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Data on photovoltaic power forecasting models for Mediterranean climate
M Malvoni1, M G De Giorgi1, P M Congedo1
1Department of Engineering for Innovation, University of Salento, via per Arnesano I-73100, Italy.
Data in Brief
|May 26, 2016
Summary
Accurate photovoltaic (PV) power forecasting relies on weather data. This study found that Least Square Support Vector Machines with Wavelet Decomposition (WD) offer superior performance for PV power prediction compared to Artificial Neural Networks.
Area of Science:
- Renewable Energy Systems
- Artificial Intelligence in Energy
- Climate Data Analysis
Background:
- Photovoltaic (PV) power forecasting is crucial for grid integration and energy management.
- Weather parameters like temperature and solar radiation significantly influence PV power generation.
- Accurate historical PV output data is essential for developing effective prediction models.
Purpose of the Study:
- To evaluate the performance of different machine learning models for PV power forecasting.
- To compare Artificial Neural Networks (ANN) with Least Square Support Vector Machines (LS-SVM) for short-term PV power prediction.
- To investigate the effectiveness of Wavelet Decomposition (WD) when integrated with LS-SVM for enhanced PV power forecasting.
Main Methods:
- Utilized hourly meteorological data (ambient temperature, module temperature, solar radiation) and PV output power from a 960kWP system in Southern Italy over 500 days.
- Implemented and compared Artificial Neural Networks (ANN) and Least Square Support Vector Machines (LS-SVM) for PV power prediction.
- Applied Wavelet Decomposition (WD) in conjunction with LS-SVM to analyze and forecast PV power output.
Main Results:
- Least Square Support Vector Machines combined with Wavelet Decomposition (LS-SVM-WD) demonstrated superior accuracy in PV power forecasting compared to the Artificial Neural Networks (ANN) method.
- The study provided insights into the comparative performance of different forecasting strategies, including hybrid models.
- LS-SVM-WD proved effective in capturing complex patterns in PV power generation influenced by weather variations.
Conclusions:
- LS-SVM with Wavelet Decomposition is a highly effective method for accurate photovoltaic power forecasting in Mediterranean climates.
- The findings support the use of advanced machine learning techniques for optimizing renewable energy integration.
- Accurate PV power prediction is vital for grid stability and efficient energy resource management.
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