Energy Management Prediction in Hybrid PV-Battery Systems Using Deep Learning Architecture
On-grid predictive energy management using machine learning is presented in this paper. A photovoltaic array considered in this study is one of the kinds of a renewable sources of energy, where the battery bank acts as a technology for energy storage, in order to optimise energy exchange with the ut...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2022/6844853 |
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| _version_ | 1850160165000577024 |
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| author | Mohamad Reda A. Refaai Shanmukha Naga Raju Vonteddu Prasanthi Kumari Nunna P. Suresh Kumar C. Anbu Mebratu Markos |
| author_facet | Mohamad Reda A. Refaai Shanmukha Naga Raju Vonteddu Prasanthi Kumari Nunna P. Suresh Kumar C. Anbu Mebratu Markos |
| author_sort | Mohamad Reda A. Refaai |
| collection | DOAJ |
| description | On-grid predictive energy management using machine learning is presented in this paper. A photovoltaic array considered in this study is one of the kinds of a renewable sources of energy, where the battery bank acts as a technology for energy storage, in order to optimise energy exchange with the utility grid using logistic regression. The model of prediction can accurately estimate photovoltaic energy output and load one step ahead using a training technique. The optimization problem is constrained by the maximum amount of CO2 produced and the maximum amount of charge stored in a battery bank. The proposed model is tested on dynamic electricity costs. Compared with existing energy systems, the proposed strategy and prediction model can handle more than half of the annual load need. |
| format | Article |
| id | doaj-art-b3e2482ca576447a8650ae75d2bcd018 |
| institution | OA Journals |
| issn | 1687-529X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-b3e2482ca576447a8650ae75d2bcd0182025-08-20T02:23:14ZengWileyInternational Journal of Photoenergy1687-529X2022-01-01202210.1155/2022/6844853Energy Management Prediction in Hybrid PV-Battery Systems Using Deep Learning ArchitectureMohamad Reda A. Refaai0Shanmukha Naga Raju Vonteddu1Prasanthi Kumari Nunna2P. Suresh Kumar3C. Anbu4Mebratu Markos5Department of Mechanical EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Mechanical EngineeringDepartment of Mechatronics EngineeringDepartment of Mechanical EngineeringOn-grid predictive energy management using machine learning is presented in this paper. A photovoltaic array considered in this study is one of the kinds of a renewable sources of energy, where the battery bank acts as a technology for energy storage, in order to optimise energy exchange with the utility grid using logistic regression. The model of prediction can accurately estimate photovoltaic energy output and load one step ahead using a training technique. The optimization problem is constrained by the maximum amount of CO2 produced and the maximum amount of charge stored in a battery bank. The proposed model is tested on dynamic electricity costs. Compared with existing energy systems, the proposed strategy and prediction model can handle more than half of the annual load need.http://dx.doi.org/10.1155/2022/6844853 |
| spellingShingle | Mohamad Reda A. Refaai Shanmukha Naga Raju Vonteddu Prasanthi Kumari Nunna P. Suresh Kumar C. Anbu Mebratu Markos Energy Management Prediction in Hybrid PV-Battery Systems Using Deep Learning Architecture International Journal of Photoenergy |
| title | Energy Management Prediction in Hybrid PV-Battery Systems Using Deep Learning Architecture |
| title_full | Energy Management Prediction in Hybrid PV-Battery Systems Using Deep Learning Architecture |
| title_fullStr | Energy Management Prediction in Hybrid PV-Battery Systems Using Deep Learning Architecture |
| title_full_unstemmed | Energy Management Prediction in Hybrid PV-Battery Systems Using Deep Learning Architecture |
| title_short | Energy Management Prediction in Hybrid PV-Battery Systems Using Deep Learning Architecture |
| title_sort | energy management prediction in hybrid pv battery systems using deep learning architecture |
| url | http://dx.doi.org/10.1155/2022/6844853 |
| work_keys_str_mv | AT mohamadredaarefaai energymanagementpredictioninhybridpvbatterysystemsusingdeeplearningarchitecture AT shanmukhanagarajuvonteddu energymanagementpredictioninhybridpvbatterysystemsusingdeeplearningarchitecture AT prasanthikumarinunna energymanagementpredictioninhybridpvbatterysystemsusingdeeplearningarchitecture AT psureshkumar energymanagementpredictioninhybridpvbatterysystemsusingdeeplearningarchitecture AT canbu energymanagementpredictioninhybridpvbatterysystemsusingdeeplearningarchitecture AT mebratumarkos energymanagementpredictioninhybridpvbatterysystemsusingdeeplearningarchitecture |