An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network
In this study, a novel framework is proposed for efficient energy management of residential buildings to reduce the electricity bill, alleviate peak-to-average ratio (PAR), and acquire the desired trade-off between the electricity bill and user-discomfort in the smart grid. The proposed framework is...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9075174/ |
| _version_ | 1818379611687354368 |
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| author | Ghulam Hafeez Khurram Saleem Alimgeer Zahid Wadud Imran Khan Muhammad Usman Abdul Baseer Qazi Farrukh Aslam Khan |
| author_facet | Ghulam Hafeez Khurram Saleem Alimgeer Zahid Wadud Imran Khan Muhammad Usman Abdul Baseer Qazi Farrukh Aslam Khan |
| author_sort | Ghulam Hafeez |
| collection | DOAJ |
| description | In this study, a novel framework is proposed for efficient energy management of residential buildings to reduce the electricity bill, alleviate peak-to-average ratio (PAR), and acquire the desired trade-off between the electricity bill and user-discomfort in the smart grid. The proposed framework is an integrated framework of artificial neural network (ANN) based forecast engine and our proposed day-ahead grey wolf modified enhanced differential evolution algorithm (DA-GmEDE) based home energy management controller (HEMC). The forecast engine forecasts price-based demand response (DR) signal and energy consumption patterns and HEMC schedules smart home appliances under the forecasted pricing signal and energy consumption pattern for efficient energy management. The proposed DA-GmEDE based strategy is compared with two benchmark strategies: day-ahead genetic algorithm (DA-GA) based strategy, and day-ahead game-theory (DA-game-theoretic) based strategy for performance validation. Moreover, extensive simulations are conducted to test the effectiveness and productiveness of the proposed DA-GmEDE based strategy for efficient energy management. The results and discussion illustrate that the proposed DA-GmEDE strategy outperforms the benchmark strategies by 33.3% in terms of efficient energy management. |
| first_indexed | 2024-12-14T02:05:33Z |
| format | Article |
| id | doaj.art-eab011d78cea4a1cb59eb96447699600 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T02:05:33Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-eab011d78cea4a1cb59eb964476996002022-12-21T23:20:54ZengIEEEIEEE Access2169-35362020-01-018844158443310.1109/ACCESS.2020.29893169075174An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural NetworkGhulam Hafeez0https://orcid.org/0000-0002-9398-9414Khurram Saleem Alimgeer1https://orcid.org/0000-0001-5732-2463Zahid Wadud2https://orcid.org/0000-0001-7118-6496Imran Khan3Muhammad Usman4https://orcid.org/0000-0001-8363-0179Abdul Baseer Qazi5Farrukh Aslam Khan6https://orcid.org/0000-0002-7023-7172Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, PakistanDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, PakistanDepartment of Computer System Engineering, University of Engineering and Technology Peshawar, Peshawar, PakistanDepartment of Electrical Engineering, University of Engineering and Technology, Mardan, PakistanDepartment of Computer Software Engineering, University of Engineering and Technology, Mardan, PakistanDepartment of Software Engineering, Bahria University, Islamabad, PakistanCenter of Excellence in Information Assurance, King Saud University, Riyadh, Saudi ArabiaIn this study, a novel framework is proposed for efficient energy management of residential buildings to reduce the electricity bill, alleviate peak-to-average ratio (PAR), and acquire the desired trade-off between the electricity bill and user-discomfort in the smart grid. The proposed framework is an integrated framework of artificial neural network (ANN) based forecast engine and our proposed day-ahead grey wolf modified enhanced differential evolution algorithm (DA-GmEDE) based home energy management controller (HEMC). The forecast engine forecasts price-based demand response (DR) signal and energy consumption patterns and HEMC schedules smart home appliances under the forecasted pricing signal and energy consumption pattern for efficient energy management. The proposed DA-GmEDE based strategy is compared with two benchmark strategies: day-ahead genetic algorithm (DA-GA) based strategy, and day-ahead game-theory (DA-game-theoretic) based strategy for performance validation. Moreover, extensive simulations are conducted to test the effectiveness and productiveness of the proposed DA-GmEDE based strategy for efficient energy management. The results and discussion illustrate that the proposed DA-GmEDE strategy outperforms the benchmark strategies by 33.3% in terms of efficient energy management.https://ieeexplore.ieee.org/document/9075174/Advanced metering infrastructureartificial neural networksdemand responseenergy managementgrey wolf modified enhanced differential evolution algorithmsmart grid |
| spellingShingle | Ghulam Hafeez Khurram Saleem Alimgeer Zahid Wadud Imran Khan Muhammad Usman Abdul Baseer Qazi Farrukh Aslam Khan An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network IEEE Access Advanced metering infrastructure artificial neural networks demand response energy management grey wolf modified enhanced differential evolution algorithm smart grid |
| title | An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network |
| title_full | An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network |
| title_fullStr | An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network |
| title_full_unstemmed | An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network |
| title_short | An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network |
| title_sort | innovative optimization strategy for efficient energy management with day ahead demand response signal and energy consumption forecasting in smart grid using artificial neural network |
| topic | Advanced metering infrastructure artificial neural networks demand response energy management grey wolf modified enhanced differential evolution algorithm smart grid |
| url | https://ieeexplore.ieee.org/document/9075174/ |
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