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EMD-Dual-Branch MLP with NSGA-II Multi-Objective Optimization for Day-Ahead Load Forecasting
https://doi.org/10.15518/isjaee.2026.01.176-194
Abstract
This study addresses day-ahead multi-step load forecasting at 15-min resolution (24 h, 96 steps), where strong nonlinearity, non-stationarity and multi-scale mixing hinder accurate long-horizon prediction. We propose an EMD-dual-branch MLP framework with component-wise modeling and calibrated fusion. Specifically, the original load series is decomposed by empirical mode decomposition and reconstructed into a high-frequency component (HFC) and a low-frequency component (LFC) using a predictability-oriented split criterion. Two direct multi-output multilayer perceptron (MLP) predictors are trained separately for the HFC and LFC branches, with branch-wise Pearson-based feature selection applied to reduce redundancy and enhance interpretability. To mitigate reconstruction bias, a Global Affine fusion is applied as post-hoc calibration to mitigate reconstruction bias. Multi-objective hyperparameter optimization is performed via NSGA-II (Optuna) by jointly minimizing validation errors of the HFC and LFC branches. Experiments on UCI ElectricityLoadDiagrams (MT_232, 2012–2013) with IPMA temperature series (Lisbon) show that EMD_MLP achieves RMSE/MAE/sMAPE = 8.567/5.434/7.546, reducing RMSE by 16.6 % and 19.5 % versus MLP and LSTM; the NSGA-II variant further improves to 8.363/5.377/7.503 and yields a more stable horizon-wise error profile. The proposed method provides reliable high-resolution day-ahead load trajectories for operational scheduling such as unit commitment, reserve scheduling and market operation.
About the Authors
Xiaoyu Chen
Ural Federal University named after the first President of Russia B. N. Yeltsin
Russian Federation
Russian Federation
Chen Xiaoyu, graduate student
620062, Yekaterinburg, Mira st., 19
V. I. Velkin
Ural Federal University named after the first President of Russia B. N. Yeltsin
Russian Federation
Russian Federation
Velkin Vladimir Ivanovic, Professor of the Ural Federal University, Department of nuclear power plants and renewable energy sources
620062, Yekaterinburg, Mira st., 19
Lisong Qin
Ural Federal University named after the first President of Russia B. N. Yeltsin
Russian Federation
Russian Federation
Qin Lisong, graduate student
620062, Yekaterinburg, Mira st., 19
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Review
For citations:
Chen X., Velkin V.I., Qin L. EMD-Dual-Branch MLP with NSGA-II Multi-Objective Optimization for Day-Ahead Load Forecasting. Alternative Energy and Ecology (ISJAEE). 2026;(1):176-194. https://doi.org/10.15518/isjaee.2026.01.176-194
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