Hybrid Energy Forecasting and Load Optimization with MILP in Smart Homes

Tokhmetov A. Mukhanova A. Nurgaliyev K. Tanchenko L.
31 December 2025 Intelligent Network and Systems Society

International Journal of Intelligent Engineering and Systems
2025 #18 Issue 11 613 - 628 pp.

This paper addresses the critical link between predictive accuracy and optimization effectiveness in smart home energy management. Our key idea is that the superior accuracy of an advanced forecasting model is a direct enabler for achieving more significant and reliable energy savings. The novelty of this work lies in the rigorous, end-to-end validation of a comprehensive framework that integrates a hybrid attention-based encoder-decoder neural model with a formal mixed-integer linear programming (MILP) optimizer. The proposed forecasting model significantly outperforms traditional baselines, achieving excellent accuracy (R² ≈ 0.91, MAPE ≈ 2.4%). This high-precision forecast serves as a critical input for the MILP-based Home Energy Management System (HEMS), which in turn facilitates a 22.5% reduction in energy cost and a 31.8% decrease in peak demand. The framework’s robustness is confirmed through extensive validation, including multi-household testing and sensitivity analysis under forecast error. Interpretability is enhanced via SHAP (SHapley Additive exPlanations) and attention visualization, linking model behavior to meaningful temporal features. The results empirically validate our central thesis, demonstrating that a high-fidelity forecasting component is fundamental to unlocking the full potential of formal optimization for demand response in intelligent residential environments. This article is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. License details: https://creativecommons.org/licenses/by-sa/4.0/

Attention-based encoder-decoder , Deep learning , Demand response , Energy forecasting , Load optimization , MILP , Smart homes

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Department of Information Systems, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
School of Software Engineering, Astana IT University, Astana, Kazakhstan

Department of Information Systems
School of Software Engineering

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