A hybrid model based on the photovoltaic conversion model and artificial neural network model for short-term photovoltaic power forecasting
Photovoltaic (PV) power is greatly uncertain due to the random meteorological parameters. Therefore, accurate PV power forecasting results are significant for the dispatching of power and improving of system stability. This paper proposes a hybrid forecasting model for one-day-ahead PV power forecas...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1446422/full |
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| Summary: | Photovoltaic (PV) power is greatly uncertain due to the random meteorological parameters. Therefore, accurate PV power forecasting results are significant for the dispatching of power and improving of system stability. This paper proposes a hybrid forecasting model for one-day-ahead PV power forecasting under different cloud amount conditions. The proposed model consists of an improved artificial neural network (ANN) algorithm and a PV power conversion model. First, the ANN model is designed to forecast the plane of array (POA) irradiance and ambient temperature. Backpropagation, gradient descent, and L2 regularization methods are applied in the structure of the ANN model to achieve the best weights, improve the prediction accuracy, and alleviate the effect of overfitting. Second, the PV power conversion model employs the forecasted results of POA irradiance and ambient temperature to determine the PV power produced by a PV module. In addition to the basic temperature factor, environmental efficiency and a reflection efficiency are incorporated into the conversion model to account for real PV module losses. The performance of the proposed model is validated with real weather and PV power data from Alice Springs and Climate Data Store. Results indicate that the model improves the forecast accuracy compared to four benchmark models. Specifically, it reduces root mean square error (RMSE) and normalized RMSE (nRMSE) by up to 25% under cloudy conditions and offers a 3% shorter training time compared to extreme gradient boosting. |
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| ISSN: | 2296-598X |