Henri Tantyoko, Satriawan Rasyid Purnama, Etna Vianita

Multi-Horizon Short-Term Residential Load Forecasting Using Decomposition-Based Linear Neural Network

Introduction

Multi-horizon short-term residential load forecasting using decomposition-based linear neural network. Improve grid stability & energy management with multi-horizon residential load forecasting. DLinear, a decomposition-based linear neural network, provides robust, accurate electricity predictions.

Abstract

Short-Term Load Forecasting is crucial for grid stability and real-time energy management, particularly in residential settings where consumption is highly volatile and influenced by behavioral and external factors. Traditional models struggle to capture complex, non-linear patterns. This study proposes a forecasting framework based on the DLinear model, which decomposes time series data into trend and seasonal components using a simple linear neural network architecture. Designed for multi-horizon forecasting, the model predicts electricity demand across several future time points simultaneously. Experimental results show that DLinear performs best at a 24-hour prediction length, achieving the lowest MSE of 41.58 and MAE of 5.11, indicating improved accuracy with longer horizons. These results confirm DLinear’s robustness and efficiency in modeling dynamic residential electricity consumption patterns.

Review

This paper addresses the critical problem of Short-Term Load Forecasting (STLF) in residential settings, a domain characterized by high volatility and complex, non-linear consumption patterns driven by behavioral and external factors. The authors propose a novel forecasting framework leveraging the DLinear model, which uniquely decomposes time series data into distinct trend and seasonal components. This decomposition is achieved through a simple linear neural network architecture, aiming to effectively capture the underlying dynamics that often challenge traditional forecasting methodologies. The timely nature of this work is undeniable, given the increasing demands on grid stability and the imperative for precise real-time energy management in distributed residential energy systems. The methodology centers on a multi-horizon forecasting approach, enabling the simultaneous prediction of electricity demand across multiple future time points. A significant finding reported is that the DLinear model demonstrates its strongest performance at a 24-hour prediction length, achieving an impressive Mean Squared Error (MSE) of 41.58 and a Mean Absolute Error (MAE) of 5.11. Notably, the abstract states that these results indicate "improved accuracy with longer horizons," a counter-intuitive yet highly valuable characteristic for operational planning, suggesting the model's robustness in capturing daily cycles and underlying trends over extended periods. This performance underscores the model's potential for enhanced accuracy and efficiency in dynamic residential electricity consumption modeling. While the abstract presents a compelling case for DLinear's capabilities, particularly its multi-horizon effectiveness and performance at a 24-hour horizon, a more exhaustive review would benefit from additional details. For instance, clarifying the specific "traditional models" against which DLinear was benchmarked would provide essential context for its claimed superiority. Furthermore, details regarding the characteristics and scale of the residential dataset used, as well as an analysis of the computational efficiency of the "simple linear neural network," would enhance the understanding of its practical applicability. Future work could explore the model's performance across an even wider range of prediction horizons, investigate its generalization across diverse residential load profiles and geographical regions, and potentially delve deeper into the interpretability of its decomposition mechanism to explain *why* longer horizons yield improved accuracy.

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