A Simple Computational Approach to Predict Long-Term Hourly Electric Consumption

By exploiting the patterns in past data points, we could forecast long-term consumption with a computationally simple algorithm. Our approach is simple to interpret. It incorporates the seasonality of past consumption and can predict power consumption for any time scale. The algorithm can be easily implemented directly in SQL. It can run sub-second long-term predictions on large-scale data marts. The proposed method scored a Mean Absolute Percentage Error (MAPE) of just 5.88% when predicting hourly values for France's electric consumption in 2017 based on hourly data from 2008 to 2011.