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2 articles for “residential load forecasting”
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Controlled Temporal Feature Ablation and Explainable Ensemble Learning for Next- Hour Residential Electricity Forecasting
Abstract: Short-term residential load forecasting is increasingly used in demand response, home energy management, and data-driven grid operation, yet reported gains are often difficult to attribute to either the forecasting algorithm or the temporal representation supplied to it. This study presents a controlled, leakage-aware evaluation of temporal feature engineering for one-hour-ahead household electricity forecasting. The public UCI Individual Household Electric Power Consumption dataset was aggregated to hourly resolution, causally imputed using …
Published in Trends in Electrical Engineering · Vol. 13, Issue 3, 2023 Read article
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Data-Driven Energy Forecasting for Smart Homes: Ensemble Learning from IoT Meters and Relevance for Polymer-Composite Based Smart Infrastructure
Abstract: Reliable estimation of household electricity demand is relevant in creating efficiency in energy usage, optimization of the loads, and intelligent demand-side management in intelligent grid systems. This paper introduces a varied machine learning model that approaches residential electric consumption prediction using an assortment of ensemble regression boosts, including Linear Regression, Lasso Regression, Decision Tree Regressor, Random Forest, and Gradient Boosting, to predict residential electricity consumption environments on a time-series arrested …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 29–64 Read article