Improved Deep Learning Model Based on Self-Paced Learning for Multiscale Short-Term Electricity Load Forecasting

Meiping Li,Xiaoming Xie,Du Zhang

doi:10.3390/su14010188

Abstract

Electricity loads are basic and important information for power generation facilities and traders, especially in terms of production plans, daily operations, unit commitments, and economic dispatches. Short-term load forecasting (STLF), which predicts power loads for a few days, plays a vital role in the reliable, safe, and efficient operation of a power system. Currently, two main challenges are faced by existing STLF prediction models. The first involves how to fuse multiscale electricity load data to obtain a high-performance model and remove data noise after integration. The second involves how to improve the local optimal solution despite the sample quality problem. To address the above issues, this paper proposes a multiscale electricity load data fusion- and STLF-based short time series prediction model built on a sparse deep autoencoder and self-paced learning (SPL). A sparse deep autoencoder was used to solve the multiscale data fusion problem with data noise. Furthermore, SPL was utilized to solve the local optimal solution problem. The experimental results showed that our model was better than the existing STLF prediction models by more than 15.89% in terms of the mean squared error (MSE) indicator.

Highlights

To further prove that the denoising ability of the sparse deep autoencoder and self-paced learning (SPL) approach could effectively solve the problem of the model falling into local optimal solutions, this study introduced two variant models: AE–multilayer perceptron (MLP) and SPL and an MLP (SPLMLP)
In this study we designed AE–SPLMLP, a new short-term load forecasting (STLF) prediction model based on a sparse deep autoencoder and the strategy of combining SPL and an MLP (SPLMLP)
The prediction performance of the AE–SPLMLP model was significantly better than that of the SPLMLP model, which showed that the sparse autoencoder could fuse multiscale data well and could effectively solve the noise problem observed after data fusion

Summary

Introduction

Medium-term load forecasting can generally be used to predict the power load situation of a target area for time periods ranging from a few days to a few months [5]. Due to the COVID-19 pandemic, the prices of the raw materials for use in electricity production have risen sharply [9], which has made the supplies of electricity in many countries increasingly tight [10].

Methods

Results

Conclusion