Self-supervised inference of missing energy sources for enhancing energy consumption forecasting

Key Points

• Enhanced energy forecasting efficiencies arise from self-supervised inference of missing energy sources.
• An improvement of 25% in prediction accuracy was achieved compared to traditional forecasting methods.
• This analysis utilized historical energy consumption data to train self-supervised models and identify gaps in energy source data.
• These findings highlight the potential for improved energy management practices using advanced inference techniques.