Multivariate Empirical Mode Decomposition based Hybrid Model for Day-ahead Peak Load Forecasting

TL;DR

This paper introduces a hybrid model combining multivariate empirical mode decomposition, support vector regression, and particle swarm optimization to improve day-ahead peak load forecasting accuracy, validated on Australian datasets.

Contribution

It presents a novel hybrid approach utilizing MEMD for multivariate data decomposition, enhancing peak load prediction accuracy over existing methods.

Findings

The proposed MEMD-PSO-SVR model outperforms traditional models in accuracy.

Application to Australian datasets demonstrates its practical effectiveness.

Quantitative assessments confirm the model's superiority in peak load forecasting.

Abstract

Accurate day-ahead peak load forecasting is crucial not only for power dispatching but also has a great interest to investors and energy policy maker as well as government. Literature reveals that 1% error drop of forecast can reduce 10 million pounds operational cost. Thus, this study proposed a novel hybrid predictive model built upon multivariate empirical mode decomposition (MEMD) and support vector regression (SVR) with parameters optimized by particle swarm optimization (PSO), which is able to capture precise electricity peak load. The novelty of this study mainly comes from the application of MEMD, which enables the multivariate data decomposition to effectively extract inherent information among relevant variables at different time frequency during the deterioration of multivariate over time. Two real-world load data sets from the New South Wales (NSW) and the Victoria (VIC) in Australia have been considered to verify the superiority of the proposed MEMD-PSO-SVR hybrid model. The quantitative and comprehensive assessments are performed, and the results indicate that the proposed MEMD-PSO-SVR method is a promising alternative for day-ahead electricity peak load forecasting.