Abstract
We describe our experience in developing a predictive model that placed a high position in the BigDEAL Challenge 2022, an energy competition of load and peak forecasting. We present a novel procedure for feature engineering and feature selection, based on cluster permutation of temperatures and calendar variables. We adopted gradient boosting of trees and we enhanced its capabilities with trend modeling and distributional forecasts. We also included an approach to forecasts combination known as temporal hierarchies, which further improves the accuracy.
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Acknowledgments
Work partially funded by the Swiss National Science Foundation (grant 212164), and the ERA-NET Smart Energy Systems program (grant 883973, project Digicities).
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Rubattu, N., Maroni, G., Corani, G. (2023). Electricity Load and Peak Forecasting: Feature Engineering, Probabilistic LightGBM and Temporal Hierarchies. In: Ifrim, G., et al. Advanced Analytics and Learning on Temporal Data. AALTD 2023. Lecture Notes in Computer Science(), vol 14343. Springer, Cham. https://doi.org/10.1007/978-3-031-49896-1_18
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DOI: https://doi.org/10.1007/978-3-031-49896-1_18
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