Smart building energy management with renewables and storage systems using a modified weighted mean of vectors algorithm
Abstract With the advancement of automation technologies in household appliances, the flexibility of smart home energy management (EM) systems has increased. However, this progress has brought about a new challenge for smart homes: the EM has become more complex with the integration of multiple conv...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-79782-5 |
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| author | Mohamed Ebeed Sabreen hassan Salah Kamel Loai Nasrat Ali Wagdy Mohamed Abdel-Raheem Youssef |
| author_facet | Mohamed Ebeed Sabreen hassan Salah Kamel Loai Nasrat Ali Wagdy Mohamed Abdel-Raheem Youssef |
| author_sort | Mohamed Ebeed |
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| description | Abstract With the advancement of automation technologies in household appliances, the flexibility of smart home energy management (EM) systems has increased. However, this progress has brought about a new challenge for smart homes: the EM has become more complex with the integration of multiple conventional, renewable, and energy storage systems. To address this challenge, a novel modified Weighted Mean of Vectors algorithm (MINFO) is proposed. This algorithm aims to enhance the performance of smart building EM by overcoming the limitations of conventional approaches, such as low solution accuracy and inadequacy in handling complex problems. MINFO operates on two key principles. Firstly, it employs the Elite Centroid Quasi-Oppositional Base Learning (ECQOBL) approach to improve the exploitation capabilities of conventional algorithms. Secondly, it utilizes an Adaptive Levy Flight Motion (ALFM) technique to enhance exploration. The EM problem tackled involves optimizing the scheduling of multiple energy sources, including diesel generators, PV units, and batteries, within a smart building context. Additionally, it incorporates time-of-use-based demand-side response (DSR) to manage shiftable loads, thereby reducing electricity costs and peak-to-average ratio (PAR) simultaneously and independently. The effectiveness of MINFO is demonstrated through comprehensive evaluations, comparing its performance with other optimization methods across 33 benchmark functions from basic and CEC-2019 test suites. Results indicate that MINFO significantly improves smart building EM, achieving a reduction of 53.20% in electricity costs (cost only), 53.19% in PAR (PAR only), and 50.84% in combined cost and PAR compared to the base case. These findings underscore the robustness of MINFO as an optimizer for smart building energy management. |
| format | Article |
| id | doaj-art-904fba277e3a4c97b29c8fd90f41c4e0 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-904fba277e3a4c97b29c8fd90f41c4e02025-02-09T12:35:01ZengNature PortfolioScientific Reports2045-23222025-02-0115113810.1038/s41598-024-79782-5Smart building energy management with renewables and storage systems using a modified weighted mean of vectors algorithmMohamed Ebeed0Sabreen hassan1Salah Kamel2Loai Nasrat3Ali Wagdy Mohamed4Abdel-Raheem Youssef5Department of Electrical Engineering, Faculty of Engineering, Sohag UniversityDepartment of Electrical Engineering, Aswan UniversityDepartment of Electrical Engineering, Aswan UniversityDepartment of Electrical Engineering, Aswan UniversityOperations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo UniversityDepartment of Electrical Engineering, Faculty of Engineering, South Valley UniversityAbstract With the advancement of automation technologies in household appliances, the flexibility of smart home energy management (EM) systems has increased. However, this progress has brought about a new challenge for smart homes: the EM has become more complex with the integration of multiple conventional, renewable, and energy storage systems. To address this challenge, a novel modified Weighted Mean of Vectors algorithm (MINFO) is proposed. This algorithm aims to enhance the performance of smart building EM by overcoming the limitations of conventional approaches, such as low solution accuracy and inadequacy in handling complex problems. MINFO operates on two key principles. Firstly, it employs the Elite Centroid Quasi-Oppositional Base Learning (ECQOBL) approach to improve the exploitation capabilities of conventional algorithms. Secondly, it utilizes an Adaptive Levy Flight Motion (ALFM) technique to enhance exploration. The EM problem tackled involves optimizing the scheduling of multiple energy sources, including diesel generators, PV units, and batteries, within a smart building context. Additionally, it incorporates time-of-use-based demand-side response (DSR) to manage shiftable loads, thereby reducing electricity costs and peak-to-average ratio (PAR) simultaneously and independently. The effectiveness of MINFO is demonstrated through comprehensive evaluations, comparing its performance with other optimization methods across 33 benchmark functions from basic and CEC-2019 test suites. Results indicate that MINFO significantly improves smart building EM, achieving a reduction of 53.20% in electricity costs (cost only), 53.19% in PAR (PAR only), and 50.84% in combined cost and PAR compared to the base case. These findings underscore the robustness of MINFO as an optimizer for smart building energy management.https://doi.org/10.1038/s41598-024-79782-5Smart homeWeighted mean of vectors algorithmCostPeak to average ratioDemand side response |
| spellingShingle | Mohamed Ebeed Sabreen hassan Salah Kamel Loai Nasrat Ali Wagdy Mohamed Abdel-Raheem Youssef Smart building energy management with renewables and storage systems using a modified weighted mean of vectors algorithm Scientific Reports Smart home Weighted mean of vectors algorithm Cost Peak to average ratio Demand side response |
| title | Smart building energy management with renewables and storage systems using a modified weighted mean of vectors algorithm |
| title_full | Smart building energy management with renewables and storage systems using a modified weighted mean of vectors algorithm |
| title_fullStr | Smart building energy management with renewables and storage systems using a modified weighted mean of vectors algorithm |
| title_full_unstemmed | Smart building energy management with renewables and storage systems using a modified weighted mean of vectors algorithm |
| title_short | Smart building energy management with renewables and storage systems using a modified weighted mean of vectors algorithm |
| title_sort | smart building energy management with renewables and storage systems using a modified weighted mean of vectors algorithm |
| topic | Smart home Weighted mean of vectors algorithm Cost Peak to average ratio Demand side response |
| url | https://doi.org/10.1038/s41598-024-79782-5 |
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