and Energy Conference (TPEC) 2019

   Abs Bib HTML

   This paper proposes a novel application of a modular multi-level converter (MMC) for photovoltaic (PV) generation system to maximize the efficiency in partial shading conditions. A DC/DC boost converter is placed between each sub-module (SM) of the MMC and the PV array to extract the maximum PV power. Furthermore, the proposed topology is enhanced with an independent active/reactive power control strategy for demand response purposes. The effect of circulating currents is also minimized using a proportional resonance (PR) controller. The proposed system is validated using time-domain simulations via MATLAB/Simscape power system toolbox on a 10 MW three-phase grid-connected MMC system.

   @inproceedings{8662179,
     author = {Khazaei, Javad and Pavlak, Gregory S. and Lee, Brandon and Elsenbaty, Mohamed},
     abbr = {TPEC},
     bibtex_show = {true},
     booktitle = {2019 IEEE Texas Power and Energy Conference (TPEC)},
     title = {A novel application of modular multi-level converters for partially shaded PV systems},
     year = {2019},
     pages = {1-6},
     doi = {10.1109/TPEC.2019.8662179},
     html = {https://doi.org/10.1109/TPEC.2019.8662179}
   }

2016

1. AUTCON

   Experimental verification of an energy consumption signal tool for operational decision support in an office building

   Pavlak, Gregory S., Henze, Gregor P., Hirsch, Adam I., Florita, Anthony R., and Dodier, Robert H.

   Automation in Construction 2016

   Abs Bib HTML

   This paper demonstrates an energy signal tool to assess the system-level and whole-building energy use of an office building in downtown Denver, Colorado. The energy signal tool uses a traffic light visualization to alert a building operator to energy use which is substantially different from expected. The tool selects which light to display for a given energy end-use by comparing measured energy use to expected energy use, accounting for uncertainty. A red light is only displayed when a fault is likely enough, and abnormal operation costly enough, that taking action will yield the lowest cost result. While the theoretical advances and tool development were reported previously, it has only been tested using a basic building model and has not, until now, been experimentally verified. Expected energy use for the field demonstration is provided by a compact reduced-order representation of the Alliance Center, generated from a detailed DOE-2.2 energy model. Actual building energy consumption data is taken from the summer of 2014 for the office building immediately after a significant renovation project. The purpose of this paper is to demonstrate a first look at the building following its major renovation compared to the design intent. The tool indicated strong under-consumption in lighting and plug loads and strong over-consumption in HVAC energy consumption, which prompted several focused actions for follow-up investigation. In addition, this paper illustrates the application of Bayesian inference to the estimation of posterior parameter probability distributions to measured data. Practical discussion of the application is provided, along with additional findings from further investigating the significant difference between expected and actual energy consumption.

   @article{PAVLAK201675,
     title = {Experimental verification of an energy consumption signal tool for operational decision support in an office building},
     abbr = {AUTCON},
     bibtex_show = {true},
     journal = {Automation in Construction},
     volume = {72},
     pages = {75-92},
     year = {2016},
     issn = {0926-5805},
     doi = {10.1016/j.autcon.2016.08.034},
     url = {https://www.sciencedirect.com/science/article/pii/S0926580516301893},
     html = {https://doi.org/10.1016/j.autcon.2016.08.034},
     author = {Pavlak, Gregory S. and Henze, Gregor P. and Hirsch, Adam I. and Florita, Anthony R. and Dodier, Robert H.},
     keywords = {Decision support, Operational building energy modeling, Bayesian updating}
   }

2015

1. Energy

   Evaluating synergistic effect of optimally controlling commercial building thermal mass portfolios

   Pavlak, Gregory S., Henze, Gregor P., and Cushing, Vincent J.

   Energy 2015

   Abs Bib HTML

   In order to achieve a sustainable energy future, advanced control paradigms will be critical at both building and grid levels to achieve harmonious integration of energy resources. This research explores the potential for synergistic effects that may exist through communal coordination of commercial building operations. A framework is presented for diurnal planning of multi-building thermal mass and HVAC system operational strategies in consideration of real-time energy prices, peak demand charges, and ancillary service revenues. Optimizing buildings as a portfolio achieved up to seven additional percentage points of cost savings over individually optimized cases, depending on the simulation case study. The magnitude and nature of synergistic effect was ultimately dependent upon the portfolio construction, grid market design, and the conditions faced by buildings when optimized individually. Enhanced energy and cost savings opportunities were observed by taking the novel perspective of optimizing building portfolios in multiple grid markets, motivating the pursuit of future smart grid advancements that take a holistic and communal vantage point.

   @article{PAVLAK2015161,
     title = {Evaluating synergistic effect of optimally controlling commercial building thermal mass portfolios},
     abbr = {Energy},
     bibtex_show = {true},
     journal = {Energy},
     volume = {84},
     pages = {161-176},
     year = {2015},
     issn = {0360-5442},
     doi = {https://doi.org/10.1016/j.energy.2015.02.073},
     html = {https://doi.org/10.1016/j.energy.2015.02.073},
     url = {https://www.sciencedirect.com/science/article/pii/S0360544215002285},
     author = {Pavlak, Gregory S. and Henze, Gregor P. and Cushing, Vincent J.},
     keywords = {Buildings to grid, Portfolio optimization, Demand response, Frequency regulation, Model predictive control, Smart buildings}
   }

2. APEN

   An energy signal tool for decision support in building energy systems

   Henze, Gregor P., Pavlak, Gregory S., Florita, Anthony R., Dodier, Robert H., and Hirsch, Adam I.

   Applied Energy 2015

   Abs Bib HTML

   A prototype energy signal tool is demonstrated for operational whole-building and system-level energy use evaluation. The purpose of the tool is to give a summary of building energy use which allows a building operator to quickly distinguish normal and abnormal energy use. Toward that end, energy use status is displayed as a traffic light, which is a visual metaphor for energy use which is substantially different from expected (red and yellow lights) or more or less the same as expected (green light). Which light to display for a given energy end-use is determined by comparing expected energy use to actual energy use. As expected energy use is necessarily uncertain, we cannot choose the appropriate light with certainty. Instead the energy signal tool chooses the light by minimizing the expected cost of displaying the wrong light. The expected energy use is represented by a probability distribution. Energy use is modeled by a low-order lumped parameter model. Uncertainty in energy use is quantified by a Monte Carlo exploration of the influence of model parameters on energy use. Distributions over model parameters are updated over time via Bayes’€™ theorem. The simulation study is devised to assess whole building energy signal accuracy in the presence of uncertainty and faults at the submetered level, which may lead to tradeoffs at the whole building level not detectable without submetering.

   @article{HENZE201551,
     title = {An energy signal tool for decision support in building energy systems},
     abbr = {APEN},
     bibtex_show = {true},
     journal = {Applied Energy},
     volume = {138},
     pages = {51-70},
     year = {2015},
     issn = {0306-2619},
     doi = {10.1016/j.apenergy.2014.10.029},
     html = {https://doi.org/10.1016/j.apenergy.2014.10.029},
     url = {https://www.sciencedirect.com/science/article/pii/S0306261914010708},
     author = {Henze, Gregor P. and Pavlak, Gregory S. and Florita, Anthony R. and Dodier, Robert H. and Hirsch, Adam I.},
     keywords = {Decision support, Operational building energy modeling, Bayesian updating}
   }

2014

1. ENB

   Optimizing commercial building participation in energy and ancillary service markets

   Pavlak, Gregory S., Henze, Gregor P., and Cushing, Vincent J.

   Energy and Buildings 2014

   Abs Bib HTML

   Providing ancillary services through flexible load response has the potential to increase electric grid reliability and efficiency while offering loads a revenue generating opportunity. The large power draw of commercial buildings, along with thermal mass characteristics, has sparked interest in providing ancillary services through intelligent operation of building mechanical systems. As a precursor to participating in ancillary service markets, the quantity of service available must be estimated. This work presents a model-based approach for estimating commercial building frequency regulation capability. A model predictive control framework is proposed to determine optimal operating strategies in consideration of energy use, energy expense, peak demand, economic demand response revenue, and frequency regulation revenue. The methodology is demonstrated through simulation for medium office and large office building applications, highlighting its ability to merge revenue generating opportunities with traditional demand and cost reducing objectives.

   @article{PAVLAK2014115,
     selected = {true},
     abbr = {ENB},
     bibtex_show = {true},
     title = {Optimizing commercial building participation in energy and ancillary service markets},
     journal = {Energy and Buildings},
     volume = {81},
     pages = {115-126},
     year = {2014},
     issn = {0378-7788},
     doi = {10.1016/j.enbuild.2014.05.048},
     url = {https://www.sciencedirect.com/science/article/pii/S0378778814004721},
     html = {https://doi.org/10.1016/j.enbuild.2014.05.048},
     author = {Pavlak, Gregory S. and Henze, Gregor P. and Cushing, Vincent J.}
   }

2. JAE

   Comparison of traditional and bayesian calibration techniques for gray-box modeling

   Pavlak, Gregory S., Florita, Anthony R., Henze, Gregor P., and Rajagopalan, Balaji

   Journal of Architectural Engineering Jun 2014

   Abs Bib HTML

   Bayesian and nonlinear least-squares methods of calibration were evaluated and compared for gray-box modeling of a retail building. Gray-box model calibration is one form of system identification and is examined here with perturbations to the simple yet popular European Committee for Standardization (CEN)-ISO thermal network model. The primary objective was to understand whether the computational expense of probabilistic Bayesian techniques is required to provide robustness to signal noise, specifically with regard to lower dimensional problems (physical or semiphysical), where model calibration is preferred over uncertainty quantification. The Bayesian approach allows parameter interactions and trade-offs to be revealed, one form of sensitivity analysis, but its full power for uncertainty quantification cannot be harnessed with gray-box or other simplified models. Surrogate data from a detailed building energy simulation program were used to ensure command over latent variables, whereas a range of signal-to-noise and noise colors were considered in the experimental study. The fidelity to the building zone temperature and thermal load was the basis for comparing results. Utilization of uniform priors showed that both methods performed similarly well. Bayesian calibration outperformed traditional methods on noisy data sets; however, traditional methods were adequate up to an approximately 25% noise level. The thermal gray-box model calibration has the intended application of embedded model predictive control, where speed, accuracy, and robustness are crucial. Traditional methods required approximately 100 times less CPU time and are recommended given the model simplicity, application, and expected system noise levels.

   @article{Pavlak2013,
     author = {Pavlak, Gregory S. and Florita, Anthony R. and Henze, Gregor P. and Rajagopalan, Balaji},
     doi = {10.1061/(ASCE)AE.1943-5568.0000145},
     issn = {1076-0431},
     abbr = {JAE},
     bibtex_show = {true},
     journal = {Journal of Architectural Engineering},
     month = jun,
     number = {2},
     pages = {04013011},
     title = {Comparison of traditional and bayesian calibration techniques for gray-box modeling},
     url = {http://ascelibrary.org/doi/abs/10.1061/{\%}28ASCE{\%}29AE.1943-5568.0000145},
     html = {https://doi.org/10.1061/(ASCE)AE.1943-5568.0000145},
     volume = {20},
     year = {2014}
   }

2013

1. AEI

   Probabilistic identification of inverse building model parameters

   Pavlak, Gregory S., Florita, Anthony R., Henze, Gregor P., and Rajagopalan, Balaji

   In AEI Conference 2013

   Abs Bib HTML

   Probabilistic and nonlinear least squares parameter estimation methods are evaluated for inverse gray box model identification of a retail building. A detailed building energy simulation program is used to generate surrogate data for estimation of parameters. The most probable or optimal parameters from each method are compared through simulation of building zone temperature and thermal loads. The least squares method generally found solutions near probable regions of the posterior from the probabilistic approach, and simulation performance was very similar between best parameter sets. A brief overview of probabilistic estimation techniques is provided, along with potential improvements to the approach presented and brief discussion on its applicability for uncertainty quantification within the building science domain.

   @inproceedings{doi:10.1061/9780784412909.025,
     author = {Pavlak, Gregory S. and Florita, Anthony R. and Henze, Gregor P. and Rajagopalan, Balaji},
     title = {Probabilistic identification of inverse building model parameters},
     abbr = {AEI},
     bibtex_show = {true},
     booktitle = {AEI Conference},
     year = {2013},
     pages = {256-265},
     doi = {10.1061/9780784412909.025},
     url = {https://ascelibrary.org/doi/abs/10.1061/9780784412909.025},
     html = {https://doi.org/10.1061/9780784412909.025}
   }