Abstract
Accurate wind power generation prediction, which has positive implications for making full use of wind energy, seems still a critical issue and a huge challenge. In this paper, a novel hybrid approach has been proposed for wind power generation forecasting in the light of Cloud-Based Evolutionary Algorithm (CBEA) and Least Squares Support Vector Machine (LSSVM). In order to improve the forecasting precision, a two-way comparison approach is conducted to preprocess the original wind power generation data. The pertinent parameters of LSSVM are optimized by using CBEA to verify the learning and generalization abilities of the LSSVM model. The experimental results indicate that the forecasting performance of the proposed model is better than the single LSSVM model and all of the other models for comparison. Moreover, the paired-sample t-test is employed to cast light on the applicability of the developed model.
Highlights
The utilization of wind energy for electric power systems offers an alternative to decrease the dependence on fuel-based energy, effectively alleviating the environmental pressure [1]
Using historical data and numerical weather predictions, a comparative study of wind power generation prediction was conducted, and the results suggested that the hybrid approach based on wavelet decomposition with Least Squares Support Vector Machine (LSSVM) clearly was superior to the hybrid Artificial
Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the coefficient of determination (R2 ) to measure the precision of the models involved in this paper
Summary
The utilization of wind energy for electric power systems offers an alternative to decrease the dependence on fuel-based energy, effectively alleviating the environmental pressure [1]. Enhancing the reliability of wind power generation, which is often affected adversely by the variability of wind speed, temperature and other factors, can be one of the major challenges [3]. High precision in wind power generation prediction allows for better planning for unit commitment and economic dispatch [5], the security of the running systems [6], and other relevant procedures. In the past few years, there has been a large body of research on wind power prediction algorithms. These studies include, but are not limited to, wind speed prediction, generated energy and generated power forecasting. The physical approaches are mainly utilized for long-term forecasting [9]. The statistical approaches, which are time series-based methods, apply mathematical and statistical models, including Vector Autoregressive (VAR) models [10], Autoregressive Moving Average (ARMA) models, Autoregressive Integrated
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