Enhancing residential load forecasting accuracy through dynamic feature selection and ensemble machine learning models: A real-world scenario in Southern Finland - UTU Tutkimustietojärjestelmä

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Enhancing residential load forecasting accuracy through dynamic feature selection and ensemble machine learning models: A real-world scenario in Southern Finland

Tekijät: Taheri, Nabi; Karttunen, Lauri; Jouttijärvi, Sami; Piazzi, Antonio; Tucci, Mauro; Miettunen, Kati

Kustantaja: ELSEVIER SCIENCE SA

Julkaisuvuosi: 2025

Lehti: Energy and Buildings

Artikkelin numero: 116589

Vuosikerta: 349

ISSN: 0378-7788

eISSN: 1872-6178

DOI: https://doi.org/10.1016/j.enbuild.2025.116589

Julkaisun avoimuus kirjaamishetkellä: Avoimesti saatavilla

Julkaisukanavan avoimuus : Osittain avoin julkaisukanava

Verkko-osoite: https://doi.org/10.1016/j.enbuild.2025.116589

Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/505439929

Tiivistelmä

This study aims to improve the accuracy of residential electricity consumption forecasts, a key component of modern energy management systems, especially in settings with high demand variability. The novelty of this work lies in combining dynamic feature selection-adapting to recent data patterns-with a stacking-based ensemble model that leverages multiple predictors. A comprehensive hourly dataset from 200 residential buildings in southwestern Finland was collected throughout 2023, including meteorological variables and real-time electricity prices. The forecasting task was defined as a day-ahead (24-hour ahead) prediction. The year was divided into cold (October-March) and warm (April-September) periods. Four machine learning models were employed per season, including XGBoost, Random Forest, Voting, and a Stacking ensemble with Ridge regression as the meta-learner. The stacking model achieved the best performance in 163 buildings in the cold period and 178 buildings in the warm period. Feature importance was assessed using SHAP values, comparing static and dynamic feature selection strategies. The dynamic approach reduced average prediction error from 11.85% to 9.31% in cold months and from 11.67% to 9.14% in warm months, outperforming the static method in over 96% of buildings. These findings underscore the effectiveness of adaptive feature selection and ensemble learning in capturing seasonal and behavioral dynamics in residential electricity usage.

Ladattava julkaisu

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Julkaisussa olevat rahoitustiedot:
Nabi Taheri gratefully acknowledges the support from the University of Pisa, the University of Turku, and i-EM Company during the preparation of this work. This project was funded by the Strategic Research Council (SRC) established within the Research Council of Finland (RealSolar, Decision No. 358542). The work was supported by the Strategic Research Council within the Research Council of Finland, Decision No. 358542 (KM, SJ). Lauri Karttunen thanks the City of Salo and the University of Turku (project HEMS), and UTUGS.