Studying Electricity Load Forecasting and Optimizing User Benefits with Smart Metering

Abstract

Accurate energy projections and optimal utilization of resources require the consideration of real-time variations in demand-side response components. Innovative ultra-short-term power load forecasting approaches such as CNN-BiLSTM-Attention, CNN-LSTM, and GRU models are used to assess the load level and predict daily raw load curve. The study shows that by incorporating predicted raw loads and two types of customer reactions influenced by average reduction rate under different energy efficient classes, wholesale market price fluctuations can be minimized through retail-to-wholesale market connection using demand-side responses. This helps diminish both frequency and amplitude of sudden changes in prices for wholesalers while taking into account an average overall usage pattern based on user class resource consumption rates.