Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System
This paper investigates how to develop a learning-based demand response approach for electric water heater in a smart home that can minimize the energy cost of the water heater while meeting the comfort requirements of energy consumers. First, a learning-based, data-driven model of an electric water...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/10/11/1722 |
| _version_ | 1811304131946610688 |
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| author | Bo Lin Shuhui Li Yang Xiao |
| author_facet | Bo Lin Shuhui Li Yang Xiao |
| author_sort | Bo Lin |
| collection | DOAJ |
| description | This paper investigates how to develop a learning-based demand response approach for electric water heater in a smart home that can minimize the energy cost of the water heater while meeting the comfort requirements of energy consumers. First, a learning-based, data-driven model of an electric water heater is developed by using a nonlinear autoregressive network with external input (NARX) using neural network. The model is updated daily so that it can more accurately capture the actual thermal dynamic characteristics of the water heater especially in real-life conditions. Then, an optimization problem, based on the NARX water heater model, is formulated to optimize energy management of the water heater in a day-ahead, dynamic electricity price framework. A genetic algorithm is proposed in order to solve the optimization problem more efficiently. MATLAB (R2016a) is used to evaluate the proposed learning-based demand response approach through a computational experiment strategy. The proposed approach is compared with conventional method for operation of an electric water heater. Cost saving and benefits of the proposed water heater energy management strategy are explored. |
| first_indexed | 2024-04-13T08:00:35Z |
| format | Article |
| id | doaj.art-d3451e6cf8034d1e8f5824c101d5e363 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T08:00:35Z |
| publishDate | 2017-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d3451e6cf8034d1e8f5824c101d5e3632022-12-22T02:55:17ZengMDPI AGEnergies1996-10732017-10-011011172210.3390/en10111722en10111722Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater SystemBo Lin0Shuhui Li1Yang Xiao2Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USADepartment of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USADepartment of Computer Science, The University of Alabama, Tuscaloosa, AL 35487, USAThis paper investigates how to develop a learning-based demand response approach for electric water heater in a smart home that can minimize the energy cost of the water heater while meeting the comfort requirements of energy consumers. First, a learning-based, data-driven model of an electric water heater is developed by using a nonlinear autoregressive network with external input (NARX) using neural network. The model is updated daily so that it can more accurately capture the actual thermal dynamic characteristics of the water heater especially in real-life conditions. Then, an optimization problem, based on the NARX water heater model, is formulated to optimize energy management of the water heater in a day-ahead, dynamic electricity price framework. A genetic algorithm is proposed in order to solve the optimization problem more efficiently. MATLAB (R2016a) is used to evaluate the proposed learning-based demand response approach through a computational experiment strategy. The proposed approach is compared with conventional method for operation of an electric water heater. Cost saving and benefits of the proposed water heater energy management strategy are explored.https://www.mdpi.com/1996-1073/10/11/1722electric water heaterenergy conservationthermodynamic modelingdemand-side managementsmart homes |
| spellingShingle | Bo Lin Shuhui Li Yang Xiao Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System Energies electric water heater energy conservation thermodynamic modeling demand-side management smart homes |
| title | Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System |
| title_full | Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System |
| title_fullStr | Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System |
| title_full_unstemmed | Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System |
| title_short | Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System |
| title_sort | optimal and learning based demand response mechanism for electric water heater system |
| topic | electric water heater energy conservation thermodynamic modeling demand-side management smart homes |
| url | https://www.mdpi.com/1996-1073/10/11/1722 |
| work_keys_str_mv | AT bolin optimalandlearningbaseddemandresponsemechanismforelectricwaterheatersystem AT shuhuili optimalandlearningbaseddemandresponsemechanismforelectricwaterheatersystem AT yangxiao optimalandlearningbaseddemandresponsemechanismforelectricwaterheatersystem |