SAWing on short term load forecasting errors: Increasing the accuracy with self adaptive weighting

Accurate electrical load forecasts are of vital interest to power companies. Short term load forecasts for next hours in particular are important for power dispatch, power trading and system operation. This paper analyzes the conjectures that a self-adaptive weighting algorithm (SAW), blending different standard load forecasting approaches, such as a dynamic standard load profile model, a linear regression model and an artificial neuronal network model, can increase forecasting performance on micro grids for one hour intraday to 24 hours day ahead forecasts. The SAW methodology and forecasting models are applied to a publicly available smart meter data set. Common evaluation metrics such as the mean average percentage error (MAPE) and the normalized rooted mean square error (NRMSE) are used to evaluate the performance of this new hybrid approach and allow a comparison to other studies. Self-adaptive weighing leads to a significant improvement of intraday and day ahead forecasts from 50%-54% and 30%-35% (MAPE improvement for 1h and 24h compared to input forecasts). The resulting intraday and day ahead load SAW forecasts range from 3.19% 1h MAPE to 4.50% 24h MAPE in this case study.