Abstract
An accurate residential load forecast benefits a consumer domain energy management system extensively, as it provides minimum threshold energy to the consumers instead of completely shedding supply by the electricity distribution companies. In this paper, a model is proposed using the extreme gradient boosting ensemble algorithm for forecasting of residential loads. The publicly available UCI dataset is utilized, which is based on the real-life electric power consumption of a residence in France. Lag-based features capturing power consumption for different periods are added to the dataset. Correlation analysis of the features is done to filter the redundancy of features. The dataset was resampled for different time resolutions and used it for forecasting the power consumption of the residence for a day and a week ahead. The results from experimentation strongly indicate that the proposed model outperforms the existing machine learning models as to the accuracy of forecast and computational time.
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Venkat, K., Gautam, T., Yadav, M., Singh, M. (2021). An XGBoost Ensemble Model for Residential Load Forecasting. In: Pandian, A.P., Palanisamy, R., Ntalianis, K. (eds) Proceedings of International Conference on Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing, vol 1272. Springer, Singapore. https://doi.org/10.1007/978-981-15-8443-5_26
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DOI: https://doi.org/10.1007/978-981-15-8443-5_26
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