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Abstract
Accurate solar generation prediction is of great significance for grid dispatching and operation of photovoltaic power plants. In this paper, we propose a novel solar generation forecasting method based on cluster analysis and ensemble model. Two common ways to improve prediction accuracy are adopted. We first conduct cluster analysis based on solar generation to obtain a weather regime, which improves the computational efficiency and avoids the difficulty in selecting weather variables to participate in the clustering process. Then random forests with different parameters is established for different weather regimes, which is used as component models in the followed ensemble model. Finally, we weighted the predictions from different weather regimes to get the final results. To avoid manual design weights, ridge regression is used to calculate weights for each weather regime automatically. A large number of experiments have been carried out on freely available data sets to verify the performance of the proposed method. The experimental results show that our method predicts solar generation more accurately, which has broad prospects in practical application.
Highlights
Solar energy, as one of the cleanest and the most promising renewable energy, has enormous potential for development and application
We propose a novel solar generation forecasting method based on cluster analysis and ensemble model, which aims to integrate the predictions for different weather regimes
Since all solar power measurements are normalized by the nominal capacity of the corresponded solar power plant, four metrics, namely, normalized mean bias error, normalized mean absolute error, normalized root mean square error and forecast skill [38] are used as evaluation metrics in this paper to assess the performance of the proposed forecasting method
Summary
As one of the cleanest and the most promising renewable energy, has enormous potential for development and application. Tan: Day-Ahead Hourly Forecasting of Solar Generation Based on Cluster Analysis and Ensemble Model inputs to KFCM. Some methods classified weather regimes based on solar generation [22], [23], but these methods do not take into account the fact that there may be some cases where the sample cannot be accurately classified into a certain weather regime, resulting in lower prediction accuracy. We propose a novel solar generation forecasting method based on cluster analysis and ensemble model, which aims to integrate the predictions for different weather regimes. The main contributions of this work are summarized as follows: 1) Cluster analysis based on actual solar generation is adopted to classify weather regimes (similar day selection), which avoids the difficulty of selecting meteorological factors for clustering and reduces the amount of calculation.
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