Monthly Electricity Consumption Forecasting: A Step-Reduction Strategy and Autoencoder Neural Network

Accurate monthly electricity consumption forecasting (ECF) can help retailers enhance the profitability in deregulated electricity markets. Most current methods use monthly load data to perform monthly ECF, which usually produces large errors due to insufficient training samples. A few methods try to use fine-grained smart-meter data (e.g., hourly data) to increase training samples. However, such methods still exhibit low accuracy due to the increase in forecasting steps.