Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models

Paraskevas Koukaras, Akeem Mustapha, Aristeidis Mystakidis and Christos Tjortjis ()
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Paraskevas Koukaras: School of Science and Technology, International Hellenic University, 57001 Thessaloniki, Greece
Akeem Mustapha: School of Science and Technology, International Hellenic University, 57001 Thessaloniki, Greece
Aristeidis Mystakidis: School of Science and Technology, International Hellenic University, 57001 Thessaloniki, Greece
Christos Tjortjis: School of Science and Technology, International Hellenic University, 57001 Thessaloniki, Greece

Energies, 2024, vol. 17, issue 6, 1-26

Abstract: The building sector, known for its high energy consumption, needs to reduce its energy use due to rising greenhouse gas emissions. To attain this goal, a projection for domestic energy usage is needed. This work optimizes short-term load forecasting (STLF) in the building sector while considering several variables (energy consumption/generation, weather information, etc.) that impact energy use. It performs a comparative analysis of various machine learning (ML) models based on different data resolutions and time steps ahead (15 min, 30 min, and 1 h with 4-step-, 2-step-, and 1-step-ahead, respectively) to identify the most accurate prediction method. Performance assessment showed that models like histogram gradient-boosting regression (HGBR), light gradient-boosting machine regression (LGBMR), extra trees regression (ETR), ridge regression (RR), Bayesian ridge regression (BRR), and categorical boosting regression (CBR) outperformed others, each for a specific resolution. Model performance was reported using R 2 , root mean square error (RMSE), coefficient of variation of RMSE (CVRMSE), normalized RMSE (NRMSE), mean absolute error (MAE), and execution time. The best overall model performance indicated that the resampled 1 h 1-step-ahead prediction was more accurate than the 15 min 4-step-ahead and the 30 min 2-step-ahead predictions. Findings reveal that data preparation is vital for the accuracy of prediction models and should be model-adjusted.

Keywords: time series forecasting; energy load forecasting; machine learning; prediction model; smart building (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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