Advances in New and Renewable Energy >
Short-term Forecasting of Photovoltaic Power Generation Based on Wavelet Decomposition and Support Vector Machine
Received date: 2014-08-29
Revised date: 2014-09-21
Online published: 2014-10-30
Photovoltaic power prediction is an effective way to reduce adverse effects caused by the large-scale photovoltaic power connected to grid, and it is of great significance for power grid scheduling and optimal operation of the photovoltaic power station. Considering the cyclical and non-stationary of photovoltaic power sequence, this paper provides a prediction method based on wavelet transform and support vector machine (SVM). By wavelet decomposition and single refactoring, photovoltaic power sequence is converted to the low frequency trend signal and high frequency random signal. In consideration of strong small sample learning ability and small amount of calculation which SVM has, every wavelet signal are separately forecasted with support vector machine models. Finally, the predicted results of original photovoltaic power sequence are achieved by merging every single forecasted value. The actual data simulation validation of a photovoltaic power station shows the feasibility and effectiveness of this prediction method.
LUO Yi , XING Xiao-tao . Short-term Forecasting of Photovoltaic Power Generation Based on Wavelet Decomposition and Support Vector Machine[J]. Advances in New and Renewable Energy, 2014 , 2(5) : 380 -384 . DOI: 10.3969/j.issn.2095-560X.2014.05.009
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