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.

Climate Change and its Effect on the Energy Production from Renewable Sources – A Case Study in Mediterranean Region

“…∑ root mean square error (9) , in sample naive MAE MASE MAE − = mean absolute scaled error (10) Many arguments and discussions of using the appropriate accuracy measurements of the model are presented in the literature. On the basis of these discussions and the nature and complexity of the data in reference of (Hyndman & Koehler, 2006) MASE offers a straightforward indication on the relative model performance compared with the naïve benchmark.…”

“…and comparing the performance of the models they agreed for the data that neural networks provide good forecasts for monthly energy produced by hydropower. Gjika and Basha (2022), classical time series and deep learning models for energy load prediction. They evaluated the impact of climacteric factors on energy load using hourly and daily time series for a period of three years.…”