Work

Integrating Kolmogorov–Arnold Networks with Time Series Prediction Framework in Electricity Demand Forecasting

Year: 2025

Type: article

Abstract: Electricity demand is driven by a diverse set of factors, including fluctuations in business cycles, interregional dynamics, and the effects of climate change. Accurately quantifying the impact of these factors remains challenging, as existing methods often fail to address the complexities inherent in these influences. This study introduces a time series forecasting model based on Kolmogorov–Arnold Networks (KANs), integrated with three advanced neural network architectures, Temporal Convolutional Network (TCN), Bidirectional Long Short-Term Memory (BiLSTM), and Transformer, to forecast UK electricity demand. The analysis utilizes real-world datasets from a leading utility company and publicly available sources. Experimental findings reveal that the integration of KANs significantly improves forecasting accuracy, robustness, and adaptability, particularly in modeling intricate sequential patterns in electricity demand time series. The proposed approach addresses the limitations of traditional time series models, underscoring the potential of KANs as a transformative tool for predictive analytics.

Source: Energies

DOI: 10.3390/en18061365

Authors: Yuyang Zhang, Lei Cui, Wenqiang Yan

Institution: Shanghai Polytechnic University

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

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

Open Access status: gold

Estimated APC paid: $3,137