A Collaborative Multi-Component Optimization Model Based on Pattern Sequence Similarity for Electricity Demand Prediction

Abstract

In the new electricity market, the accurate electricity demand prediction can make high possible profit. However, electricity consumption data exhibits nonlinearity, high volatility, and susceptibility to various factors. Most existing prediction schemes inadequately account for these traits, resulting in weak performance. In view of this, we propose a collaborative multi-component optimization model (MCO-BHPSF) to achieve high accuracy electricity demand prediction. For this model, the original data is first decomposed into linear trend components and nonlinear residual components using the Moving Average filter. Then, the enhanced Pattern Sequence-based Forecasting (PSF) algorithm that can effectively capture data patterns with obvious changes is used to accurately forecast the trend component and the embedded LightGBM for residual components. We further optimize the prediction results by using an error optimization scheme based on online sequence extreme learning machines to reduce prediction errors. The results of extensive experiments on four real-world datasets demonstrate that our proposed MCO-BHPSF model outperforms four advanced baseline models. In day-ahead prediction, our model is on average 31% better than PSF baselines. For long-term prediction, our proposed MCO-BHPSF model has an average improvement rate of 37% compared to PSF baselines.