Multiple-Load Forecasting for Integrated Energy System Based on Copula-DBiLSTM

With the tight coupling of multi-energy systems, accurate multiple-load forecasting will be primary premise for optimal operation integrated energy systems. Therefore, this paper proposes a Copula correlation analysis combined with deep bidirectional long and short-term memory neural network model. First, is used to conduct on multiple loads various influencing factors. The factors that have great were screened out as input feature set model eliminate influence interfering Then, was constructed. Combined by method, useful information contained in historical data more comprehensively learned from forward backward directions training forecasting. Through actual calculation example comparison other models, accuracy method presented improved certain extent.