The Experts below are selected from a list of 79716 Experts worldwide ranked by ideXlab platform
Yao Huang - One of the best experts on this subject based on the ideXlab platform.
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Machine learning-based thermal response time ahead energy demand prediction for Building Heating systems
Applied Energy, 2018Co-Authors: Yabin Guo, Huanxin Chen, Jiangyu Wang, Jiangyan Liu, Ronggeng Huang, Yao HuangAbstract:Abstract Energy demand prediction of Building Heating is conducive to optimal control, fault detection and diagnosis and Building intelligentization. In this study, energy demand prediction models are developed through machine learning methods, including extreme learning machine, multiple linear regression, support vector regression and backpropagation neural network. Seven different meteorological parameters, operating parameters, time and indoor temperature parameters are used as feature variables of the model. Correlation analysis method is utilized to optimize the feature sets. Moreover, this paper proposes a strategy for obtaining the thermal response time of Building, which is used as the time ahead of prediction models. The prediction performances of extreme learning machine models with various hidden layer nodes are analyzed and contrasted. Actual data of Building Heating using a ground source heat pump system are collected and used to test the performances of the models. Results show that the thermal response time of the Building is approximately 40 min. Four feature sets are obtained, and the performances of the models with feature set 4 are better. For different machine learning methods, the performances of extreme learning machine models are better than others. In addition, the optimal number of hidden layer nodes is 11 for the extreme learning machine model with feature set 4.
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A thermal response time ahead energy demand prediction strategy for Building Heating system using machine learning methods
Energy Procedia, 2017Co-Authors: Yabin Guo, Huanxin Chen, Jiangyu Wang, Yao HuangAbstract:Abstract Energy demand prediction of Building Heating is conducive to optimal control, fault detection and diagnosis and Building intelligent. In this paper, the prediction models are developed using machine learning methods including extreme learning machine (ELM), multiple linear regression, support vector regression and BP neural network. The feature variable sets are optimized through correlation analysis and supplementing indoor temperature. Besides, this paper proposed a strategy to determine the time ahead of prediction model. The thermal response time of Building is used as the prediction time step of model. The prediction performances of ELM models with different hidden layer nodes are analyzed and contrasted. The actual data of the Building Heating using ground source heat pump system are collected and used to test the performances of the models. The results show that the thermal response time of the Building is about 40 minutes. Four feature sets are obtained and performances of models with FS4 are better. For different machine learning methods, the performances of ELM models are better than others. In addition, the optimal number of hidden layer nodes is 11 for the ELM model with FS4.
Yabin Guo - One of the best experts on this subject based on the ideXlab platform.
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energy consumption prediction for water source heat pump system using pattern recognition based algorithms
Applied Thermal Engineering, 2018Co-Authors: Jiangyu Wang, Yabin Guo, Huanxin Chen, Jiangyan Liu, Shaobo SunAbstract:Abstract Building Heating/cooling consumption prediction is of great importance for HVAC system management tasks, such as optimal operation/control strategies, demand and supply management, abnormal energy diagnosis, etc. Compared to traditional methods, data-driven methods have received a lot of attention due to their flexibility and efficiency. In particular, this paper investigates the potential of data partitioning techniques in improving prediction performance of ultra-short-term Building Heating load prediction. Specifically, with three proposed statistical attributes of 32 days considered by clustering analysis, similar daily operation patterns of pumps (OPPs) in a water-source heat pump system (WSHPS) were identified stepwise. Afterward, the sub-models based on different OPPs were developed by machine learning methods and their performance were compared to the general model without data partitioning. In additional, an operation tree was constructed to predict daily OPPs based on historical weather conditions and available date information. With the assistance of the operation tree, the proposed method can be applied in online prediction. Based on the validation, it can be concluded that the introduction of OPPs-clustering can improve the performance of Building Heating load prediction.
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Machine learning-based thermal response time ahead energy demand prediction for Building Heating systems
Applied Energy, 2018Co-Authors: Yabin Guo, Huanxin Chen, Jiangyu Wang, Jiangyan Liu, Ronggeng Huang, Yao HuangAbstract:Abstract Energy demand prediction of Building Heating is conducive to optimal control, fault detection and diagnosis and Building intelligentization. In this study, energy demand prediction models are developed through machine learning methods, including extreme learning machine, multiple linear regression, support vector regression and backpropagation neural network. Seven different meteorological parameters, operating parameters, time and indoor temperature parameters are used as feature variables of the model. Correlation analysis method is utilized to optimize the feature sets. Moreover, this paper proposes a strategy for obtaining the thermal response time of Building, which is used as the time ahead of prediction models. The prediction performances of extreme learning machine models with various hidden layer nodes are analyzed and contrasted. Actual data of Building Heating using a ground source heat pump system are collected and used to test the performances of the models. Results show that the thermal response time of the Building is approximately 40 min. Four feature sets are obtained, and the performances of the models with feature set 4 are better. For different machine learning methods, the performances of extreme learning machine models are better than others. In addition, the optimal number of hidden layer nodes is 11 for the extreme learning machine model with feature set 4.
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A thermal response time ahead energy demand prediction strategy for Building Heating system using machine learning methods
Energy Procedia, 2017Co-Authors: Yabin Guo, Huanxin Chen, Jiangyu Wang, Yao HuangAbstract:Abstract Energy demand prediction of Building Heating is conducive to optimal control, fault detection and diagnosis and Building intelligent. In this paper, the prediction models are developed using machine learning methods including extreme learning machine (ELM), multiple linear regression, support vector regression and BP neural network. The feature variable sets are optimized through correlation analysis and supplementing indoor temperature. Besides, this paper proposed a strategy to determine the time ahead of prediction model. The thermal response time of Building is used as the prediction time step of model. The prediction performances of ELM models with different hidden layer nodes are analyzed and contrasted. The actual data of the Building Heating using ground source heat pump system are collected and used to test the performances of the models. The results show that the thermal response time of the Building is about 40 minutes. Four feature sets are obtained and performances of models with FS4 are better. For different machine learning methods, the performances of ELM models are better than others. In addition, the optimal number of hidden layer nodes is 11 for the ELM model with FS4.
Jiangyu Wang - One of the best experts on this subject based on the ideXlab platform.
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energy consumption prediction for water source heat pump system using pattern recognition based algorithms
Applied Thermal Engineering, 2018Co-Authors: Jiangyu Wang, Yabin Guo, Huanxin Chen, Jiangyan Liu, Shaobo SunAbstract:Abstract Building Heating/cooling consumption prediction is of great importance for HVAC system management tasks, such as optimal operation/control strategies, demand and supply management, abnormal energy diagnosis, etc. Compared to traditional methods, data-driven methods have received a lot of attention due to their flexibility and efficiency. In particular, this paper investigates the potential of data partitioning techniques in improving prediction performance of ultra-short-term Building Heating load prediction. Specifically, with three proposed statistical attributes of 32 days considered by clustering analysis, similar daily operation patterns of pumps (OPPs) in a water-source heat pump system (WSHPS) were identified stepwise. Afterward, the sub-models based on different OPPs were developed by machine learning methods and their performance were compared to the general model without data partitioning. In additional, an operation tree was constructed to predict daily OPPs based on historical weather conditions and available date information. With the assistance of the operation tree, the proposed method can be applied in online prediction. Based on the validation, it can be concluded that the introduction of OPPs-clustering can improve the performance of Building Heating load prediction.
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Machine learning-based thermal response time ahead energy demand prediction for Building Heating systems
Applied Energy, 2018Co-Authors: Yabin Guo, Huanxin Chen, Jiangyu Wang, Jiangyan Liu, Ronggeng Huang, Yao HuangAbstract:Abstract Energy demand prediction of Building Heating is conducive to optimal control, fault detection and diagnosis and Building intelligentization. In this study, energy demand prediction models are developed through machine learning methods, including extreme learning machine, multiple linear regression, support vector regression and backpropagation neural network. Seven different meteorological parameters, operating parameters, time and indoor temperature parameters are used as feature variables of the model. Correlation analysis method is utilized to optimize the feature sets. Moreover, this paper proposes a strategy for obtaining the thermal response time of Building, which is used as the time ahead of prediction models. The prediction performances of extreme learning machine models with various hidden layer nodes are analyzed and contrasted. Actual data of Building Heating using a ground source heat pump system are collected and used to test the performances of the models. Results show that the thermal response time of the Building is approximately 40 min. Four feature sets are obtained, and the performances of the models with feature set 4 are better. For different machine learning methods, the performances of extreme learning machine models are better than others. In addition, the optimal number of hidden layer nodes is 11 for the extreme learning machine model with feature set 4.
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A thermal response time ahead energy demand prediction strategy for Building Heating system using machine learning methods
Energy Procedia, 2017Co-Authors: Yabin Guo, Huanxin Chen, Jiangyu Wang, Yao HuangAbstract:Abstract Energy demand prediction of Building Heating is conducive to optimal control, fault detection and diagnosis and Building intelligent. In this paper, the prediction models are developed using machine learning methods including extreme learning machine (ELM), multiple linear regression, support vector regression and BP neural network. The feature variable sets are optimized through correlation analysis and supplementing indoor temperature. Besides, this paper proposed a strategy to determine the time ahead of prediction model. The thermal response time of Building is used as the prediction time step of model. The prediction performances of ELM models with different hidden layer nodes are analyzed and contrasted. The actual data of the Building Heating using ground source heat pump system are collected and used to test the performances of the models. The results show that the thermal response time of the Building is about 40 minutes. Four feature sets are obtained and performances of models with FS4 are better. For different machine learning methods, the performances of ELM models are better than others. In addition, the optimal number of hidden layer nodes is 11 for the ELM model with FS4.
Huanxin Chen - One of the best experts on this subject based on the ideXlab platform.
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energy consumption prediction for water source heat pump system using pattern recognition based algorithms
Applied Thermal Engineering, 2018Co-Authors: Jiangyu Wang, Yabin Guo, Huanxin Chen, Jiangyan Liu, Shaobo SunAbstract:Abstract Building Heating/cooling consumption prediction is of great importance for HVAC system management tasks, such as optimal operation/control strategies, demand and supply management, abnormal energy diagnosis, etc. Compared to traditional methods, data-driven methods have received a lot of attention due to their flexibility and efficiency. In particular, this paper investigates the potential of data partitioning techniques in improving prediction performance of ultra-short-term Building Heating load prediction. Specifically, with three proposed statistical attributes of 32 days considered by clustering analysis, similar daily operation patterns of pumps (OPPs) in a water-source heat pump system (WSHPS) were identified stepwise. Afterward, the sub-models based on different OPPs were developed by machine learning methods and their performance were compared to the general model without data partitioning. In additional, an operation tree was constructed to predict daily OPPs based on historical weather conditions and available date information. With the assistance of the operation tree, the proposed method can be applied in online prediction. Based on the validation, it can be concluded that the introduction of OPPs-clustering can improve the performance of Building Heating load prediction.
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Machine learning-based thermal response time ahead energy demand prediction for Building Heating systems
Applied Energy, 2018Co-Authors: Yabin Guo, Huanxin Chen, Jiangyu Wang, Jiangyan Liu, Ronggeng Huang, Yao HuangAbstract:Abstract Energy demand prediction of Building Heating is conducive to optimal control, fault detection and diagnosis and Building intelligentization. In this study, energy demand prediction models are developed through machine learning methods, including extreme learning machine, multiple linear regression, support vector regression and backpropagation neural network. Seven different meteorological parameters, operating parameters, time and indoor temperature parameters are used as feature variables of the model. Correlation analysis method is utilized to optimize the feature sets. Moreover, this paper proposes a strategy for obtaining the thermal response time of Building, which is used as the time ahead of prediction models. The prediction performances of extreme learning machine models with various hidden layer nodes are analyzed and contrasted. Actual data of Building Heating using a ground source heat pump system are collected and used to test the performances of the models. Results show that the thermal response time of the Building is approximately 40 min. Four feature sets are obtained, and the performances of the models with feature set 4 are better. For different machine learning methods, the performances of extreme learning machine models are better than others. In addition, the optimal number of hidden layer nodes is 11 for the extreme learning machine model with feature set 4.
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A thermal response time ahead energy demand prediction strategy for Building Heating system using machine learning methods
Energy Procedia, 2017Co-Authors: Yabin Guo, Huanxin Chen, Jiangyu Wang, Yao HuangAbstract:Abstract Energy demand prediction of Building Heating is conducive to optimal control, fault detection and diagnosis and Building intelligent. In this paper, the prediction models are developed using machine learning methods including extreme learning machine (ELM), multiple linear regression, support vector regression and BP neural network. The feature variable sets are optimized through correlation analysis and supplementing indoor temperature. Besides, this paper proposed a strategy to determine the time ahead of prediction model. The thermal response time of Building is used as the prediction time step of model. The prediction performances of ELM models with different hidden layer nodes are analyzed and contrasted. The actual data of the Building Heating using ground source heat pump system are collected and used to test the performances of the models. The results show that the thermal response time of the Building is about 40 minutes. Four feature sets are obtained and performances of models with FS4 are better. For different machine learning methods, the performances of ELM models are better than others. In addition, the optimal number of hidden layer nodes is 11 for the ELM model with FS4.
Jiangyan Liu - One of the best experts on this subject based on the ideXlab platform.
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energy consumption prediction for water source heat pump system using pattern recognition based algorithms
Applied Thermal Engineering, 2018Co-Authors: Jiangyu Wang, Yabin Guo, Huanxin Chen, Jiangyan Liu, Shaobo SunAbstract:Abstract Building Heating/cooling consumption prediction is of great importance for HVAC system management tasks, such as optimal operation/control strategies, demand and supply management, abnormal energy diagnosis, etc. Compared to traditional methods, data-driven methods have received a lot of attention due to their flexibility and efficiency. In particular, this paper investigates the potential of data partitioning techniques in improving prediction performance of ultra-short-term Building Heating load prediction. Specifically, with three proposed statistical attributes of 32 days considered by clustering analysis, similar daily operation patterns of pumps (OPPs) in a water-source heat pump system (WSHPS) were identified stepwise. Afterward, the sub-models based on different OPPs were developed by machine learning methods and their performance were compared to the general model without data partitioning. In additional, an operation tree was constructed to predict daily OPPs based on historical weather conditions and available date information. With the assistance of the operation tree, the proposed method can be applied in online prediction. Based on the validation, it can be concluded that the introduction of OPPs-clustering can improve the performance of Building Heating load prediction.
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Machine learning-based thermal response time ahead energy demand prediction for Building Heating systems
Applied Energy, 2018Co-Authors: Yabin Guo, Huanxin Chen, Jiangyu Wang, Jiangyan Liu, Ronggeng Huang, Yao HuangAbstract:Abstract Energy demand prediction of Building Heating is conducive to optimal control, fault detection and diagnosis and Building intelligentization. In this study, energy demand prediction models are developed through machine learning methods, including extreme learning machine, multiple linear regression, support vector regression and backpropagation neural network. Seven different meteorological parameters, operating parameters, time and indoor temperature parameters are used as feature variables of the model. Correlation analysis method is utilized to optimize the feature sets. Moreover, this paper proposes a strategy for obtaining the thermal response time of Building, which is used as the time ahead of prediction models. The prediction performances of extreme learning machine models with various hidden layer nodes are analyzed and contrasted. Actual data of Building Heating using a ground source heat pump system are collected and used to test the performances of the models. Results show that the thermal response time of the Building is approximately 40 min. Four feature sets are obtained, and the performances of the models with feature set 4 are better. For different machine learning methods, the performances of extreme learning machine models are better than others. In addition, the optimal number of hidden layer nodes is 11 for the extreme learning machine model with feature set 4.
