Medium and long term probability density forecasting based on Box-Cox transformation quantile regression and load relation factor identification

Medium and long term load forecasting is an important prerequisite for the power sector's development planning and stable operation. According to the multiple factors of influencing the medium and long term power load forecasting accuracy, this paper uses the stepwise regression method to identify the key influencing factors from a number of factors associated load forecasting, and proposes a probability density forecasting method based on the Box-Cox transformation quantile regression combined with kernel density estimation. The probability density forecasting results of load under the different quantiles at any year in the next few years are evaluated. The proposed method is likely to realize the accurate range prediction of future annual electricity consumption. The historical load and socio-economic data of Anhui province are adopted as simulation experiment. The results show that the proposed method not only realizes the medium and long term load forecasting, but also well improves the precision of medium and long-term power load probability density forecasting by means of introducing strong relation factors, and effectively solves medium and long term power load probability density forecasting problem considering multiple factors.