Interpretable Energy Forecasting: Comparative Analysis of Voting Regression and NODE Models for Electricity Power Consumption Prediction

Basit Öğe Kaydı
dc.contributor.authorAsal, Burcak
dc.date.accessioned2026-02-27T07:32:59Z
dc.date.available2026-02-27T07:32:59Z
dc.date.issued2025
dc.description7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications-ICHORA
dc.description.abstractAccurate electricity power consumption estimation is vital for efficient energy management, demand-side response strategies and grid optimization. This study explores ensemble based machine learning-based and deep learning approaches for electricity power consumption prediction across three distinct zones by using a Voting Regression model including Linear Regression, Random Forest and K-Nearest Neighbors (KNN) and NODE (Neural Oblivious Decision Ensembles) model. Quantitative and qualitative experimental results show that Voting Regression model outperforms NODE model in overall and additionally, SHAP (SHapley Additive exPlanations) technique is applied on the Voting Regression model to analyze and explain the feature contributions and effects for the model prediction. Consequently, this study provides a reliable, explainable and data-driven framework for optimizing electricity power distribution and electricity consumption planning.
dc.identifier.doi10.1109/ICHORA65333.2025.11017058
dc.identifier.isbn979-8-3315-1089-3; 979-8-3315-1088-6
dc.identifier.issn2996-4385
dc.identifier.urihttp://dx.doi.org/10.1109/ICHORA65333.2025.11017058
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4405
dc.identifier.wosWOS:001533792800076
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2025 7th International Congress On Human-Computer Interaction, Optimization and Robotic Applications, Ichora
dc.relation.ispartofseriesInternational Congress on Human-Computer Interaction Optimization and Robotic Applications
dc.relation.publicationcategoryKonferans ��esi - Uluslararas� - Kurum ��retim Eleman�
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20260302
dc.subjectexplainable artificial intelligence (XAI)
dc.subjectmachine learning
dc.subjectdeep learning
dc.subjectSHAP
dc.subjectelectricity power consumption prediction
dc.subjectlinear regression
dc.subjectKNN
dc.subjectrandom forest
dc.subjectvoting regression
dc.subjectNODE
dc.titleInterpretable Energy Forecasting: Comparative Analysis of Voting Regression and NODE Models for Electricity Power Consumption Prediction
dc.typeProceedings Paper

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu