Solar photovoltaic generation and electrical demand forecasting using multi-objective deep learning model for smart grid systems
AbstractThe growing of the photovoltaic (PV) panel’s installation in the world and the intermittent nature of the climate conditions highlights the importance of power forecasting for smart grid integration. This work aims to study and implement existing Deep Learning (DL) methods used for PV power...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Cogent Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2024.2340302 |
| _version_ | 1797200417698873344 |
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| author | Camille Franklin Mbey Vinny Junior Foba Kakeu Alexandre Teplaira Boum Felix Ghislain Yem Souhe |
| author_facet | Camille Franklin Mbey Vinny Junior Foba Kakeu Alexandre Teplaira Boum Felix Ghislain Yem Souhe |
| author_sort | Camille Franklin Mbey |
| collection | DOAJ |
| description | AbstractThe growing of the photovoltaic (PV) panel’s installation in the world and the intermittent nature of the climate conditions highlights the importance of power forecasting for smart grid integration. This work aims to study and implement existing Deep Learning (DL) methods used for PV power and electrical load forecasting. We then developed a novel hybrid model made of Feed-Forward Neural Network (FFNN), Long Short Term Memory (LSTM) and Multi-Objective Particle Swarm Optimization (MOPSO). In this work, electrical load forecasting is long-term and will consider smart meter data, socio-economic and demographic data. PV power generation forecasting is long-term by considering climatic data such as solar irradiance, temperature and humidity. Moreover, we implemented these deep learning methods on two datasets, the first one is made of electrical consumption data collected from smart meters installed at consumers in Douala. The second one is made of climate data collected at the climate management center in Douala. The performances of the models are evaluated using different error metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) and regression (R). The proposed hybrid model gives a RMSE, MAE and R of 1.15, 0.75 and 0.999 respectively. The results obtained show that the novel deep learning model is effective in the both electrical load prediction and PV power forecasting and outperforms other models such as FFNN, Recurrent Neural Network (RNN), Decision Tree (DT), Gated Recurrent Unit (GRU) and eXtreme Gradient Boosting (XGBoost). |
| first_indexed | 2024-04-24T07:31:19Z |
| format | Article |
| id | doaj.art-44da1ba545eb4996995e9d03ff537523 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-04-24T07:31:19Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-44da1ba545eb4996995e9d03ff5375232024-04-20T13:16:31ZengTaylor & Francis GroupCogent Engineering2331-19162024-12-0111110.1080/23311916.2024.2340302Solar photovoltaic generation and electrical demand forecasting using multi-objective deep learning model for smart grid systemsCamille Franklin Mbey0Vinny Junior Foba Kakeu1Alexandre Teplaira Boum2Felix Ghislain Yem Souhe3Department of Electrical Engineering, ENSET, University of Douala, CameroonDepartment of Electrical Engineering, ENSET, University of Douala, CameroonDepartment of Electrical Engineering, ENSET, University of Douala, CameroonDepartment of Electrical Engineering, ENSET, University of Douala, CameroonAbstractThe growing of the photovoltaic (PV) panel’s installation in the world and the intermittent nature of the climate conditions highlights the importance of power forecasting for smart grid integration. This work aims to study and implement existing Deep Learning (DL) methods used for PV power and electrical load forecasting. We then developed a novel hybrid model made of Feed-Forward Neural Network (FFNN), Long Short Term Memory (LSTM) and Multi-Objective Particle Swarm Optimization (MOPSO). In this work, electrical load forecasting is long-term and will consider smart meter data, socio-economic and demographic data. PV power generation forecasting is long-term by considering climatic data such as solar irradiance, temperature and humidity. Moreover, we implemented these deep learning methods on two datasets, the first one is made of electrical consumption data collected from smart meters installed at consumers in Douala. The second one is made of climate data collected at the climate management center in Douala. The performances of the models are evaluated using different error metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) and regression (R). The proposed hybrid model gives a RMSE, MAE and R of 1.15, 0.75 and 0.999 respectively. The results obtained show that the novel deep learning model is effective in the both electrical load prediction and PV power forecasting and outperforms other models such as FFNN, Recurrent Neural Network (RNN), Decision Tree (DT), Gated Recurrent Unit (GRU) and eXtreme Gradient Boosting (XGBoost).https://www.tandfonline.com/doi/10.1080/23311916.2024.2340302Electrical demand predictionphotovoltaic power forecastingdeep learning modelssmart gridlong short term memorymulti-objective particle swarm optimization |
| spellingShingle | Camille Franklin Mbey Vinny Junior Foba Kakeu Alexandre Teplaira Boum Felix Ghislain Yem Souhe Solar photovoltaic generation and electrical demand forecasting using multi-objective deep learning model for smart grid systems Cogent Engineering Electrical demand prediction photovoltaic power forecasting deep learning models smart grid long short term memory multi-objective particle swarm optimization |
| title | Solar photovoltaic generation and electrical demand forecasting using multi-objective deep learning model for smart grid systems |
| title_full | Solar photovoltaic generation and electrical demand forecasting using multi-objective deep learning model for smart grid systems |
| title_fullStr | Solar photovoltaic generation and electrical demand forecasting using multi-objective deep learning model for smart grid systems |
| title_full_unstemmed | Solar photovoltaic generation and electrical demand forecasting using multi-objective deep learning model for smart grid systems |
| title_short | Solar photovoltaic generation and electrical demand forecasting using multi-objective deep learning model for smart grid systems |
| title_sort | solar photovoltaic generation and electrical demand forecasting using multi objective deep learning model for smart grid systems |
| topic | Electrical demand prediction photovoltaic power forecasting deep learning models smart grid long short term memory multi-objective particle swarm optimization |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2024.2340302 |
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