Short-term Forecast of Multiple Loads in Integrated Energy System Based on IPSO-WNN

Cao Yuwei,Zeng Ming,Shi Pengjia,Yang Weihong,Guo Xiaopeng,Jiang Shigong,R Weerasinghe,J Wu,C.-H Weng

doi:10.1051/e3sconf/202019401029

Abstract

Accurate short-term energy load forecasting has a considerable influence on the economic scheduling and optimal operation of integrated energy system. This study proposes an improved particle swarm optimization-wavelet neural network (IPSO-WNN) method for short-term load forecasting of integrated energy system. First, Kendall rank correlation coefficient in Copula theory is used to analyze the correlation among the influencing factors, through which the influencing factors with strong correlation are selected as input variables of the model. Secondly, chaos algorithm and adaptive weight selection strategy are introduced in the POS-WNN forecasting model to improve the prediction accuracy. Therefore, a short-term load forecasting model of integrated energy system based on IPSO-WNN is established. Finally, the analysis of examples shows that the load prediction accuracy is significantly improved based on the IPSO-WNN model compared with the traditional forecasting model.

Highlights

The integrated energy system (IES) is a comprehensive energy supply platform, including plenty of energy sources such as cold, heat, and electricity [1]
The above studies have high forecasting accuracy, they are only used for electric load forecasting and do not consider multiple loads such as electric, gas, heat, and cold loads
In order to more intuitively show the correlation between multiple loads in the IES and the relationship between them and weather factors, this paper uses Copula theory for correlation analysis, which describes the non-linear correlation between variables

Summary

Introduction

The integrated energy system (IES) is a comprehensive energy supply platform, including plenty of energy sources such as cold, heat, and electricity [1]. Reference [3] introduced chaos disturbance factor in cuckoo algorithm and established the Elman-IOC short-term electric load forecasting model. Reference [4] proposed a short-term load forecasting method based on deep belief network applied in a complex environment. Reference [5] proposed a new method for short-term load forecasting sensitive to external factors based on empirical mode decomposition and feature correlation analysis. Reference [7] proposed a short-term combined forecasting method of multiple loads based on deep structure multi-task learning. Reference [8] quantitatively analyzed the correlation between cold and heat loads based on Copula theory, which improved the load prediction accuracy of the IES. This paper uses an improved wavelet neural network (WNN) to make short-term forecasting for electric, heat, cold and gas loads. An example is used to prove the prediction accuracy of the method

Correlation analysis based on Copula theory

Example analysis

Correlation analysis

Findings

Conclusion