A Comparative Study of Statistical and Deep Learning Models for Energy Load Prediction

Abstract

The objective of this study is to analyze and compare classical time series and deep learning models for energy load prediction. Energy predictions are important for management and sustainable systems. After analyzing the climacteric factors impact on energy load (a case study in Albania) we considered classical and deep learning models to perform forecasts. We have used hourly and daily time series for a period of three years. In total respectively 26,280 hours and 1095 days. Average temperature is considered as external variable in both statistical and deep learning models. The dynamic evolution of hourly (daily) load is correlated with hourly (daily) average temperature. The performance of the proposed models is analyzed and evaluated based on accuracy measurements (MSE, RMSE, MAPE, AIC, BIC etc.) and graphics results of statistical tests. In-sample and out-of-sample accuracy is evaluated. The models show competitive performance to some recent works in the field of short-and medium-term energy load forecasts. This work may be used by stakeholders to optimize their activities and obtain accurate forecasts of energy system behavior.