Daily peak electrical load forecasting with a multi-resolution approach

TL;DR

This paper introduces a multi-resolution modeling framework for daily peak electrical load forecasting, combining high- and low-resolution data to improve accuracy in smart grid applications.

Contribution

It provides a formal multi-resolution approach, discusses implementation with GAMs and Neural Networks, and demonstrates competitive results on UK electricity data.

Findings

The approach achieves performance comparable to existing methods.

Multi-resolution modeling improves peak demand prediction accuracy.

Experimental validation on real UK data confirms effectiveness.

Abstract

In the context of smart grids and load balancing, daily peak load forecasting has become a critical activity for stakeholders of the energy industry. An understanding of peak magnitude and timing is paramount for the implementation of smart grid strategies such as peak shaving. The modelling approach proposed in this paper leverages high-resolution and low-resolution information to forecast daily peak demand size and timing. The resulting multi-resolution modelling framework can be adapted to different model classes. The key contributions of this paper are a) a general and formal introduction to the multi-resolution modelling approach, b) a discussion on modelling approaches at different resolutions implemented via Generalised Additive Models and Neural Networks and c) experimental results on real data from the UK electricity market. The results confirm that the predictive performance of the proposed modelling approach is competitive with that of low- and high-resolution alternatives.