Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning

Ruijin Zhu,Weilin Guo,Xuejiao Gong

doi:10.3390/en12173308

Ruijin Zhu, Weilin Guo + Show 1 more

Open Access

https://doi.org/10.3390/en12173308

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Journal: Energies	Publication Date: Aug 28, 2019
Citations: 29	License type: CC BY 4.0

Affiliation: Tibet University

Abstract

Combined cooling, heating, and power (CCHP) systems is a distributed energy system that uses the power station or heat engine to generate electricity and useful heat simultaneously. Due to its wide range of advantages including efficiency, ecological, and financial, the CCHP will be the main direction of the integrated system. The accurate prediction of heating, gas, and electrical loads plays an essential role in energy management in CCHP systems. This paper combined long short-term memory (LSTM) network and convolutional neural network (CNN) to design a novel hybrid neural network for short-term loads forecasting considering their correlation. Pearson correlation coefficient will be utilized to measure the temporal correlation between current load and historical loads, and analyze the coupling between heating, gas and electrical loads. The dropout technique is proposed to solve the over-fitting of the network due to the lack of data diversity and network parameter redundancy. The case study shows that considering the coupling between heating, gas and electrical loads can effectively improve the forecasting accuracy, the performance of the proposed approach is better than that of the traditional methods.

Highlights

With the rapid development of industry, the consumption of energy and other natural resources has increased substantially
Each case ran 50 times independently to obtain the average mean absolute percentage error (MAPE), and the results shown in Figures are shown shown in Figure of heating loads at at different different network network depth
Each method ran times independently to obtain the average including BP network, support vector machine (SVM), autoregressive integrated moving average (ARIMA), convolutional neural network (CNN), and long short-term memory (LSTM) were taken as a comparison and assessed the average MAPE of the test set, and the results are shown in Figure12 to Figure 14 and Table 6

Summary

Introduction

With the rapid development of industry, the consumption of energy and other natural resources has increased substantially. The combined cooling heating, and power system is one of the distributed energy systems, which uses a power station or heat engine to generate useful heat and electricity at the same time. It is arranged near the users on a small scale, decentralized, and targeted manner, and delivers heating energy and electric energy to nearby users according to the users’ different needs [1,2]. Compared with conventional centralized power systems, the combined cooling, heating, and power (CCHP) system has lower energy costs, higher energy efficiency, and higher energy availability. The CCHP system will become the main form of the integrated energy system [3]

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