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

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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)

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.

Suggested Citation

  • Ruijin Zhu & Weilin Guo & Xuejiao Gong, 2019. "Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning," Energies, MDPI, vol. 12(17), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3308-:d:261567
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    as
    1. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    2. Wei, Maolin & Yuan, Weixing & Fu, Lin & Zhang, Shigang & Zhao, Xiling, 2018. "Summer performance analysis of coal-based CCHP with new configurations comparing with separate system," Energy, Elsevier, vol. 143(C), pages 104-113.
    3. Wang, Zheng-Xin & Li, Qin & Pei, Ling-Ling, 2018. "A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors," Energy, Elsevier, vol. 154(C), pages 522-534.
    4. Singh, Priyanka & Dwivedi, Pragya, 2018. "Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem," Applied Energy, Elsevier, vol. 217(C), pages 537-549.
    5. Qing, Xiangyun & Niu, Yugang, 2018. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, Elsevier, vol. 148(C), pages 461-468.
    6. Wang, Jiangjiang & Ma, Chaofan & Wu, Jing, 2019. "Thermodynamic analysis of a combined cooling, heating and power system based on solar thermal biomass gasification☆," Applied Energy, Elsevier, vol. 247(C), pages 102-115.
    7. Jin Wu & Jiangjiang Wang & Jing Wu & Chaofan Ma, 2019. "Exergy and Exergoeconomic Analysis of a Combined Cooling, Heating, and Power System Based on Solar Thermal Biomass Gasification," Energies, MDPI, vol. 12(12), pages 1-19, June.
    8. Zheng, Xuyue & Wu, Guoce & Qiu, Yuwei & Zhan, Xiangyan & Shah, Nilay & Li, Ning & Zhao, Yingru, 2018. "A MINLP multi-objective optimization model for operational planning of a case study CCHP system in urban China," Applied Energy, Elsevier, vol. 210(C), pages 1126-1140.
    9. Chia, Yen Yee & Lee, Lam Hong & Shafiabady, Niusha & Isa, Dino, 2015. "A load predictive energy management system for supercapacitor-battery hybrid energy storage system in solar application using the Support Vector Machine," Applied Energy, Elsevier, vol. 137(C), pages 588-602.
    10. Barman, Mayur & Dev Choudhury, N.B. & Sutradhar, Suman, 2018. "A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India," Energy, Elsevier, vol. 145(C), pages 710-720.
    11. Moritz Wegener & Antonio Isalgué & Anders Malmquist & Andrew Martin, 2019. "3E-Analysis of a Bio-Solar CCHP System for the Andaman Islands, India—A Case Study," Energies, MDPI, vol. 12(6), pages 1-19, March.
    12. Dedinec, Aleksandra & Filiposka, Sonja & Dedinec, Aleksandar & Kocarev, Ljupco, 2016. "Deep belief network based electricity load forecasting: An analysis of Macedonian case," Energy, Elsevier, vol. 115(P3), pages 1688-1700.
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    Cited by:

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    3. Zizhen Cheng & Li Wang & Yumeng Yang, 2023. "A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting," Energies, MDPI, vol. 16(7), pages 1-18, March.
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    5. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
    6. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    7. Sungwoo Park & Jihoon Moon & Seungwon Jung & Seungmin Rho & Sung Wook Baik & Eenjun Hwang, 2020. "A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling," Energies, MDPI, vol. 13(2), pages 1-23, January.
    8. Li, Chuang & Li, Guojie & Wang, Keyou & Han, Bei, 2022. "A multi-energy load forecasting method based on parallel architecture CNN-GRU and transfer learning for data deficient integrated energy systems," Energy, Elsevier, vol. 259(C).
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