Comparative Study of Machine Learning and Deep Learning Models for Short-Term Energy Consumption Prediction

Alice Treesa M and Dr. Arpita Choudhary ()
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Dr. Arpita Choudhary: Assistant Professor, Madras School of Economics, Chennai, India.

Working Papers from Madras School of Economics,Chennai,India

Abstract: Reliable energy consumption forecasting in the short term is essential for improving building operations efficiency and creating sustainable energy consumption plans. The authors of this study evaluate the forecasting performance of machine learning and deep learning methods which use climate data and time data to predict energy usage at hourly intervals. The study used Linear Regression, Decision Trees, Random Forest, XGBoost and Long Short-Term Memory as comparison methods to assess performance in the same context. The study demonstrated that energy consumption forecasting accuracy depends more on selected features than on the model's complexity. The study found that LSTM model learning capacity remained stable while Random Forest model performance showed superior results in dealing with non-linear features that had temporal attributes.

Keywords: Energy Consumption Prediction; Machine Learning; Ensemble Models; LSTM Model; Feature Engineering; Sustainable Energy Managementsemantics; Neural architectures (search for similar items in EconPapers)
JEL-codes: C38 C45 C53 L94 Q41 Q47 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2026-05
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