Improving Electric Load Demand Forecasting with Anchor-Based Forecasting Method

In this paper we deal with the problem of Electric Load Demand Forecasting (ELDF) considering the Greek Energy Market. Motivated by the anchored-based object detection methods, we argue that considering the ELDF task we can define an anchor and transform the problem into predicting the offset instead of predicting the actual load values. The experimental evaluation considering the one-day-ahead forecasting task, validated the effectiveness of the proposed Anchor-based FOREcasting (AFORE) method. The AFORE method achieved significant improvements in terms of mean absolute percentage error under various setups, using different loss functions and model architectures.