Power output forecasting of solar photovoltaic plant using LSTM
Renewable energy sources are gaining popularity, where solar photovolaics (PV) being the most preferred option due to its cleanliness, affordability, and abundance. The energy output of solar PV is primarily based on temperature & irradiance. Therefore, a weather-based intelligent model is n...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Green Energy and Intelligent Transportation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S277315372300049X |
| _version_ | 1797582920197603328 |
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| author | Dheeraj Kumar Dhaked Sharad Dadhich Dinesh Birla |
| author_facet | Dheeraj Kumar Dhaked Sharad Dadhich Dinesh Birla |
| author_sort | Dheeraj Kumar Dhaked |
| collection | DOAJ |
| description | Renewable energy sources are gaining popularity, where solar photovolaics (PV) being the most preferred option due to its cleanliness, affordability, and abundance. The energy output of solar PV is primarily based on temperature & irradiance. Therefore, a weather-based intelligent model is needed for estimating solar energy output to fulfil energy demand and decision making. Predicting PV power output is essential for energy management, security, and operation. In addition to enhancing the output efficiency of PV power plants, the power grid's stability can be enhanced by enhancing the efficacy of PV power plants' electricity generation. This work focuses on LSTM and BPNN for forecasting solar plant power output and it is observed that their findings are virtually compatible with realistic power production in terms of MAE, MAPE, RMSPE, and R2 score. LSTM model comparisons with different layers for each weather season are also analysed. Comparing the extent of errors in the LSTM and BPNN models reveals that LSTM provides more accurate predictions. |
| first_indexed | 2024-03-10T23:29:25Z |
| format | Article |
| id | doaj.art-1bcfe3dfadc54a58a1278a816d9834dc |
| institution | Directory Open Access Journal |
| issn | 2773-1537 |
| language | English |
| last_indexed | 2024-03-10T23:29:25Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Green Energy and Intelligent Transportation |
| spelling | doaj.art-1bcfe3dfadc54a58a1278a816d9834dc2023-11-19T04:35:48ZengElsevierGreen Energy and Intelligent Transportation2773-15372023-10-0125100113Power output forecasting of solar photovoltaic plant using LSTMDheeraj Kumar Dhaked0Sharad Dadhich1Dinesh Birla2Indian Institute of Technology Guwahati, Assam, India; Corresponding author.Indian Institute of Technology Guwahati, Assam, IndiaRajasthan Technical University, Kota, IndiaRenewable energy sources are gaining popularity, where solar photovolaics (PV) being the most preferred option due to its cleanliness, affordability, and abundance. The energy output of solar PV is primarily based on temperature & irradiance. Therefore, a weather-based intelligent model is needed for estimating solar energy output to fulfil energy demand and decision making. Predicting PV power output is essential for energy management, security, and operation. In addition to enhancing the output efficiency of PV power plants, the power grid's stability can be enhanced by enhancing the efficacy of PV power plants' electricity generation. This work focuses on LSTM and BPNN for forecasting solar plant power output and it is observed that their findings are virtually compatible with realistic power production in terms of MAE, MAPE, RMSPE, and R2 score. LSTM model comparisons with different layers for each weather season are also analysed. Comparing the extent of errors in the LSTM and BPNN models reveals that LSTM provides more accurate predictions.http://www.sciencedirect.com/science/article/pii/S277315372300049XSolar PVMachine learningRNNLSTMMean absolute error (MAE) |
| spellingShingle | Dheeraj Kumar Dhaked Sharad Dadhich Dinesh Birla Power output forecasting of solar photovoltaic plant using LSTM Green Energy and Intelligent Transportation Solar PV Machine learning RNN LSTM Mean absolute error (MAE) |
| title | Power output forecasting of solar photovoltaic plant using LSTM |
| title_full | Power output forecasting of solar photovoltaic plant using LSTM |
| title_fullStr | Power output forecasting of solar photovoltaic plant using LSTM |
| title_full_unstemmed | Power output forecasting of solar photovoltaic plant using LSTM |
| title_short | Power output forecasting of solar photovoltaic plant using LSTM |
| title_sort | power output forecasting of solar photovoltaic plant using lstm |
| topic | Solar PV Machine learning RNN LSTM Mean absolute error (MAE) |
| url | http://www.sciencedirect.com/science/article/pii/S277315372300049X |
| work_keys_str_mv | AT dheerajkumardhaked poweroutputforecastingofsolarphotovoltaicplantusinglstm AT sharaddadhich poweroutputforecastingofsolarphotovoltaicplantusinglstm AT dineshbirla poweroutputforecastingofsolarphotovoltaicplantusinglstm |