Abstract
Due to the disparity between energy supply and demand, forecasting power consumption plays a crucial role in the global economy, especially during current turbulent times. Energy managers can enhance their power management and bring in energy efficiency with the use of machine learning models, which have gained widespread recognition for their accurate prediction. The author found random forest as the best-performing model for forecasting power generation based on the meteorological factors in his previous paper. On the basis of that, in this paper author has employed only tree-based algorithms and did a comparative analysis among them for predicting power consumption. We have used a publically available dataset of Tetouan city for our analysis and used a variety of evaluation metrics to gauge how well each model performs. Based on the experimentation, XGBoost came out as the best-performing model which performed equally well on both the training and test set.
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Rawat, D.S., Premraj, D.M. (2023). Predicting Power Consumption Using Tree-Based Model. In: Devedzic, V., Agarwal, B., Gupta, M.K. (eds) Proceedings of the International Conference on Intelligent Computing, Communication and Information Security. ICICCIS 2022. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-1373-2_15
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DOI: https://doi.org/10.1007/978-981-99-1373-2_15
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