Flexible Load Multi-Step Forecasting Method Based on Non-Intrusive Load Decomposition

Abstract

Accurate forecasting of flexible loads can capture the potential of their application and improve the adjustable space of the distribution network. Flexible load data, such as air conditioning (AC) and electric vehicles (EV), are generally included in the total load data, making it difficult to forecast them directly. To this end, this paper proposes a multi-step flexible load prediction model based on the non-intrusive load decomposition technique and Informer algorithm. The CNN-BiLSTM model is first used to decompose the flexible load from the total load via feature extraction and feature mapping of the flexible load to the overall load. The Informer model is then used to predict the flexible load and the residual load separately in multiple steps, and the prediction results are summed to obtain the overall prediction results. In this paper, the model is validated using two datasets, where in dataset 1, the prediction coefficients of determination for flexible load air conditioning and electric vehicles are 0.9329 and 0.9892. The predicted value of the total load is obtained by adding the flexible load to the residual load. At a prediction step of 1, the total load prediction coefficient of determination is 0.9813, which improves the prediction coefficient of determination by 0.0069 compared to the direct prediction of the total load, and prediction decision coefficient improves by 0.067 at 20 predicted steps. When applied to data set 2, the prediction coefficient of determination for flexible load air conditioning is 0.9646.