A study on short-term power load probability density forecasting considering wind power effects. (December 2019) - He, Yaoyao; Qin, Yang; Lei, Xiaohui; Feng, Nanping
Highlights: The uncertainty of wind power has a significant impact on power load forecasting. LASSO-QR is presented for power load probability density forecasting considering the wind power. LASSO and GCV methods are adopted to extract the important explanatory variables. Two cases of Canada in summer and winter display the performance of proposed method. Abstract: Short-term load forecasting (STLF) is the foundation of safe and stable operation for power systems. In recent years, large amount of intermittent wind power has integrated into the power system, which significantly increases the uncertainties of power load forecasting. Along with the gradual increase for the proportion of wind power in the power grid, the frequency stability problem attributed to wind power connection attracts increasing attention from various aspects. Fully considering the impact of the wind power factor, a method of probability density forecasting based on Least Absolute Shrinkage and Selection Operator-Quantile Regression (LASSO-QR) is proposed in this paper. Firstly, the significant explanatory variables are screened out from the historical power load and wind power factors based on LASSO algorithm via generalized cross validation (GCV), and the LASSO-QR model is established. Secondly, in combination with kernel density estimation (KDE) method, short-term power load probability density forecasting based on LASSO-QR is implemented utilizing Epanechnikov kernel function. Thirdly, the paper appraises the exactitude of the prediction interval (PI) in accordance with two criteria, prediction interval coverage probability (PICP) and prediction interval normalized average width (PINAW). Two real datasets from Ontario of Canada in summer and winter, are exploited to validate the LASSO-QR method. Fully considering the impact of wind power factor on the power load, experiment results demonstrate that the LASSO-QR method can construct more accurate PI and obtain more precise probability density forecasting results than quantile regression (QR). Contrastive analysis with the existing state-of-the-art methods further verifies superiority of the method proposed, which reduces the nondeterminacy of the prediction process to avoid large prediction errors and economic losses.