Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks
The solar photovoltaic (PV) power forecast is crucial for steady grid operation, scheduling, and grid electricity management. In this work, numerous time series forecast methodologies, including the statistical and artificial intelligence-based methods, are studied and compared fastidiously to forec...
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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/10.1155/2022/7015818 |
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| author | Sohrab Khan Faheemullah Shaikh Mokhi Maan Siddiqui Tanweer Hussain Laveet Kumar Afroza Nahar |
| author_facet | Sohrab Khan Faheemullah Shaikh Mokhi Maan Siddiqui Tanweer Hussain Laveet Kumar Afroza Nahar |
| author_sort | Sohrab Khan |
| collection | DOAJ |
| description | The solar photovoltaic (PV) power forecast is crucial for steady grid operation, scheduling, and grid electricity management. In this work, numerous time series forecast methodologies, including the statistical and artificial intelligence-based methods, are studied and compared fastidiously to forecast PV electricity. Moreover, the impact of different environmental conditions for all of the algorithms is investigated. Hourly solar PV power forecasting is done to confirm the effectiveness of various models. Data used in this paper is of one entire year and is acquired from a 100 MW solar power plant, namely, Quaid-e-Azam Solar Park, Bahawalpur, Pakistan. This paper suggests recurrent neural networks (RNNs) as the best-performing forecasting model for PV power output. Furthermore, the bidirectional long-short-term memory RNN framework delivered high accuracy results in all weather conditions, especially under cloudy weather conditions where root mean square error (RMSE) was found lowest 0.0025, R square stands at 0.99, and coefficient of variation of root mean square error (RMSE) Cv was observed 0.0095%. |
| format | Article |
| id | doaj-art-9d12b5fc2ec945f784973865dea3f307 |
| institution | Kabale University |
| issn | 1687-529X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-9d12b5fc2ec945f784973865dea3f3072025-02-03T01:30:39ZengWileyInternational Journal of Photoenergy1687-529X2022-01-01202210.1155/2022/7015818Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural NetworksSohrab Khan0Faheemullah Shaikh1Mokhi Maan Siddiqui2Tanweer Hussain3Laveet Kumar4Afroza Nahar5Department of Electrical EngineeringDepartment of Electrical EngineeringDepartment of Electrical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Computer ScienceThe solar photovoltaic (PV) power forecast is crucial for steady grid operation, scheduling, and grid electricity management. In this work, numerous time series forecast methodologies, including the statistical and artificial intelligence-based methods, are studied and compared fastidiously to forecast PV electricity. Moreover, the impact of different environmental conditions for all of the algorithms is investigated. Hourly solar PV power forecasting is done to confirm the effectiveness of various models. Data used in this paper is of one entire year and is acquired from a 100 MW solar power plant, namely, Quaid-e-Azam Solar Park, Bahawalpur, Pakistan. This paper suggests recurrent neural networks (RNNs) as the best-performing forecasting model for PV power output. Furthermore, the bidirectional long-short-term memory RNN framework delivered high accuracy results in all weather conditions, especially under cloudy weather conditions where root mean square error (RMSE) was found lowest 0.0025, R square stands at 0.99, and coefficient of variation of root mean square error (RMSE) Cv was observed 0.0095%.http://dx.doi.org/10.1155/2022/7015818 |
| spellingShingle | Sohrab Khan Faheemullah Shaikh Mokhi Maan Siddiqui Tanweer Hussain Laveet Kumar Afroza Nahar Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks International Journal of Photoenergy |
| title | Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks |
| title_full | Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks |
| title_fullStr | Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks |
| title_full_unstemmed | Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks |
| title_short | Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks |
| title_sort | hourly forecasting of solar photovoltaic power in pakistan using recurrent neural networks |
| url | http://dx.doi.org/10.1155/2022/7015818 |
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