Multinodes Interval Electric Vehicle Charging Load Forecasting Based on Joint Adversarial Generation

The spatial–temporal distribution of electric vehicle (EV) charging load has strong randomness and is affected by battery capacity and user behavior. In addition, the multinode charging load in the distribution network has differential correlations. To effectively forecast the spatial–temporal distribution of EV charging load, a multinode charging load joint adversarial generation interval-forecasting method considering the spatial correlation of the charging load between nodes is proposed. First, the multinode joint charging scenario is constructed. Under the spatial charging load matrix, the spatial–temporal correlation between multinode charging loads in the joint charging scenario of the forecast day and the historical day is analyzed. According to the strong-correlation historical-day multinode joint charging scenario of the forecasting day, the original multinode multiple-correlation-day joint charging scenario set, describing the charging behavior of multinode EVs, is determined. Second, a Wasserstein generative adversarial network with a gradient penalty is used to characterize the strong randomness of the spatial–temporal distribution of the charging load. A large number of joint charging scenarios with similar probability distributions but different timing distributions from the original scenario set are generated to obtain the potential spatial–temporal distribution of the multinode joint charging load. Then, based on the weighted two-dimensional correlation coefficient, the strong-correlation joint scenario set on the day to be forecast is selected from the generated multinode multiple-correlation-day joint charging scenario set. Finally, according to the strong-correlation joint scenario set on the day to be forecast, the interval-forecasting conclusion of the multinode EV charging load is obtained. A comparative experiment demonstrated that the proposed method has more-refined intervals and higher coverage than state-of-the-art interval forecasting models