Kolmogorov-Arnold Recurrent Network for Short Term Load Forecasting Across Diverse Consumers

TL;DR

This paper introduces the Kolmogorov-Arnold Recurrent Network (KARN), a novel model that improves short-term load forecasting accuracy across diverse consumer types by combining Kolmogorov-Arnold Networks with RNNs.

Contribution

KARN is a new load forecasting model that uses learnable spline functions and edge-based activations to better capture non-linear load patterns across various consumers.

Findings

KARN outperforms traditional RNNs, LSTMs, and GRUs in load forecasting accuracy.

KARN demonstrates consistent performance across multiple real-world datasets.

KARN is adaptable to diverse consumer types, including residential and industrial.

Abstract

Load forecasting plays a crucial role in energy management, directly impacting grid stability, operational efficiency, cost reduction, and environmental sustainability. Traditional Vanilla Recurrent Neural Networks (RNNs) face issues such as vanishing and exploding gradients, whereas sophisticated RNNs such as LSTMs have shown considerable success in this domain. However, these models often struggle to accurately capture complex and sudden variations in energy consumption, and their applicability is typically limited to specific consumer types, such as offices or schools. To address these challenges, this paper proposes the Kolmogorov-Arnold Recurrent Network (KARN), a novel load forecasting approach that combines the flexibility of Kolmogorov-Arnold Networks with RNN's temporal modeling capabilities. KARN utilizes learnable temporal spline functions and edge-based activations to better model non-linear relationships in load data, making it adaptable across a diverse range of consumer types. The proposed KARN model was rigorously evaluated on a variety of real-world datasets, including student residences, detached homes, a home with electric vehicle charging, a townhouse, and industrial buildings. Across all these consumer categories, KARN consistently outperformed traditional Vanilla RNNs, while it surpassed LSTM and Gated Recurrent Units (GRUs) in six buildings. The results demonstrate KARN's superior accuracy and applicability, making it a promising tool for enhancing load forecasting in diverse energy management scenarios.