Baseline Load Forecasting for Demand Response Taking Meteorological Factors into Account

Abstract To conduct baseline load forecasting for commercial building users participating in demand response, this paper proposes a response method that takes meteorological factors into account. Using the principal component analysis, indicators contributing more than 80% to are selected as reference data neural network load. The isolated forest algorithm and KNN used identify abnormal fill missing data, with different attributes partially “decoupled.” two parts of decoupled separately forecasting, total is obtained after merging. From these results, it obvious proposed can effectively improve accuracy algorithm.