To meet the challenge of mismatches between power supply and demand,modern buildings must schedule flexible energy loads in order to improve the efficiency of power grids.Furthermore,it is essential to understand the ...To meet the challenge of mismatches between power supply and demand,modern buildings must schedule flexible energy loads in order to improve the efficiency of power grids.Furthermore,it is essential to understand the effectiveness of flexibility management strategies under different climate conditions and extreme weather events.Using both typical and extreme weather data from cities in five major climate zones of China,this study investigates the energy flexibility potential of an office building under three short-term HVAC management strategies in the context of different climates.The results show that the peak load flexibility and overall energy performance of the three short-term strategies were affected by the surrounding climate conditions.The peak load reduction rate of the pre-cooling and zone temperature reset strategies declined linearly as outdoor temperature increased.Under extreme climate conditions,the daily peak-load time was found to be over two hours earlier than under typical conditions,and the intensive solar radiation found in the extreme conditions can weaken the correlation between peak load reduction and outdoor temperature,risking the ability of a building’s HVAC system to maintain a comfortable indoor environment.展开更多
In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） f...In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.展开更多
Ice thermal storage is a promising technology to reduce energy costs by shifting the cooling cost from on-peak to off-peak periods. The paper investigates the application of ice thermal storage and its impact on energ...Ice thermal storage is a promising technology to reduce energy costs by shifting the cooling cost from on-peak to off-peak periods. The paper investigates the application of ice thermal storage and its impact on energy consumption, demand and total energy cost. Energy simulation software along with a chiller model is used to simulate the energy consumption and demand for the existing office building located in central Florida. Furthermore, the study presents a case study to demonstrate the cost saving achieved by the ice storage applications. The results show that although the energy consumption may increase by using ice thermal storage, the energy cost drops significantly, mainly depending on the local utility rate structure. It found that for the investigated system the annual energy consumption increases by about 12% but the annual energy cost drops by about 3 6%.展开更多
Owing to the fluctuant renewable generation and power demand,the energy surplus or deficit in nanogrids embodies differently across time.To stimulate local renewable energy consumption and minimize long-term energy co...Owing to the fluctuant renewable generation and power demand,the energy surplus or deficit in nanogrids embodies differently across time.To stimulate local renewable energy consumption and minimize long-term energy costs,some issues still remain to be explored:when and how the energy demand and bidirectional trading prices are scheduled considering personal comfort preferences and environmental factors.For this purpose,the demand response and two-way pricing problems concurrently for nanogrids and a public monitoring entity(PME)are studied with exploiting the large potential thermal elastic ability of heating,ventilation and air-conditioning(HVAC)units.Different from nanogrids,in terms of minimizing time-average costs,PME aims to set reasonable prices and optimize profits by trading with nanogrids and the main grid bi-directionally.Such bilevel energy management problem is formulated as a stochastic form in a longterm horizon.Since there are uncertain system parameters,time-coupled queue constraints and the interplay of bilevel decision-making,it is challenging to solve the formulated problems.To this end,we derive a form of relaxation based on Lyapunov optimization technique to make the energy management problem tractable without forecasting the related system parameters.The transaction between nanogrids and PME is captured by a one-leader and multi-follower Stackelberg game framework.Then,theoretical analysis of the existence and uniqueness of Stackelberg equilibrium(SE)is developed based on the proposed game property.Following that,we devise an optimization algorithm to reach the SE with less information exchange.Numerical experiments validate the effectiveness of the proposed approach.展开更多
Heating, ventilation, and air conditioning (HVAC) system is significant to the energy efficiency in buildings. In this paper, temperature control of HVAC system is studied in winter operation season. The physical mode...Heating, ventilation, and air conditioning (HVAC) system is significant to the energy efficiency in buildings. In this paper, temperature control of HVAC system is studied in winter operation season. The physical model of the zone, the fan, the heating coil and sensor are built. HVAC is a non-linear, strong disturbance and coupling system. Linear active-rejection-disturbance-control is an appreciate control algorithm which can adapt to less information, strong-disturbance influence, and has relative-fixed structure and simple tuning process of the controller parameters. Active-rejection-disturbance-control of the HVAC system is proposed. Simulation in Matlab/Simulink was done. Simulation results show that linear active-rejection-disturbance-control was prior to PID and integral-fuzzy controllers in rising time, overshoot and response time of step disturbance. The study can provide fundamental basis for the control of the air-condition system with strong-disturbance and high-precision needed.展开更多
As mentioned by National Geographic(2017),70%of world’s population is expected to live in large apartment buildings by 2050.Today,buildings in cities generate 30%of world’s greenhouse gas emission or GHG(National Ge...As mentioned by National Geographic(2017),70%of world’s population is expected to live in large apartment buildings by 2050.Today,buildings in cities generate 30%of world’s greenhouse gas emission or GHG(National Geographic,2017).Major urban centers are committed to reducing greenhouse gases by 80%by 2050(IEA,2021).However,achieving such goals in rental properties is not easy.Landlords are hesitant to use high-efficiency technologies because,typically,tenants pay the utilities bill.However,that situation is rapidly changing.For example,New York City like other US cities,is considering a carbon cap on all large buildings(Local Law 97,2019).That means landlords will pay a carbon penalty if the building’s carbon footprint exceeds certain threshold no matter who uses that carbon.The Pacific Northwest National Laboratory(PNNL)has received funds from DOE(US Department of Energy)with the collaboration of a commercial partner to address emerging energy efficiency market opportunity in multi-family or rental housing as discussed above.It has partnered with a large national real estate owner in order to test a novel energy optimization method at a rental property in Tempe,Arizona.By using a seamless-integrated method of acquiring building’s operating data,the optimization approach essentially resets setpoints of different energy consuming equipment such as chillers,boilers,pumps,and fans.Data-driven optimization approach is pragmatic and easily transferrable to other buildings.The authors shall share the problem background,technical approach,and preliminary results.展开更多
With the increasing demand for a better life, people have higher and higher requirements for the quality of indoor environment for work and life. In order to improve the quality of indoor environment, HVAC system has ...With the increasing demand for a better life, people have higher and higher requirements for the quality of indoor environment for work and life. In order to improve the quality of indoor environment, HVAC system has become an indispensable part of building system. But at the same time, the energy consumption of HVAC system also accounts for 50~60% of the total energy consumption of buildings, and it is a major contributor to the total energy consumption of buildings. Therefore, it is of great significance to carry out research on energy-saving technology of HVAC system and improve energy efficiency for achieving the goal of "double carbon". Based on this, this paper takes the urgency and importance of the application of energy-saving measures of HVAC system as the starting point, expounds the energy-saving design principles of building HVAC system, and puts forward some main energy-saving measures of HVAC system for reference.展开更多
Demand Response(DR)is a critical strategy for managing the integration of renewable energy sources into the power grid,addressing the challenges posed by their intermittent and unpredictable nature.This study introduc...Demand Response(DR)is a critical strategy for managing the integration of renewable energy sources into the power grid,addressing the challenges posed by their intermittent and unpredictable nature.This study introduces a rapid evaluation method for assessing the DR potential of large-scale Heating,Ventilation,and Air Conditioning(HVAC)systems,focusing on the significant role these systems play in energy consumption and grid flexibility.Firstly,the methodology involves constructing a simulation model library that encompasses three dimensions including room type,room location,and internal heat gain mode to reflect the dynamic characteristics of cooling load.Additionally,batch simulations generate DR profiles under various typical weather conditions,and surrogate models are trained for each simulation model,leveraging feature engineering and cross-validation to enhance accuracy.The Multi-Layer Perceptron(MLP)surrogate models achieve high accuracy in predicting DR potential under various scenarios,with R^(2) values exceeding 0.95.This study provides a robust framework that enables load aggregators to accurately estimate the demand response potential of large-scale HVAC systems.It supports the quantification of response capabilities and facilitates participation in bidding processes.Furthermore,it highlights the potential of data-driven models to enable rapid and scalable energy management.展开更多
Heating,ventilation,and air conditioning system runtime is a crucial metric for establishing the connection between system operation and energy performance.Similar homes in the same location can have varying runtime d...Heating,ventilation,and air conditioning system runtime is a crucial metric for establishing the connection between system operation and energy performance.Similar homes in the same location can have varying runtime due to different factors.To understand such heterogeneity,this study conducted an energy signature analysis of heating and cooling system runtime for 5,014 homes across the US>using data from ecobee smart thermostats.Two approaches were compared for the energy signature analysis:(1)using daily mean outdoor temperature and(2)using the difference between the daily mean outdoor temperature and the indoor thermostat setpoint(delta T)as the independent variable.The best-fitting energy signature parameters(balance temperatures and slopes)for each house were estimated and statistically analyzed.The results revealed significant differences in balance temperatures and slopes across various climates and individual homes.Additionally,we identified the impact of housing characteristics and weather conditions on the energy signature parameters using a long absolute shrinkage and selection operator(LASSO)regression.Incorporating delta T into the energy signature model significantly enhances its ability to detect hidden impacts of various features by minimizing the influence of setpoint preferences.Moreover,our cooling slope analysis highlights the significant impact of outdoor humidity levels,underscoring the need to include latent loads in building energy models.展开更多
The power flexibility from smart buildings makes them suitable candidates for providing grid services.The building automation system(BAS)that employs model predictive control(MPC)for grid services relies heavily on se...The power flexibility from smart buildings makes them suitable candidates for providing grid services.The building automation system(BAS)that employs model predictive control(MPC)for grid services relies heavily on sensor data gathered from IoTbased HVAC systems through communication networks.However,cyberattacks that tamper sensor values can compromise the accuracy and flexibility of HVAC system power adjustment.Existing studies on grid‐interactive buildings mainly focus on the efficiency and flexibility of buildings’participation in grid operations,whereas the security aspect is lacking.In this paper,we investigate the effects of cyberattacks on HVAC systems in grid-interactive buildings,specifically their power-tracking performance.We design a stochastic optimisation-based stealthy sensor attack and a corresponding defence strategy using a robust control framework.The attack and its defence are tested in a physical model of a test building with a single-chiller HVAC system.Simulation results demonstrate that minor falsifications caused by a stealthy sensor attack can significantly alter the power profile,leading to large power tracking errors.However,the robust control framework can reduce the power tracking error by over 70% under such attacks without filtering out compromised data.展开更多
Heating,ventilation,and air conditioning(HVAC)systems constitute a significant portion of the office building load and are important flexibility resources.However,the HVAC loads are often inaccessible to the utility o...Heating,ventilation,and air conditioning(HVAC)systems constitute a significant portion of the office building load and are important flexibility resources.However,the HVAC loads are often inaccessible to the utility or load aggregators who only have total load data.Most existing studies require subloads for supervised disaggregation or prior knowledge for unsupervised disaggregation,but such information is hard to obtain.It is necessary to develop an effective,completely unsupervised non-intrusive monitoring method to obtain the HVAC load data.In this study,a multiple seasonal-trend decomposition using the LOESS(MSTL)method is proposed to disaggregate the HVAC load from the total metered electricity data of office buildings.The effects of periodic types(daily,weekly,monthly,etc.),periodic sequences,and parallel/serial structures are analyzed.The proposed method is verified based on the historical electricity data of ten buildings.The results show that the proposed MSTL can accurately disaggregate the HVAC load with a coefficient of variation of the root mean square error(CVRMSE)of 10.94%,a normalized root mean squared error(NRMSE)of 2.1%,and a weighted absolute percentage error(WAPE)of 8.52%.Compared to single-cycle STL,the proposed method can significantly improve load disaggregation performance,with a maximum reduction of 16.36%in CVRMSE,5.3%in NRMSE,and 12.91%in WAPE.Backward-chain-based MSTL is recommended with higher accuracy and robustness.The proposed method provides an effective solution for utilities or load aggregators to improve demand response management and grid stability.展开更多
Heating,ventilation and air conditioning(HVAC)systems maintain personal thermal comfort(PTC)and serve as key demand response(DR)resources.Given the challenges of uncertain environments,varied user preferences,and lega...Heating,ventilation and air conditioning(HVAC)systems maintain personal thermal comfort(PTC)and serve as key demand response(DR)resources.Given the challenges of uncertain environments,varied user preferences,and legal requirements for transparent control strategies,this paper proposes an explainable reinforcement learning(XRL)solution for household multi-zone HVAC systems.This approach aims to optimize energy costs,ensure users’PTC,and maintain the explainability of the DR strategy under uncertainty.Firstly,an XRL-based optimization framework is proposed.The framework utilizes XRL’s online learning capabilities to handle uncertainties and meet the PTC requirements of different zones while maintaining the explainability of the optimization strategy.Then,we propose an explainable proximal policy optimization(XPPO)algorithm as an instantiation of XRL for optimizing household multi-zone HVAC systems,using interpretable continuous control trees as actor networks of the XPPO.Moreover,we design state space,action space,reward,learning network,and learning algorithm of the XPPO in detail for the needs of HVAC operational optimization.Simulation results show that our method is explainable and thus can fulfill the requirements of the laws.At the same time,the proposed method consumes 22.4%less energy cost compared to scenarios without DR.Furthermore,the optimization of DR for a typical 4-zone household can be achieved within 15 min on an i7-12700 CPU.展开更多
Sensor faults,which are primarily caused by environmental changes,calibration deficiencies,and component aging,critically compromise energy efficiency and operational reliability for building heating,ventilation and a...Sensor faults,which are primarily caused by environmental changes,calibration deficiencies,and component aging,critically compromise energy efficiency and operational reliability for building heating,ventilation and air-conditioning(HVAC)systems.Although conventional data-driven sensor fault calibration methods showed theoretical precision with low variable dependency,their practical implementation still faces challenges:difficulties in maintaining high accuracy and stability during model updates and HVAC system operation varies,insufficient data quantity and quality for effective modeling.To address these challenges,this study proposed a forgetting-adaptive(FA)mechanism based on data incremental learning(DIL),and develops a data selection method by autoencoder(AE)reconstruction to enhance Bayesian inference(BI)calibration models.FA selectively forgets and discards low-contribution data samples via AE reconstruction distance analysis while adaptively integrating high-contribution newly incremental data.Validations were conducted on two case studies:an EnergyPlus-Python simulated Chiller-AHU system and a practical water-cooled chiller system.It was revealed that FA reduced sensor calibration mean absolute error by 20.21%on average compared to the traditional MLR-BI.The impacts of modeling data volume on calibration performance were also explored,FA can maintain calibration accuracy with relatively limited data volumes.Also,this study tried to interpret the FA mechanism in BI model improvement by assessing the modeling data quality using the AE based reconstruction distances and adaptively selecting the high-contribution data via the AEThreshold.展开更多
Supervisory control can be used to optimize the HVAC system operation and achieve building energy conservation,while reinforcement learning(RL)is considered as a promising model-free supervisory control method.In this...Supervisory control can be used to optimize the HVAC system operation and achieve building energy conservation,while reinforcement learning(RL)is considered as a promising model-free supervisory control method.In this paper,we apply RL algorithm to the operation optimization of air-conditioning(AC)system and propose an innovative RL-based model-free control strategy combining rule-based and RL-based control algorithm as well as complete application process.We use a variable air volume(VAV)air-conditioning system for a single-storey office building as a case study to validate the optimization performance of the RL-based controller.We select control strategies with the rule-based control controller(RBC)and proportional-integral-derivative(PID)controller respectively as the reference cases.The results show that,for the air supply of single zone,the RL controller performs the best in terms of both non-comfortable time and energy costs of AC system after one-year exploration learning.The total energy consumption of AC system reduced by 7.7%and 4.7%,respectively compared with RBC and PID strategies.For the air supply of multi-zone,the performance of RL controller begins to outperform the reference strategies after two-year exploration learning and two-year buffer stage.From the seventh year on,RL controller performs much better in terms of both non-comfortable time and operating costs of AC system,while the operating cost of AC system is reduced by 2.7%to 4.6%compared with the reference strategies.In addition,RL controller is more suitable for small-scale operation optimization problems.展开更多
The continuous accumulation of operational data has provided an ideal platform to devise and implement customized data analytics for smart HVAC fault detection and diagnosis.In practice,the potentials of advanced supe...The continuous accumulation of operational data has provided an ideal platform to devise and implement customized data analytics for smart HVAC fault detection and diagnosis.In practice,the potentials of advanced supervised learning algorithms have not been fully realized due to the lack of sufficient labeled data.To tackle such data challenges,this study proposes a graph neural network-based approach to effectively utilizing both labeled and unlabeled operational data for optimum decision-makings.More specifically,a graph generation method is proposed to transform tabular building operational data into association graphs,based on which graph convolutions are performed to derive useful insights for fault classifications.Data experiments have been designed to evaluate the values of the methods proposed.Three datasets on HVAC air-side operations have been used to ensure the generalizability of results obtained.Different data scenarios,which vary in training data amounts and imbalance ratios,have been created to comprehensively quantify behavioral patterns of representative graph convolution networks and their architectures.The research results indicate that graph neural networks can effectively leverage associations among labeled and unlabeled data samples to achieve an increase of 2.86%–7.30%in fault classification accuracies,providing a novel and promising solution for smart building management.展开更多
Although computer technologies have greatly advanced in recent years and help engineers improve work efficiency,the heating,ventilation,and air conditioning(HVAC)design process is still very time-consuming.In this pap...Although computer technologies have greatly advanced in recent years and help engineers improve work efficiency,the heating,ventilation,and air conditioning(HVAC)design process is still very time-consuming.In this paper,we propose a conceptual framework for automating the entire design process to replace current human-based HVAC design procedures.This framework includes the following automated processes:building information modeling(BIM)simplification,building energy modeling(BEM)generation&load calculation,HVAC system topology generation&equipment sizing,and system diagram generation.In this study,we analyze the importance of each process and possible ways to implement them using software.Then,we use a case study to test the automated design procedure and illustrate the feasibility of the new automated design approach.The purpose of this study is to simplify the steps in the traditional rule-based HVAC system design process by introducing artificial intelligence(Al)technology based on the traditional computer-aided design(CAD)process.Experimental results show that the automatic processes are feasible,compared with the traditional design process can effectively shorten the design time from 23.37 working hours to nearly 1 hour,and improve the efficiency.展开更多
Heating,ventilation and air conditioning(HVAC)systems are the most energy-consuming building implements for the improvement of indoor environmental quality(IEQ).We have developed the optimal control strategies for HVA...Heating,ventilation and air conditioning(HVAC)systems are the most energy-consuming building implements for the improvement of indoor environmental quality(IEQ).We have developed the optimal control strategies for HVAC system to respectively achieve the optimal selections of ventilation rate and supplied air temperature with consideration of energy conservation,through the fast prediction methods by using low-dimensional linear ventilation model(LLVM)based artificial neural network(ANN)and low-dimensional linear temperature model(LLTM)based contribution ratio of indoor climate(CRI_((T))).To be continued for integrated control of multi-parameters,we further developed the fast prediction model for indoor humidity by using low-dimensional linear humidity model(LLHM)and contribution ratio of indoor humidity(CRI_((H))),and thermal sensation index(TS)for assessment.CFD was used to construct the prediction database for CO_(2),temperature and humidity.Low-dimensional linear models(LLM),including LLVM,LLTM and LLHM,were adopted to expand database for the sake of data storage reduction.Then,coupling with ANN,CRI_((T)) and CRI_((H)), the distributions of indoor CO_(2) concentration,temperature,and humidity were rapidly predicted on the basis of LLVM-based ANN,LLTM-based CRIm and LLHM-based CRM respectively.Finally,according to the self-defined indices(i.e.,E_(V),E_(T),E_(H)),the optimal balancing between IEQ(indicated by CO_(2) concentration,PMV and TS)and energy consumption(indicated by ventilation rate,supplied air temperature and humidity)were synthetically evaluated.The total HVAC energy consumption could be reduced by 35%on the strength of current control strategies.This work can further contribute to development of the intelligent online control for HVAC systems.展开更多
The prediction of building energy consumption offers essential technical support for intelligent operation and maintenance of buildings,promoting energy conservation and low-carbon control.This paper focused on the en...The prediction of building energy consumption offers essential technical support for intelligent operation and maintenance of buildings,promoting energy conservation and low-carbon control.This paper focused on the energy consumption of heating,ventilation and air conditioning(HVAC)systems operating under various modes across different seasons.We constructed multi-attribute and high-dimensional clustering vectors that encompass indoor and outdoor environmental parameters,along with historical energy consumption data.To enhance the K-means algorithm,we employed statistical feature extraction and dimensional normalization(SFEDN)to facilitate data clustering and deconstruction.This method,combined with the gated recurrent unit(GRU)prediction model employing adaptive training based on the Particle Swarm Optimization algorithm,was evaluated for robustness and stability through k-fold cross-validation.Within the clustering-based modeling framework,optimal submodels were configured based on the statistical features of historical 24-hour data to achieve dynamic prediction using multiple models.The dynamic prediction models with SFEDN cluster showed a 11.9%reduction in root mean square error(RMSE)compared to static prediction,achieving a coefficient of determination(R2)of 0.890 and a mean absolute percentage error(MAPE)reduction of 19.9%.When compared to dynamic prediction based on single-attribute of HVAC systems energy consumption clustering modeling,RMSE decreased by 12.6%,R2 increased by 4.0%,and MAPE decreased by 26.3%.The dynamic prediction performance demonstrated that the SFEDN clustering method surpasses conventional clustering method,and multi-attribute clustering modeling outperforms single-attribute modeling.展开更多
Fault detection and diagnosis(FDD)of heating,ventilation,and air conditioning(HVAC)systems can help to improve the energy saving in building energy systems.However,most data-driven trained FDD models have limited gene...Fault detection and diagnosis(FDD)of heating,ventilation,and air conditioning(HVAC)systems can help to improve the energy saving in building energy systems.However,most data-driven trained FDD models have limited generalizability and can only be applied to specific systems.The diversity of HVAC systems and the high cost of data acquisition present challenges for the practical application of FDD.Transfer learning technology can be employed to mitigate this problem by training a model on systems with sufficient data and then transfer it to other systems with limited data.In this study,a novel transfer learning approach for HVAC FDD is proposed.First,the transformer model is modified to incorporate one encoder and two decoders connected,enabling two outputs.This modified transformer model accommodates absent features in the target domain and serves as a robust foundation for transfer learning.It has effective performance in complex systems and achieves an accuracy of 91.38%for a system with 16 faults and multiple fault severity levels.Second,the adapter-based parameter-efficient transfer learning method,facilitating the transfer of trained models simply by inserting small adapter modules,is investigated as the transfer learning strategy.Results demonstrate that this adapter-based transfer learning approach achieves satisfactory performance similar to full fine-tuning with fewer trainable parameters.It works well with limited data amount in target domain.Furthermore,the findings highlight the significance of adapters positioned near the bottom and top layers,emphasizing their critical role in facilitating successful transfer learning.展开更多
基金National Key R&D Program of China of the 13th Five-Year Plan(No.2018YFD1100704)。
文摘To meet the challenge of mismatches between power supply and demand,modern buildings must schedule flexible energy loads in order to improve the efficiency of power grids.Furthermore,it is essential to understand the effectiveness of flexibility management strategies under different climate conditions and extreme weather events.Using both typical and extreme weather data from cities in five major climate zones of China,this study investigates the energy flexibility potential of an office building under three short-term HVAC management strategies in the context of different climates.The results show that the peak load flexibility and overall energy performance of the three short-term strategies were affected by the surrounding climate conditions.The peak load reduction rate of the pre-cooling and zone temperature reset strategies declined linearly as outdoor temperature increased.Under extreme climate conditions,the daily peak-load time was found to be over two hours earlier than under typical conditions,and the intensive solar radiation found in the extreme conditions can weaken the correlation between peak load reduction and outdoor temperature,risking the ability of a building’s HVAC system to maintain a comfortable indoor environment.
基金This work was supported by Young Scientists Fundamental Research Program of Shandong Province of China (No. 031B5147).
文摘In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.
文摘Ice thermal storage is a promising technology to reduce energy costs by shifting the cooling cost from on-peak to off-peak periods. The paper investigates the application of ice thermal storage and its impact on energy consumption, demand and total energy cost. Energy simulation software along with a chiller model is used to simulate the energy consumption and demand for the existing office building located in central Florida. Furthermore, the study presents a case study to demonstrate the cost saving achieved by the ice storage applications. The results show that although the energy consumption may increase by using ice thermal storage, the energy cost drops significantly, mainly depending on the local utility rate structure. It found that for the investigated system the annual energy consumption increases by about 12% but the annual energy cost drops by about 3 6%.
基金Supported by the National Key Research and Development Program of China(2018YFB1702300)the National Natural Science Foundation of China(61731012)。
文摘Owing to the fluctuant renewable generation and power demand,the energy surplus or deficit in nanogrids embodies differently across time.To stimulate local renewable energy consumption and minimize long-term energy costs,some issues still remain to be explored:when and how the energy demand and bidirectional trading prices are scheduled considering personal comfort preferences and environmental factors.For this purpose,the demand response and two-way pricing problems concurrently for nanogrids and a public monitoring entity(PME)are studied with exploiting the large potential thermal elastic ability of heating,ventilation and air-conditioning(HVAC)units.Different from nanogrids,in terms of minimizing time-average costs,PME aims to set reasonable prices and optimize profits by trading with nanogrids and the main grid bi-directionally.Such bilevel energy management problem is formulated as a stochastic form in a longterm horizon.Since there are uncertain system parameters,time-coupled queue constraints and the interplay of bilevel decision-making,it is challenging to solve the formulated problems.To this end,we derive a form of relaxation based on Lyapunov optimization technique to make the energy management problem tractable without forecasting the related system parameters.The transaction between nanogrids and PME is captured by a one-leader and multi-follower Stackelberg game framework.Then,theoretical analysis of the existence and uniqueness of Stackelberg equilibrium(SE)is developed based on the proposed game property.Following that,we devise an optimization algorithm to reach the SE with less information exchange.Numerical experiments validate the effectiveness of the proposed approach.
文摘Heating, ventilation, and air conditioning (HVAC) system is significant to the energy efficiency in buildings. In this paper, temperature control of HVAC system is studied in winter operation season. The physical model of the zone, the fan, the heating coil and sensor are built. HVAC is a non-linear, strong disturbance and coupling system. Linear active-rejection-disturbance-control is an appreciate control algorithm which can adapt to less information, strong-disturbance influence, and has relative-fixed structure and simple tuning process of the controller parameters. Active-rejection-disturbance-control of the HVAC system is proposed. Simulation in Matlab/Simulink was done. Simulation results show that linear active-rejection-disturbance-control was prior to PID and integral-fuzzy controllers in rising time, overshoot and response time of step disturbance. The study can provide fundamental basis for the control of the air-condition system with strong-disturbance and high-precision needed.
文摘As mentioned by National Geographic(2017),70%of world’s population is expected to live in large apartment buildings by 2050.Today,buildings in cities generate 30%of world’s greenhouse gas emission or GHG(National Geographic,2017).Major urban centers are committed to reducing greenhouse gases by 80%by 2050(IEA,2021).However,achieving such goals in rental properties is not easy.Landlords are hesitant to use high-efficiency technologies because,typically,tenants pay the utilities bill.However,that situation is rapidly changing.For example,New York City like other US cities,is considering a carbon cap on all large buildings(Local Law 97,2019).That means landlords will pay a carbon penalty if the building’s carbon footprint exceeds certain threshold no matter who uses that carbon.The Pacific Northwest National Laboratory(PNNL)has received funds from DOE(US Department of Energy)with the collaboration of a commercial partner to address emerging energy efficiency market opportunity in multi-family or rental housing as discussed above.It has partnered with a large national real estate owner in order to test a novel energy optimization method at a rental property in Tempe,Arizona.By using a seamless-integrated method of acquiring building’s operating data,the optimization approach essentially resets setpoints of different energy consuming equipment such as chillers,boilers,pumps,and fans.Data-driven optimization approach is pragmatic and easily transferrable to other buildings.The authors shall share the problem background,technical approach,and preliminary results.
文摘With the increasing demand for a better life, people have higher and higher requirements for the quality of indoor environment for work and life. In order to improve the quality of indoor environment, HVAC system has become an indispensable part of building system. But at the same time, the energy consumption of HVAC system also accounts for 50~60% of the total energy consumption of buildings, and it is a major contributor to the total energy consumption of buildings. Therefore, it is of great significance to carry out research on energy-saving technology of HVAC system and improve energy efficiency for achieving the goal of "double carbon". Based on this, this paper takes the urgency and importance of the application of energy-saving measures of HVAC system as the starting point, expounds the energy-saving design principles of building HVAC system, and puts forward some main energy-saving measures of HVAC system for reference.
基金supported by the State Grid Headquarters Science and Technology Fund(5400-202340383A-2-3-XG).
文摘Demand Response(DR)is a critical strategy for managing the integration of renewable energy sources into the power grid,addressing the challenges posed by their intermittent and unpredictable nature.This study introduces a rapid evaluation method for assessing the DR potential of large-scale Heating,Ventilation,and Air Conditioning(HVAC)systems,focusing on the significant role these systems play in energy consumption and grid flexibility.Firstly,the methodology involves constructing a simulation model library that encompasses three dimensions including room type,room location,and internal heat gain mode to reflect the dynamic characteristics of cooling load.Additionally,batch simulations generate DR profiles under various typical weather conditions,and surrogate models are trained for each simulation model,leveraging feature engineering and cross-validation to enhance accuracy.The Multi-Layer Perceptron(MLP)surrogate models achieve high accuracy in predicting DR potential under various scenarios,with R^(2) values exceeding 0.95.This study provides a robust framework that enables load aggregators to accurately estimate the demand response potential of large-scale HVAC systems.It supports the quantification of response capabilities and facilitates participation in bidding processes.Furthermore,it highlights the potential of data-driven models to enable rapid and scalable energy management.
基金supported by the National Science Foundation(award OAC-2005572)the State of Illinois,USA.
文摘Heating,ventilation,and air conditioning system runtime is a crucial metric for establishing the connection between system operation and energy performance.Similar homes in the same location can have varying runtime due to different factors.To understand such heterogeneity,this study conducted an energy signature analysis of heating and cooling system runtime for 5,014 homes across the US>using data from ecobee smart thermostats.Two approaches were compared for the energy signature analysis:(1)using daily mean outdoor temperature and(2)using the difference between the daily mean outdoor temperature and the indoor thermostat setpoint(delta T)as the independent variable.The best-fitting energy signature parameters(balance temperatures and slopes)for each house were estimated and statistically analyzed.The results revealed significant differences in balance temperatures and slopes across various climates and individual homes.Additionally,we identified the impact of housing characteristics and weather conditions on the energy signature parameters using a long absolute shrinkage and selection operator(LASSO)regression.Incorporating delta T into the energy signature model significantly enhances its ability to detect hidden impacts of various features by minimizing the influence of setpoint preferences.Moreover,our cooling slope analysis highlights the significant impact of outdoor humidity levels,underscoring the need to include latent loads in building energy models.
文摘The power flexibility from smart buildings makes them suitable candidates for providing grid services.The building automation system(BAS)that employs model predictive control(MPC)for grid services relies heavily on sensor data gathered from IoTbased HVAC systems through communication networks.However,cyberattacks that tamper sensor values can compromise the accuracy and flexibility of HVAC system power adjustment.Existing studies on grid‐interactive buildings mainly focus on the efficiency and flexibility of buildings’participation in grid operations,whereas the security aspect is lacking.In this paper,we investigate the effects of cyberattacks on HVAC systems in grid-interactive buildings,specifically their power-tracking performance.We design a stochastic optimisation-based stealthy sensor attack and a corresponding defence strategy using a robust control framework.The attack and its defence are tested in a physical model of a test building with a single-chiller HVAC system.Simulation results demonstrate that minor falsifications caused by a stealthy sensor attack can significantly alter the power profile,leading to large power tracking errors.However,the robust control framework can reduce the power tracking error by over 70% under such attacks without filtering out compromised data.
文摘Heating,ventilation,and air conditioning(HVAC)systems constitute a significant portion of the office building load and are important flexibility resources.However,the HVAC loads are often inaccessible to the utility or load aggregators who only have total load data.Most existing studies require subloads for supervised disaggregation or prior knowledge for unsupervised disaggregation,but such information is hard to obtain.It is necessary to develop an effective,completely unsupervised non-intrusive monitoring method to obtain the HVAC load data.In this study,a multiple seasonal-trend decomposition using the LOESS(MSTL)method is proposed to disaggregate the HVAC load from the total metered electricity data of office buildings.The effects of periodic types(daily,weekly,monthly,etc.),periodic sequences,and parallel/serial structures are analyzed.The proposed method is verified based on the historical electricity data of ten buildings.The results show that the proposed MSTL can accurately disaggregate the HVAC load with a coefficient of variation of the root mean square error(CVRMSE)of 10.94%,a normalized root mean squared error(NRMSE)of 2.1%,and a weighted absolute percentage error(WAPE)of 8.52%.Compared to single-cycle STL,the proposed method can significantly improve load disaggregation performance,with a maximum reduction of 16.36%in CVRMSE,5.3%in NRMSE,and 12.91%in WAPE.Backward-chain-based MSTL is recommended with higher accuracy and robustness.The proposed method provides an effective solution for utilities or load aggregators to improve demand response management and grid stability.
基金supported by the National Key R&D Program of China(Grant No.2018AAA0103300)the National Natural Science Foundation of China(Grant No.62122093)the Key Project of Scientific Research Fund of Hunan Provincial Education Department,China(Grant No.23A0142).
文摘Heating,ventilation and air conditioning(HVAC)systems maintain personal thermal comfort(PTC)and serve as key demand response(DR)resources.Given the challenges of uncertain environments,varied user preferences,and legal requirements for transparent control strategies,this paper proposes an explainable reinforcement learning(XRL)solution for household multi-zone HVAC systems.This approach aims to optimize energy costs,ensure users’PTC,and maintain the explainability of the DR strategy under uncertainty.Firstly,an XRL-based optimization framework is proposed.The framework utilizes XRL’s online learning capabilities to handle uncertainties and meet the PTC requirements of different zones while maintaining the explainability of the optimization strategy.Then,we propose an explainable proximal policy optimization(XPPO)algorithm as an instantiation of XRL for optimizing household multi-zone HVAC systems,using interpretable continuous control trees as actor networks of the XPPO.Moreover,we design state space,action space,reward,learning network,and learning algorithm of the XPPO in detail for the needs of HVAC operational optimization.Simulation results show that our method is explainable and thus can fulfill the requirements of the laws.At the same time,the proposed method consumes 22.4%less energy cost compared to scenarios without DR.Furthermore,the optimization of DR for a typical 4-zone household can be achieved within 15 min on an i7-12700 CPU.
基金supported by the National Natural Science Foundation of China(51906181)“The 14th Five Year Plan”Hubei Provincial advantaged characteristic disciplines(groups)project of Wuhan University of Science and Technology(2023D0504).
文摘Sensor faults,which are primarily caused by environmental changes,calibration deficiencies,and component aging,critically compromise energy efficiency and operational reliability for building heating,ventilation and air-conditioning(HVAC)systems.Although conventional data-driven sensor fault calibration methods showed theoretical precision with low variable dependency,their practical implementation still faces challenges:difficulties in maintaining high accuracy and stability during model updates and HVAC system operation varies,insufficient data quantity and quality for effective modeling.To address these challenges,this study proposed a forgetting-adaptive(FA)mechanism based on data incremental learning(DIL),and develops a data selection method by autoencoder(AE)reconstruction to enhance Bayesian inference(BI)calibration models.FA selectively forgets and discards low-contribution data samples via AE reconstruction distance analysis while adaptively integrating high-contribution newly incremental data.Validations were conducted on two case studies:an EnergyPlus-Python simulated Chiller-AHU system and a practical water-cooled chiller system.It was revealed that FA reduced sensor calibration mean absolute error by 20.21%on average compared to the traditional MLR-BI.The impacts of modeling data volume on calibration performance were also explored,FA can maintain calibration accuracy with relatively limited data volumes.Also,this study tried to interpret the FA mechanism in BI model improvement by assessing the modeling data quality using the AE based reconstruction distances and adaptively selecting the high-contribution data via the AEThreshold.
基金This study is supported by the Thirteenth Five-Year National Key Research and Development Program“Study on the Technical Standard System for Post-evaluation of Green Building Performance”,Ministry of Science and Technology of China(No.2016YFC0700105).
文摘Supervisory control can be used to optimize the HVAC system operation and achieve building energy conservation,while reinforcement learning(RL)is considered as a promising model-free supervisory control method.In this paper,we apply RL algorithm to the operation optimization of air-conditioning(AC)system and propose an innovative RL-based model-free control strategy combining rule-based and RL-based control algorithm as well as complete application process.We use a variable air volume(VAV)air-conditioning system for a single-storey office building as a case study to validate the optimization performance of the RL-based controller.We select control strategies with the rule-based control controller(RBC)and proportional-integral-derivative(PID)controller respectively as the reference cases.The results show that,for the air supply of single zone,the RL controller performs the best in terms of both non-comfortable time and energy costs of AC system after one-year exploration learning.The total energy consumption of AC system reduced by 7.7%and 4.7%,respectively compared with RBC and PID strategies.For the air supply of multi-zone,the performance of RL controller begins to outperform the reference strategies after two-year exploration learning and two-year buffer stage.From the seventh year on,RL controller performs much better in terms of both non-comfortable time and operating costs of AC system,while the operating cost of AC system is reduced by 2.7%to 4.6%compared with the reference strategies.In addition,RL controller is more suitable for small-scale operation optimization problems.
基金support of this research by the National Natural Science Foundation of China (No.52278117)the Philosophical and Social Science Program of Guangdong Province,China (GD22XGL20)the Shenzhen Science and Technology Program (No.20220531101800001 and No.20220810160221001).
文摘The continuous accumulation of operational data has provided an ideal platform to devise and implement customized data analytics for smart HVAC fault detection and diagnosis.In practice,the potentials of advanced supervised learning algorithms have not been fully realized due to the lack of sufficient labeled data.To tackle such data challenges,this study proposes a graph neural network-based approach to effectively utilizing both labeled and unlabeled operational data for optimum decision-makings.More specifically,a graph generation method is proposed to transform tabular building operational data into association graphs,based on which graph convolutions are performed to derive useful insights for fault classifications.Data experiments have been designed to evaluate the values of the methods proposed.Three datasets on HVAC air-side operations have been used to ensure the generalizability of results obtained.Different data scenarios,which vary in training data amounts and imbalance ratios,have been created to comprehensively quantify behavioral patterns of representative graph convolution networks and their architectures.The research results indicate that graph neural networks can effectively leverage associations among labeled and unlabeled data samples to achieve an increase of 2.86%–7.30%in fault classification accuracies,providing a novel and promising solution for smart building management.
基金This research is supported by China Southern Power Grid Co.LTD for the Science and Technology Project(Grant No.GDKJXM20212099).
文摘Although computer technologies have greatly advanced in recent years and help engineers improve work efficiency,the heating,ventilation,and air conditioning(HVAC)design process is still very time-consuming.In this paper,we propose a conceptual framework for automating the entire design process to replace current human-based HVAC design procedures.This framework includes the following automated processes:building information modeling(BIM)simplification,building energy modeling(BEM)generation&load calculation,HVAC system topology generation&equipment sizing,and system diagram generation.In this study,we analyze the importance of each process and possible ways to implement them using software.Then,we use a case study to test the automated design procedure and illustrate the feasibility of the new automated design approach.The purpose of this study is to simplify the steps in the traditional rule-based HVAC system design process by introducing artificial intelligence(Al)technology based on the traditional computer-aided design(CAD)process.Experimental results show that the automatic processes are feasible,compared with the traditional design process can effectively shorten the design time from 23.37 working hours to nearly 1 hour,and improve the efficiency.
基金the funding support from National Natural Science Foundation of China(No.51778385).
文摘Heating,ventilation and air conditioning(HVAC)systems are the most energy-consuming building implements for the improvement of indoor environmental quality(IEQ).We have developed the optimal control strategies for HVAC system to respectively achieve the optimal selections of ventilation rate and supplied air temperature with consideration of energy conservation,through the fast prediction methods by using low-dimensional linear ventilation model(LLVM)based artificial neural network(ANN)and low-dimensional linear temperature model(LLTM)based contribution ratio of indoor climate(CRI_((T))).To be continued for integrated control of multi-parameters,we further developed the fast prediction model for indoor humidity by using low-dimensional linear humidity model(LLHM)and contribution ratio of indoor humidity(CRI_((H))),and thermal sensation index(TS)for assessment.CFD was used to construct the prediction database for CO_(2),temperature and humidity.Low-dimensional linear models(LLM),including LLVM,LLTM and LLHM,were adopted to expand database for the sake of data storage reduction.Then,coupling with ANN,CRI_((T)) and CRI_((H)), the distributions of indoor CO_(2) concentration,temperature,and humidity were rapidly predicted on the basis of LLVM-based ANN,LLTM-based CRIm and LLHM-based CRM respectively.Finally,according to the self-defined indices(i.e.,E_(V),E_(T),E_(H)),the optimal balancing between IEQ(indicated by CO_(2) concentration,PMV and TS)and energy consumption(indicated by ventilation rate,supplied air temperature and humidity)were synthetically evaluated.The total HVAC energy consumption could be reduced by 35%on the strength of current control strategies.This work can further contribute to development of the intelligent online control for HVAC systems.
基金supported by the National Natural Science Foundation of China(No.52108074)the National Natural Science Foundation of China(No.52078144).
文摘The prediction of building energy consumption offers essential technical support for intelligent operation and maintenance of buildings,promoting energy conservation and low-carbon control.This paper focused on the energy consumption of heating,ventilation and air conditioning(HVAC)systems operating under various modes across different seasons.We constructed multi-attribute and high-dimensional clustering vectors that encompass indoor and outdoor environmental parameters,along with historical energy consumption data.To enhance the K-means algorithm,we employed statistical feature extraction and dimensional normalization(SFEDN)to facilitate data clustering and deconstruction.This method,combined with the gated recurrent unit(GRU)prediction model employing adaptive training based on the Particle Swarm Optimization algorithm,was evaluated for robustness and stability through k-fold cross-validation.Within the clustering-based modeling framework,optimal submodels were configured based on the statistical features of historical 24-hour data to achieve dynamic prediction using multiple models.The dynamic prediction models with SFEDN cluster showed a 11.9%reduction in root mean square error(RMSE)compared to static prediction,achieving a coefficient of determination(R2)of 0.890 and a mean absolute percentage error(MAPE)reduction of 19.9%.When compared to dynamic prediction based on single-attribute of HVAC systems energy consumption clustering modeling,RMSE decreased by 12.6%,R2 increased by 4.0%,and MAPE decreased by 26.3%.The dynamic prediction performance demonstrated that the SFEDN clustering method surpasses conventional clustering method,and multi-attribute clustering modeling outperforms single-attribute modeling.
基金supported by the National Natural Science Foundation of China(Grant Nos.:52293413 and 52076161).
文摘Fault detection and diagnosis(FDD)of heating,ventilation,and air conditioning(HVAC)systems can help to improve the energy saving in building energy systems.However,most data-driven trained FDD models have limited generalizability and can only be applied to specific systems.The diversity of HVAC systems and the high cost of data acquisition present challenges for the practical application of FDD.Transfer learning technology can be employed to mitigate this problem by training a model on systems with sufficient data and then transfer it to other systems with limited data.In this study,a novel transfer learning approach for HVAC FDD is proposed.First,the transformer model is modified to incorporate one encoder and two decoders connected,enabling two outputs.This modified transformer model accommodates absent features in the target domain and serves as a robust foundation for transfer learning.It has effective performance in complex systems and achieves an accuracy of 91.38%for a system with 16 faults and multiple fault severity levels.Second,the adapter-based parameter-efficient transfer learning method,facilitating the transfer of trained models simply by inserting small adapter modules,is investigated as the transfer learning strategy.Results demonstrate that this adapter-based transfer learning approach achieves satisfactory performance similar to full fine-tuning with fewer trainable parameters.It works well with limited data amount in target domain.Furthermore,the findings highlight the significance of adapters positioned near the bottom and top layers,emphasizing their critical role in facilitating successful transfer learning.
