Multi-Resolution LSTNet Framework with Wavelet Decomposition and Residual Correction for Long-Term Hourly Load Forecasting on Distribution Feeders

Wook-Won Kim and Jun-Hyeok Kim ()
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Wook-Won Kim: Smart Power Distribution Laboratory, Korea Electric Power Corporation Research Institute, Daejeon 34056, Republic of Korea
Jun-Hyeok Kim: School of Electronic and Electrical Engineering, Hankyong National University, Anseong 17579, Republic of Korea

Energies, 2025, vol. 18, issue 20, 1-16

Abstract: Distribution-level long-term load forecasting with hourly resolution is essential for modern power systems operation, yet it remains challenging due to complex temporal patterns and error accumulation over extended horizons. This study proposes a Multi-Resolution Residual LSTNet framework integrating Discrete Wavelet Transform (DWT), Long Short-Term Memory Networks (LSTNet), and Normalized Linear (NLinear) models for accurate one-year ahead hourly load forecasting. The methodology decomposes load time series into daily, weekly, and monthly components using multi-resolution DWT, applies direct forecasting with LSTNet to capture short-term and long-term dependencies, performs residual correction using NLinear models, and integrates predictions through dynamic weighting mechanisms. Validation using five years of Korean distribution feeder data (2015–2019) demonstrates significant performance improvements over benchmark methods including Autoformer, LSTM, and NLinear, achieving Mean Absolute Error of 0.5771, Mean Absolute Percentage Error of 17.29%, and Huber Loss of 0.2567. The approach effectively mitigates error accumulation common in long-term forecasting while maintaining hourly resolution, providing practical value for demand response, distributed resource control, and infrastructure planning without requiring external variables.

Keywords: long-term load forecasting; distribution feeder; multi-resolution wavelet transform; LSTNet; residual correction; dynamic weighting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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