Pub Date : 2022-08-04DOI: 10.1080/19401493.2022.2092652
S. Anbarasu, W. Zuo, Yangyang Fu, Yash Shukla, Rajan Rawal
Direct evaporative coolers (DECs) are a low-energy cooling alternative to conventional air conditioning in hot-dry climates. The key component of DEC is the cooling pad, which evaporatively cools the air passing through it. While detailed numerical models of heat and mass transfer have been proposed for the cooling pad, these require many input parameters that are not readily accessible. Alternatively, simplified models lack accuracy and are confined to common types of cooling pad. To address these limitations, we developed and validated a physics-based model, that only needs the nominal data to compute the heat and mass transfer with considerable accuracy. The proposed model is implemented in Modelica, an equation-based object-oriented modeling language. For comparison, a basic lumped model from EnergyPlus based on the efficiency curve of the cooling pad is also implemented. The physics-based model exhibits <2% error from the experimental data and the lumped model exhibits a 12.3% error.
{"title":"Validated open-source Modelica model of direct evaporative cooler with minimal inputs","authors":"S. Anbarasu, W. Zuo, Yangyang Fu, Yash Shukla, Rajan Rawal","doi":"10.1080/19401493.2022.2092652","DOIUrl":"https://doi.org/10.1080/19401493.2022.2092652","url":null,"abstract":"Direct evaporative coolers (DECs) are a low-energy cooling alternative to conventional air conditioning in hot-dry climates. The key component of DEC is the cooling pad, which evaporatively cools the air passing through it. While detailed numerical models of heat and mass transfer have been proposed for the cooling pad, these require many input parameters that are not readily accessible. Alternatively, simplified models lack accuracy and are confined to common types of cooling pad. To address these limitations, we developed and validated a physics-based model, that only needs the nominal data to compute the heat and mass transfer with considerable accuracy. The proposed model is implemented in Modelica, an equation-based object-oriented modeling language. For comparison, a basic lumped model from EnergyPlus based on the efficiency curve of the cooling pad is also implemented. The physics-based model exhibits <2% error from the experimental data and the lumped model exhibits a 12.3% error.","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"16 1","pages":"757 - 770"},"PeriodicalIF":2.5,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91287604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-27DOI: 10.1080/19401493.2022.2097311
Hugo Geoffroy, J. Berger, E. Gonze, C. Buhé
Fault Detection and Diagnosis (FDD) is an important tool in building commissioning. Providing a consolidated dataset for FDD benchmarking is necessary to accurately evaluate the FDD prediction accuracy and detect anomalies. In this study, we provide an experimental dataset for an air handling unit containing two ducts linked by an air-to-air heat exchanger. The dataset is composed of nominal and faulty operations of the system, including the ground truth in order to investigate various faults in 52 cases. The dataset was obtained by measuring a representative system with real climate variations like the ones obtained by Building Automation Systems. The transition between nominal and fault sequences was continuous, as in real operating conditions. An uncertainty evaluation was carried out to provide confidence bounds in the experimental dataset.
{"title":"Experimental dataset for an AHU air-to-air heat exchanger with normal and simulated fault operations","authors":"Hugo Geoffroy, J. Berger, E. Gonze, C. Buhé","doi":"10.1080/19401493.2022.2097311","DOIUrl":"https://doi.org/10.1080/19401493.2022.2097311","url":null,"abstract":"Fault Detection and Diagnosis (FDD) is an important tool in building commissioning. Providing a consolidated dataset for FDD benchmarking is necessary to accurately evaluate the FDD prediction accuracy and detect anomalies. In this study, we provide an experimental dataset for an air handling unit containing two ducts linked by an air-to-air heat exchanger. The dataset is composed of nominal and faulty operations of the system, including the ground truth in order to investigate various faults in 52 cases. The dataset was obtained by measuring a representative system with real climate variations like the ones obtained by Building Automation Systems. The transition between nominal and fault sequences was continuous, as in real operating conditions. An uncertainty evaluation was carried out to provide confidence bounds in the experimental dataset.","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"1 1","pages":"268 - 290"},"PeriodicalIF":2.5,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88906663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-15DOI: 10.1080/19401493.2022.2080864
Hugo Geoffroy, J. Berger, Benoît Colange, S. Lespinats, D. Dutykh
Fault detection and diagnosis (FDD) are important tools to perform on-going monitoring of the systems and help in their building commissioning. An innovative method is investigated based on combined data-driven and knowledge-based approaches. This article presents the method. In the first phase, a so-called operating map of the system is built using a dimension reduction method and numerical or experimental dataset. This map is composed of several regions corresponding to nominal operation and to specific faults. The second phase focuses on the FDD. The monitored data are projected on the map. According to the position, a clear and precise FDD can be carried. The method is applied to an air handling unit. The map is built using data generated with a building simulation programme. The reliability of the method is proven using experimental data of nominal and fault operation generated.
{"title":"The use of dimensionality reduction techniques for fault detection and diagnosis in a AHU unit: critical assessment of its reliability","authors":"Hugo Geoffroy, J. Berger, Benoît Colange, S. Lespinats, D. Dutykh","doi":"10.1080/19401493.2022.2080864","DOIUrl":"https://doi.org/10.1080/19401493.2022.2080864","url":null,"abstract":"Fault detection and diagnosis (FDD) are important tools to perform on-going monitoring of the systems and help in their building commissioning. An innovative method is investigated based on combined data-driven and knowledge-based approaches. This article presents the method. In the first phase, a so-called operating map of the system is built using a dimension reduction method and numerical or experimental dataset. This map is composed of several regions corresponding to nominal operation and to specific faults. The second phase focuses on the FDD. The monitored data are projected on the map. According to the position, a clear and precise FDD can be carried. The method is applied to an air handling unit. The map is built using data generated with a building simulation programme. The reliability of the method is proven using experimental data of nominal and fault operation generated.","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"73 1","pages":"249 - 267"},"PeriodicalIF":2.5,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79602159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-18DOI: 10.1080/19401493.2022.2079827
J. Candanedo, A. Athienitis
The ever-increasing availability of data in buildings has sparked a profound transformation across the board in all areas of human activity, in fields as diverse as engineering, entertainment, marketing and medicine. Building performance simulation and building operation are no exception: slowly but steadily, datasets frombuildings are being used for load forecasting, fault detection and diagnosis, the identification of opportunities for energy savings and peak load reduction, optimizing interaction with smart grids and a better understanding of occupant behaviour. International ongoing efforts, such as the work of the IEA EBC Annex 81 ‘Data-Driven Smart Buildings’ efforts, focus on how to better use data to gain insight on building operation and improve their overall performance. While important hurdles have been identified, most notably the need to standardize data labelling and structure in building automation systems, numerous technological advances such as machine learning, in addition to the need to decarbonize the building sector will drive the adoption of data-driven tools over the next decades. In the field of building simulation, the value of data is immense. While building performance simulation rests upon well-understood and rigorous physical principles, thenumerous intervening variables and their interactions make it difficult to assess to what extent the aggregate of these models yields a clear picture of themajor energy flows in abuildingandof its interactionwith thegrid.Data accessibility and treatment will provide an increasingly solid ground for a new paradigm of ‘evidence-based’ building performance simulation, particularly in aspects related to short-term dynamics and building operation. ‘Big Data’, either from a single building or from many buildings, will bridge the gap between the understanding of building physics and mechanical systems, and the educated guesses required in the assumptions made to develop a model. The impact of data is twofold: (a) it will facilitate the task of creating reliable predictive building models with generalization capabilities; (b) it will streamline the implementation of advanced control in a large diversity of building configurations and climatic conditions, with increasingly integrated renewable energy sources such as building-integrated photovoltaics, as well as energy storage systems.
{"title":"Leveraging data: a new frontier in building modelling and advanced control","authors":"J. Candanedo, A. Athienitis","doi":"10.1080/19401493.2022.2079827","DOIUrl":"https://doi.org/10.1080/19401493.2022.2079827","url":null,"abstract":"The ever-increasing availability of data in buildings has sparked a profound transformation across the board in all areas of human activity, in fields as diverse as engineering, entertainment, marketing and medicine. Building performance simulation and building operation are no exception: slowly but steadily, datasets frombuildings are being used for load forecasting, fault detection and diagnosis, the identification of opportunities for energy savings and peak load reduction, optimizing interaction with smart grids and a better understanding of occupant behaviour. International ongoing efforts, such as the work of the IEA EBC Annex 81 ‘Data-Driven Smart Buildings’ efforts, focus on how to better use data to gain insight on building operation and improve their overall performance. While important hurdles have been identified, most notably the need to standardize data labelling and structure in building automation systems, numerous technological advances such as machine learning, in addition to the need to decarbonize the building sector will drive the adoption of data-driven tools over the next decades. In the field of building simulation, the value of data is immense. While building performance simulation rests upon well-understood and rigorous physical principles, thenumerous intervening variables and their interactions make it difficult to assess to what extent the aggregate of these models yields a clear picture of themajor energy flows in abuildingandof its interactionwith thegrid.Data accessibility and treatment will provide an increasingly solid ground for a new paradigm of ‘evidence-based’ building performance simulation, particularly in aspects related to short-term dynamics and building operation. ‘Big Data’, either from a single building or from many buildings, will bridge the gap between the understanding of building physics and mechanical systems, and the educated guesses required in the assumptions made to develop a model. The impact of data is twofold: (a) it will facilitate the task of creating reliable predictive building models with generalization capabilities; (b) it will streamline the implementation of advanced control in a large diversity of building configurations and climatic conditions, with increasingly integrated renewable energy sources such as building-integrated photovoltaics, as well as energy storage systems.","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"22 1","pages":"431 - 432"},"PeriodicalIF":2.5,"publicationDate":"2022-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87306154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-30DOI: 10.1080/19401493.2022.2080865
Muhammad Zeeshan, Zaib Ali, Muhammad Sajid, Majid Ali, Muhammad Usman
{"title":"Modelling the cooling effectiveness of street trees with actual canopy drag and real transpiration rate under representative climatic conditions","authors":"Muhammad Zeeshan, Zaib Ali, Muhammad Sajid, Majid Ali, Muhammad Usman","doi":"10.1080/19401493.2022.2080865","DOIUrl":"https://doi.org/10.1080/19401493.2022.2080865","url":null,"abstract":"","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"8 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85170278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-25DOI: 10.1080/19401493.2022.2063947
J. Candanedo, Charalampos Vallianos, B. Delcroix, J. Date, Ali Saberi Derakhtenjani, N. Morovat, C. John, A. Athienitis
While the potential of model-based control is recognized, the development of reasonably accurate models for control applications remains a challenging, cumbersome and time-consuming task. This paper proposes a systematic and generalizable approach – based on low-order control-oriented thermal network (RC) archetypes – for the development, testing and implementation of readily scalable control solutions for buildings. These archetypes, focusing specifically on control applications, can significantly facilitate assessing the effect of control strategies on energy efficiency and load management. Furthermore, this approach can also be used for characterization, design and testing of simple retrofit strategies. The utilization of RC-based archetypes for common types of zones (such as those heated/cooled with forced-air or radiant systems) is proposed. These simple models (often 1st to 4th order models suffice), can also be used for the control of residential buildings. For larger buildings, zonal models can be combined to form whole building models.
{"title":"Control-oriented archetypes: a pathway for the systematic application of advanced controls in buildings","authors":"J. Candanedo, Charalampos Vallianos, B. Delcroix, J. Date, Ali Saberi Derakhtenjani, N. Morovat, C. John, A. Athienitis","doi":"10.1080/19401493.2022.2063947","DOIUrl":"https://doi.org/10.1080/19401493.2022.2063947","url":null,"abstract":"While the potential of model-based control is recognized, the development of reasonably accurate models for control applications remains a challenging, cumbersome and time-consuming task. This paper proposes a systematic and generalizable approach – based on low-order control-oriented thermal network (RC) archetypes – for the development, testing and implementation of readily scalable control solutions for buildings. These archetypes, focusing specifically on control applications, can significantly facilitate assessing the effect of control strategies on energy efficiency and load management. Furthermore, this approach can also be used for characterization, design and testing of simple retrofit strategies. The utilization of RC-based archetypes for common types of zones (such as those heated/cooled with forced-air or radiant systems) is proposed. These simple models (often 1st to 4th order models suffice), can also be used for the control of residential buildings. For larger buildings, zonal models can be combined to form whole building models.","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"48 1","pages":"433 - 444"},"PeriodicalIF":2.5,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80958178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-06DOI: 10.1080/19401493.2022.2058089
Mina Pouyanmehr, P. Pilechiha, U. Berardi, P. Carnemolla
Providing sufficient daylight and view access to the outdoors is crucial to creating a productive work environment and ensuring employees’ wellbeing and mental health in offices. To these aims, determining an optimum shading form can be challenging for designers. This study applied an ‘external shading form-finding’ and a novel ‘dynamic view access assessment’ method to find the optimum shading devices from 723 shading systems. Each system contains a typical louvre blade with two equidistant shading devices. These were externally fixed in front of a south-facing window with a dynamic interior blind, and were tested across three window-to-wall ratios. Optimum forms were selected according to LEED v4 daylight needs and unobstructed views. The results indicate that these proposed methods have the potential to support decision-making related to shading design, helping designers and architects to study the view quantitatively and combine its results with daylight assessment leading to improved building performance, employee mental health and wellbeing.
{"title":"External shading form-finding: simulating daylighting and dynamic view access assessment","authors":"Mina Pouyanmehr, P. Pilechiha, U. Berardi, P. Carnemolla","doi":"10.1080/19401493.2022.2058089","DOIUrl":"https://doi.org/10.1080/19401493.2022.2058089","url":null,"abstract":"Providing sufficient daylight and view access to the outdoors is crucial to creating a productive work environment and ensuring employees’ wellbeing and mental health in offices. To these aims, determining an optimum shading form can be challenging for designers. This study applied an ‘external shading form-finding’ and a novel ‘dynamic view access assessment’ method to find the optimum shading devices from 723 shading systems. Each system contains a typical louvre blade with two equidistant shading devices. These were externally fixed in front of a south-facing window with a dynamic interior blind, and were tested across three window-to-wall ratios. Optimum forms were selected according to LEED v4 daylight needs and unobstructed views. The results indicate that these proposed methods have the potential to support decision-making related to shading design, helping designers and architects to study the view quantitatively and combine its results with daylight assessment leading to improved building performance, employee mental health and wellbeing.","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"4 1","pages":"398 - 409"},"PeriodicalIF":2.5,"publicationDate":"2022-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74337572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-06DOI: 10.1080/19401493.2022.2058088
J. Formhals, B. Welsch, H. Hemmatabady, D. Schulte, L. Seib, I. Sass
Integration of borehole heat exchangers (BHE) into district heating (DH) should be supported by numerical simulations to ensure efficient operation. Co-simulation allows for the use of dedicated software for above and below ground sub-models, facilitating the use of detailed 3D geological models. This paper presents a methodology for coupling DH models in Modelica to 3D FEM subsurface models. An interface which implements BHE models in Modelica and one with BHE models in the FEM model are compared to a benchmark model. Furthermore, an adaptive control of the communication steps reduces communication error and computational times simultaneously. A fictional solar DH system with underground thermal energy storage is co-simulated to demonstrate potential advantages of the proposed method. Overall, co-simulation of DH systems and BHE arrays facilitates accurate performance assessment of systems for which this would not be possible otherwise, but should be applied carefully, due to the increased computational effort.
{"title":"Co-simulation of district heating systems and borehole heat exchanger arrays using 3D finite element method subsurface models","authors":"J. Formhals, B. Welsch, H. Hemmatabady, D. Schulte, L. Seib, I. Sass","doi":"10.1080/19401493.2022.2058088","DOIUrl":"https://doi.org/10.1080/19401493.2022.2058088","url":null,"abstract":"Integration of borehole heat exchangers (BHE) into district heating (DH) should be supported by numerical simulations to ensure efficient operation. Co-simulation allows for the use of dedicated software for above and below ground sub-models, facilitating the use of detailed 3D geological models. This paper presents a methodology for coupling DH models in Modelica to 3D FEM subsurface models. An interface which implements BHE models in Modelica and one with BHE models in the FEM model are compared to a benchmark model. Furthermore, an adaptive control of the communication steps reduces communication error and computational times simultaneously. A fictional solar DH system with underground thermal energy storage is co-simulated to demonstrate potential advantages of the proposed method. Overall, co-simulation of DH systems and BHE arrays facilitates accurate performance assessment of systems for which this would not be possible otherwise, but should be applied carefully, due to the increased computational effort.","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"44 3 1","pages":"362 - 378"},"PeriodicalIF":2.5,"publicationDate":"2022-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78026065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-06DOI: 10.1080/19401493.2022.2058087
Anke Uytterhoeven, Robbe Van Rompaey, K. Bruninx, L. Helsen
This paper presents a chance constrained stochastic model predictive control (SMPC) approach for building climate control under combined parametric and additive uncertainties. The proposed SMPCap approach enables the quantification, and manipulation, of both the mean and covariance of the stochastic system states and inputs. Its enhanced uncertainty anticipation is shown to induce improved thermal comfort in closed-loop simulations compared to the conventional deterministic MPC (DMPC) and the state-of-the-art SMPCa only accounting for additive uncertainties, at the cost of a maximum relative increase in energy use of 21.6% and 4.2%, respectively. By incorporating the SMPCap strategy in an integrated optimal control and design (IOCD) approach, its additional added value for obtaining a more appropriate, yet robust, heat supply system sizing is illustrated. Via simulations, size reductions up to 33.3% are shown to be achievable for a terraced single-family dwelling without increasing thermal discomfort compared to an IOCD approach incorporating DMPC.
{"title":"Chance constrained stochastic MPC for building climate control under combined parametric and additive uncertainty","authors":"Anke Uytterhoeven, Robbe Van Rompaey, K. Bruninx, L. Helsen","doi":"10.1080/19401493.2022.2058087","DOIUrl":"https://doi.org/10.1080/19401493.2022.2058087","url":null,"abstract":"This paper presents a chance constrained stochastic model predictive control (SMPC) approach for building climate control under combined parametric and additive uncertainties. The proposed SMPCap approach enables the quantification, and manipulation, of both the mean and covariance of the stochastic system states and inputs. Its enhanced uncertainty anticipation is shown to induce improved thermal comfort in closed-loop simulations compared to the conventional deterministic MPC (DMPC) and the state-of-the-art SMPCa only accounting for additive uncertainties, at the cost of a maximum relative increase in energy use of 21.6% and 4.2%, respectively. By incorporating the SMPCap strategy in an integrated optimal control and design (IOCD) approach, its additional added value for obtaining a more appropriate, yet robust, heat supply system sizing is illustrated. Via simulations, size reductions up to 33.3% are shown to be achievable for a terraced single-family dwelling without increasing thermal discomfort compared to an IOCD approach incorporating DMPC.","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"83 1","pages":"410 - 430"},"PeriodicalIF":2.5,"publicationDate":"2022-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88985276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-06DOI: 10.1080/19401493.2022.2058091
Thibault Marzullo, Sourav Dey, N. Long, Jose Angel Leiva Vilaplana, G. Henze
We present an open-source building performance simulation test bed, the Advanced Controls Test Bed (ACTB), that interfaces high-fidelity Spawn of EnergyPlus building models, with advanced controllers implemented in Python. The ACTB leverages the Building Optimization Testing and Alfalfa platforms for managing simulations, providing an external clock, a representational state transfer (REST) application programming interface (API), and key performance indicators for evaluating the effectiveness of control strategies. The REST API allows the development of external controllers programmed in languages such as Python, which provides flexibility and a rich choice of scientific libraries for designing control sequences. We present three test cases based on the U.S. Department of Energy's Reference Small Office Building to demonstrate the ACTB's capabilities: (a) rule-based controls compliant with ASHRAE Guideline 36 control sequences; (b) an economic model predictive control implemented using do-mpc; and (c) a deep Q-network reinforcement learning agent implemented using OpenAI Gym. Abbreviations: ACTB: Advanced Controller Test Bed; AHU: Air Handling Unit; AI:Artificial Intelligence; API: Application Programming Interface; BEM: Building EnergyModeling; BSS: Best Subset Selection; DOE: Department of Energy; DQN: Deep-QNetwork; EKF: Extended Kalman Filter; FMI: Functional Mock-up Interface; FMU:Functional Mock-up Unit; FSS: Forward Stepwise Selection; HVAC: Heating; Ventilationand Air Conditioning; KPI: Key Performance Indicator; LTI: Linear Time-Invariant; MBL: Modelica Buildings Library; MHE: Moving Horizon Estimator; MPC: ModelPredictive Control; N4SID: Numerical Subspace State-Space System Identification; REST: Representational State Transfer; RL: Reinforcement Learning; ROM: Reducedorder model
我们提出了一个开源的建筑性能模拟测试台，高级控制测试台(ACTB)，它将EnergyPlus建筑模型的高保真衍生与Python实现的高级控制器相连接。ACTB利用Building Optimization Testing和Alfalfa平台来管理仿真，提供外部时钟、representational state transfer (REST)应用程序编程接口(API)，以及评估控制策略有效性的关键性能指标。REST API允许开发用Python等语言编程的外部控制器，这为设计控制序列提供了灵活性和丰富的科学库选择。我们提出了三个基于美国能源部参考小型办公大楼的测试案例，以展示ACTB的能力:(a)符合ASHRAE指南36控制序列的基于规则的控制;(b)使用do-mpc实现的经济模型预测控制;(c)使用OpenAI Gym实现的深度q网络强化学习代理。缩写:ACTB: Advanced Controller Test Bed;AHU:空气处理装置;人工智能:人工智能;API:应用程序编程接口;BEM:建筑能源建模;BSS:最佳子集选择;DOE:美国能源部;DQN: Deep-QNetwork;EKF:扩展卡尔曼滤波;FMI:功能模型界面;FMU:功能模型单元;FSS:正向逐步选择;空调:加热;通风及空调;KPI:关键绩效指标;LTI:线性时不变;MBL: Modelica建筑图书馆;MHE:移动地平线估计器;MPC:模型预测控制;数值子空间状态-空间系统辨识;REST:具象状态转移;RL:强化学习;ROM:约序模型
{"title":"A high-fidelity building performance simulation test bed for the development and evaluation of advanced controls","authors":"Thibault Marzullo, Sourav Dey, N. Long, Jose Angel Leiva Vilaplana, G. Henze","doi":"10.1080/19401493.2022.2058091","DOIUrl":"https://doi.org/10.1080/19401493.2022.2058091","url":null,"abstract":"We present an open-source building performance simulation test bed, the Advanced Controls Test Bed (ACTB), that interfaces high-fidelity Spawn of EnergyPlus building models, with advanced controllers implemented in Python. The ACTB leverages the Building Optimization Testing and Alfalfa platforms for managing simulations, providing an external clock, a representational state transfer (REST) application programming interface (API), and key performance indicators for evaluating the effectiveness of control strategies. The REST API allows the development of external controllers programmed in languages such as Python, which provides flexibility and a rich choice of scientific libraries for designing control sequences. We present three test cases based on the U.S. Department of Energy's Reference Small Office Building to demonstrate the ACTB's capabilities: (a) rule-based controls compliant with ASHRAE Guideline 36 control sequences; (b) an economic model predictive control implemented using do-mpc; and (c) a deep Q-network reinforcement learning agent implemented using OpenAI Gym. Abbreviations: ACTB: Advanced Controller Test Bed; AHU: Air Handling Unit; AI:Artificial Intelligence; API: Application Programming Interface; BEM: Building EnergyModeling; BSS: Best Subset Selection; DOE: Department of Energy; DQN: Deep-QNetwork; EKF: Extended Kalman Filter; FMI: Functional Mock-up Interface; FMU:Functional Mock-up Unit; FSS: Forward Stepwise Selection; HVAC: Heating; Ventilationand Air Conditioning; KPI: Key Performance Indicator; LTI: Linear Time-Invariant; MBL: Modelica Buildings Library; MHE: Moving Horizon Estimator; MPC: ModelPredictive Control; N4SID: Numerical Subspace State-Space System Identification; REST: Representational State Transfer; RL: Reinforcement Learning; ROM: Reducedorder model","PeriodicalId":49168,"journal":{"name":"Journal of Building Performance Simulation","volume":"50 1","pages":"379 - 397"},"PeriodicalIF":2.5,"publicationDate":"2022-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73232213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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