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

Ruijin Zhu, Weilin Guo and Xuejiao Gong
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Ruijin Zhu: Electric Engineering College, Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China
Weilin Guo: Electric Engineering College, Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China
Xuejiao Gong: Electric Engineering College, Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China

Energies, 2019, vol. 12, issue 17, 1-18

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.

Keywords: short-term loads forecasting; CCHP systems; convolutional neural network; short-term memory network; dropout layer (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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