Multi Model Recursion for Hungarian Electricity Load Forecasting

Abstract

Time series analysis and prediction is a difficult and complex problem. Many machine- and deep-learning methods exist with better and better results. This paper proposes a strategy called Multi Model Recursion. It uses separate deep-learning models per feature that needs predicting. Another improvement is not predicting features which are easily calculated. Having extra models per feature helps in "simulating" a future environment since it predicts external variables otherwise unknown.

The Multi Model Recursion developed is an improvement of the commonly used Recursive strategy. The paper compares this method with models and strategies frequently used in the field. The testing dataset is put together from publicly available Hungarian electricity load and weather data. The task was to predict the country's net electricity load for the next 3 hours.