Prediction of Power Generation of a Photovoltaic Power Plant Based on Neural Networks
Photovoltaic energy production is an important factor for increasing the electricity supply. The ability to predict the electric power production (EPP) of a photovoltaic (PV) farm supports from the management process of the power grid to the trade in the energy market and much more. Also, by predict...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10054046/ |
| _version_ | 1811159115661049856 |
|---|---|
| author | Raluca Nelega Dan Ioan Greu Eusebiu Jecan Vasile Rednic Ciprian Zamfirescu Emanuel Puschita Romulus Valeriu Flaviu Turcu |
| author_facet | Raluca Nelega Dan Ioan Greu Eusebiu Jecan Vasile Rednic Ciprian Zamfirescu Emanuel Puschita Romulus Valeriu Flaviu Turcu |
| author_sort | Raluca Nelega |
| collection | DOAJ |
| description | Photovoltaic energy production is an important factor for increasing the electricity supply. The ability to predict the electric power production (EPP) of a photovoltaic (PV) farm supports from the management process of the power grid to the trade in the energy market and much more. Also, by predicting the production of PV power (PVP), it is possible to monitor the lifetime of the solar cells that form the backbone of any solar PV system. As a critical result, sudden failures of the PV plant can be avoided. Using a long short-term memory recurrent neural network (LSTM-RNN) model, this work evaluates the prediction accuracy of two forecasting strategies: the recursive strategy and the non-recursive Multiple-Input and Multiple-Output, respectively. The dataset consists of 5-years in-filed production data measurements collected from the CETATEA photovoltaic power plant, a research site facility for renewable energies located in Cluj-Napoca, Romania. The high granularity of the electric power production dataset values recorded each 1 hour guarantees the overall prediction accuracy of the system. The impact of the dataset size, the number of previous observations, and the forecast horizon on the neural network prediction accuracy is evaluated for each strategy. The performance metrics used to evaluate the prediction accuracy are the root mean square error, the mean bias error, and the mean average error. The results analysis demonstrates the ability of the implemented machine learning models to predict electric power production, as well as their importance in the energy loss management process. |
| first_indexed | 2024-04-10T05:35:34Z |
| format | Article |
| id | doaj.art-63ace21469124e6ca513619b1c4e6ba6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T05:35:34Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-63ace21469124e6ca513619b1c4e6ba62023-03-07T00:01:01ZengIEEEIEEE Access2169-35362023-01-0111207132072410.1109/ACCESS.2023.324948410054046Prediction of Power Generation of a Photovoltaic Power Plant Based on Neural NetworksRaluca Nelega0Dan Ioan Greu1Eusebiu Jecan2Vasile Rednic3Ciprian Zamfirescu4Emanuel Puschita5https://orcid.org/0000-0002-5635-3011Romulus Valeriu Flaviu Turcu6https://orcid.org/0000-0002-0857-9868Communications Department, Technical University of Cluj-Napoca, Cluj-Napoca, RomaniaCommunications Department, Technical University of Cluj-Napoca, Cluj-Napoca, RomaniaCommunications Department, Technical University of Cluj-Napoca, Cluj-Napoca, RomaniaNational Institute for Research and Development of Isotopic and Molecular Technologies, Cluj-Napoca, RomaniaDepartment of Telecommunications, Politehnica University of Bucharest, Bucharest, RomaniaCommunications Department, Technical University of Cluj-Napoca, Cluj-Napoca, RomaniaNational Institute for Research and Development of Isotopic and Molecular Technologies, Cluj-Napoca, RomaniaPhotovoltaic energy production is an important factor for increasing the electricity supply. The ability to predict the electric power production (EPP) of a photovoltaic (PV) farm supports from the management process of the power grid to the trade in the energy market and much more. Also, by predicting the production of PV power (PVP), it is possible to monitor the lifetime of the solar cells that form the backbone of any solar PV system. As a critical result, sudden failures of the PV plant can be avoided. Using a long short-term memory recurrent neural network (LSTM-RNN) model, this work evaluates the prediction accuracy of two forecasting strategies: the recursive strategy and the non-recursive Multiple-Input and Multiple-Output, respectively. The dataset consists of 5-years in-filed production data measurements collected from the CETATEA photovoltaic power plant, a research site facility for renewable energies located in Cluj-Napoca, Romania. The high granularity of the electric power production dataset values recorded each 1 hour guarantees the overall prediction accuracy of the system. The impact of the dataset size, the number of previous observations, and the forecast horizon on the neural network prediction accuracy is evaluated for each strategy. The performance metrics used to evaluate the prediction accuracy are the root mean square error, the mean bias error, and the mean average error. The results analysis demonstrates the ability of the implemented machine learning models to predict electric power production, as well as their importance in the energy loss management process.https://ieeexplore.ieee.org/document/10054046/Power generation predictionprediction accuracyforecasting horizonPV farmsolar energy |
| spellingShingle | Raluca Nelega Dan Ioan Greu Eusebiu Jecan Vasile Rednic Ciprian Zamfirescu Emanuel Puschita Romulus Valeriu Flaviu Turcu Prediction of Power Generation of a Photovoltaic Power Plant Based on Neural Networks IEEE Access Power generation prediction prediction accuracy forecasting horizon PV farm solar energy |
| title | Prediction of Power Generation of a Photovoltaic Power Plant Based on Neural Networks |
| title_full | Prediction of Power Generation of a Photovoltaic Power Plant Based on Neural Networks |
| title_fullStr | Prediction of Power Generation of a Photovoltaic Power Plant Based on Neural Networks |
| title_full_unstemmed | Prediction of Power Generation of a Photovoltaic Power Plant Based on Neural Networks |
| title_short | Prediction of Power Generation of a Photovoltaic Power Plant Based on Neural Networks |
| title_sort | prediction of power generation of a photovoltaic power plant based on neural networks |
| topic | Power generation prediction prediction accuracy forecasting horizon PV farm solar energy |
| url | https://ieeexplore.ieee.org/document/10054046/ |
| work_keys_str_mv | AT ralucanelega predictionofpowergenerationofaphotovoltaicpowerplantbasedonneuralnetworks AT danioangreu predictionofpowergenerationofaphotovoltaicpowerplantbasedonneuralnetworks AT eusebiujecan predictionofpowergenerationofaphotovoltaicpowerplantbasedonneuralnetworks AT vasilerednic predictionofpowergenerationofaphotovoltaicpowerplantbasedonneuralnetworks AT ciprianzamfirescu predictionofpowergenerationofaphotovoltaicpowerplantbasedonneuralnetworks AT emanuelpuschita predictionofpowergenerationofaphotovoltaicpowerplantbasedonneuralnetworks AT romulusvaleriuflaviuturcu predictionofpowergenerationofaphotovoltaicpowerplantbasedonneuralnetworks |