Work

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

Year: 2025

Type: article

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.

Source: Energies

DOI: 10.3390/en18205385

Authors: Wook-Won Kim, Jun-Hyeok Kim

Institutions: Korea Electric Power Research Institute, Hankyong National University

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137