Evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods
As Photovoltaic (PV) energy is impacted by various weather variables such as solar radiation and temperature, one of the key challenges facing solar energy forecasting is choosing the right inputs to achieve the most accurate prediction. Weather datasets, past power data sets, or both sets can be ut...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/3ae393c6d1f944e5874485ee9ad4f97d |
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| Sumario: | As Photovoltaic (PV) energy is impacted by various weather variables such as solar radiation and temperature, one of the key challenges facing solar energy forecasting is choosing the right inputs to achieve the most accurate prediction. Weather datasets, past power data sets, or both sets can be utilized to build different forecasting models. However, operators of grid-connected PV farms do not always have full sets of data available to them especially over an extended period of time as required by key techniques such as multiple regression (MR) or artificial neural network (ANN). Therefore, the research reported here considered these two main approaches of building prediction models and compared their performance when utilizing structural, time-series, and hybrid methods for data input. Three years of PV power generation data (of an actual farm) as well as historical weather data (of the same location) with several key variables were collected and utilized to build and test six prediction models. Models were built and designed to forecast the PV power for a 24-hour ahead horizon with 15 min resolutions. Results of comparative performance analysis show that different models have different prediction accuracy depending on the input method used to build the model: ANN models perform better than the MR regardless of the input method used. The hybrid input method results in better prediction accuracy for both MR and ANN techniques, while using the time-series method results in the least accurate forecasting models. Furthermore, sensitivity analysis shows that poor data quality does impact forecasting accuracy negatively especially for the structural approach. |
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