Forecasting of Residential Energy Utilisation Based on Regression Machine Learning Schemes

Thapelo Mosetlhe () and Adedayo Ademola Yusuff
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Thapelo Mosetlhe: Department of Electrical and Smart Systems Engineering, University of South Africa, Florida 1710, South Africa
Adedayo Ademola Yusuff: Department of Electrical and Smart Systems Engineering, University of South Africa, Florida 1710, South Africa

Energies, 2024, vol. 17, issue 18, 1-9

Abstract: Energy utilisation in residential dwellings is stochastic and can worsen the issue of operational planning for energy provisioning. Additionally, planning with intermittent energy sources exacerbates the challenges posed by the uncertainties in energy utilisation. In this work, machine learning regression schemes (random forest and decision tree) are used to train a forecasting model. The model is based on a yearly dataset and its subset seasonal partitions. The dataset is first preprocessed to remove inconsistencies and outliers. The performance measures of mean absolute error (MAE), mean square error (MSE) and root mean square error (RMSE) are used to evaluate the accuracy of the model. The results show that the performance of the model can be enhanced with hyperparameter tuning. This is shown with an observed improvement of about 44% in accuracy after tuning the hyperparameters of the decision tree regressor. The results further show that the decision tree model can be more suitable for utilisation in forecasting the partitioned dataset.

Keywords: energy planning; energy forecasting; intermittent energy sources; decision tree regression; hyperparameter tuning (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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