Hourly Photovoltaic Production Prediction Using Numerical Weather Data and Neural Networks for Solar Energy Decision Support
The day-ahead photovoltaic electricity forecast is increasingly necessary for grid operators and for energy communities. In the present work, the hourly PV production is estimated using two models based on feedforward neural networks (FFNNs). Most existing models use solar radiation as an input. Ins...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/2/466 |
| _version_ | 1797344128302841856 |
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| author | Francesco Nicoletti Piero Bevilacqua |
| author_facet | Francesco Nicoletti Piero Bevilacqua |
| author_sort | Francesco Nicoletti |
| collection | DOAJ |
| description | The day-ahead photovoltaic electricity forecast is increasingly necessary for grid operators and for energy communities. In the present work, the hourly PV production is estimated using two models based on feedforward neural networks (FFNNs). Most existing models use solar radiation as an input. Instead, the models proposed here use numerical weather prediction (NWP) data: ambient temperature, relative humidity, and wind speed, which are easily accessible to anyone. The first proposed model uses multiple inputs, while the second one uses only the necessary information. A sensitivity analysis allows for the identification of the variables that are most influential on the estimation accuracy. This study concludes that the hourly temperature trend is the most important variable for prediction. The models’ accuracy was tested using experimental and NWP data, with the second model having almost the same accuracy as the first despite using fewer input data. The results obtained using experimental data as inputs show a coefficient of determination (R<sup>2</sup>) of 0.95 for the hourly PV energy produced. The RMSE is about 6.4% of the panel peak power. When NWP data are used as inputs, R<sup>2</sup> is 0.879 and the RMSE is 10.5%. These models can have a significant impact by enabling individual energy communities to make their forecasts, resulting in energy savings and increased self-consumed energy. |
| first_indexed | 2024-03-08T10:57:52Z |
| format | Article |
| id | doaj.art-12c2aedd1f7e4d6191b7b9105b30b287 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-08T10:57:52Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-12c2aedd1f7e4d6191b7b9105b30b2872024-01-26T16:20:17ZengMDPI AGEnergies1996-10732024-01-0117246610.3390/en17020466Hourly Photovoltaic Production Prediction Using Numerical Weather Data and Neural Networks for Solar Energy Decision SupportFrancesco Nicoletti0Piero Bevilacqua1Department of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria, Via P. Bucci, 87036 Rende, ItalyDepartment of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria, Via P. Bucci, 87036 Rende, ItalyThe day-ahead photovoltaic electricity forecast is increasingly necessary for grid operators and for energy communities. In the present work, the hourly PV production is estimated using two models based on feedforward neural networks (FFNNs). Most existing models use solar radiation as an input. Instead, the models proposed here use numerical weather prediction (NWP) data: ambient temperature, relative humidity, and wind speed, which are easily accessible to anyone. The first proposed model uses multiple inputs, while the second one uses only the necessary information. A sensitivity analysis allows for the identification of the variables that are most influential on the estimation accuracy. This study concludes that the hourly temperature trend is the most important variable for prediction. The models’ accuracy was tested using experimental and NWP data, with the second model having almost the same accuracy as the first despite using fewer input data. The results obtained using experimental data as inputs show a coefficient of determination (R<sup>2</sup>) of 0.95 for the hourly PV energy produced. The RMSE is about 6.4% of the panel peak power. When NWP data are used as inputs, R<sup>2</sup> is 0.879 and the RMSE is 10.5%. These models can have a significant impact by enabling individual energy communities to make their forecasts, resulting in energy savings and increased self-consumed energy.https://www.mdpi.com/1996-1073/17/2/466PV forecastartificial neural networkphotovoltaicweather forecast |
| spellingShingle | Francesco Nicoletti Piero Bevilacqua Hourly Photovoltaic Production Prediction Using Numerical Weather Data and Neural Networks for Solar Energy Decision Support Energies PV forecast artificial neural network photovoltaic weather forecast |
| title | Hourly Photovoltaic Production Prediction Using Numerical Weather Data and Neural Networks for Solar Energy Decision Support |
| title_full | Hourly Photovoltaic Production Prediction Using Numerical Weather Data and Neural Networks for Solar Energy Decision Support |
| title_fullStr | Hourly Photovoltaic Production Prediction Using Numerical Weather Data and Neural Networks for Solar Energy Decision Support |
| title_full_unstemmed | Hourly Photovoltaic Production Prediction Using Numerical Weather Data and Neural Networks for Solar Energy Decision Support |
| title_short | Hourly Photovoltaic Production Prediction Using Numerical Weather Data and Neural Networks for Solar Energy Decision Support |
| title_sort | hourly photovoltaic production prediction using numerical weather data and neural networks for solar energy decision support |
| topic | PV forecast artificial neural network photovoltaic weather forecast |
| url | https://www.mdpi.com/1996-1073/17/2/466 |
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