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Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm

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  1. Guixiang Xue & Yu Pan & Tao Lin & Jiancai Song & Chengying Qi & Zhipan Wang, 2019. "District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model," Energies, MDPI, vol. 12(11), pages 1-21, June.
  2. Lee, Jae Yong & Yim, Taesu, 2021. "Energy and flow demand analysis of domestic hot water in an apartment complex using a smart meter," Energy, Elsevier, vol. 229(C).
  3. Chen, Xuejun & Yang, Yongming & Cui, Zhixin & Shen, Jun, 2019. "Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy," Energy, Elsevier, vol. 174(C), pages 1100-1109.
  4. Hamed Safayenikoo & Fatemeh Nejati & Moncef L. Nehdi, 2022. "Indirect Analysis of Concrete Slump Using Different Metaheuristic-Empowered Neural Processors," Sustainability, MDPI, vol. 14(16), pages 1-16, August.
  5. Xue, Puning & Jiang, Yi & Zhou, Zhigang & Chen, Xin & Fang, Xiumu & Liu, Jing, 2019. "Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms," Energy, Elsevier, vol. 188(C).
  6. Mengting Jiang & Camilo Rindt & David M. J. Smeulders, 2022. "Optimal Planning of Future District Heating Systems—A Review," Energies, MDPI, vol. 15(19), pages 1-38, September.
  7. Chanuk Lee & Dong Eun Jung & Donghoon Lee & Kee Han Kim & Sung Lok Do, 2021. "Prediction Performance Analysis of Artificial Neural Network Model by Input Variable Combination for Residential Heating Loads," Energies, MDPI, vol. 14(3), pages 1-19, February.
  8. Bui, Dac-Khuong & Nguyen, Tuan Ngoc & Ngo, Tuan Duc & Nguyen-Xuan, H., 2020. "An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings," Energy, Elsevier, vol. 190(C).
  9. Zhang, Hao & Shi, Yuxin & Yang, Xueran & Zhou, Ruiling, 2021. "A firefly algorithm modified support vector machine for the credit risk assessment of supply chain finance," Research in International Business and Finance, Elsevier, vol. 58(C).
  10. Xue, Puning & Zhou, Zhigang & Fang, Xiumu & Chen, Xin & Liu, Lin & Liu, Yaowen & Liu, Jing, 2017. "Fault detection and operation optimization in district heating substations based on data mining techniques," Applied Energy, Elsevier, vol. 205(C), pages 926-940.
  11. Maljkovic, Danica & Basic, Bojana Dalbelo, 2020. "Determination of influential parameters for heat consumption in district heating systems using machine learning," Energy, Elsevier, vol. 201(C).
  12. Ferrari, Simone & Zagarella, Federica & Caputo, Paola & D'Amico, Antonino, 2019. "Results of a literature review on methods for estimating buildings energy demand at district level," Energy, Elsevier, vol. 175(C), pages 1130-1137.
  13. Joanna Piotrowska-Woroniak & Tomasz Szul & Krzysztof Cieśliński & Jozef Krilek, 2022. "The Impact of Weather-Forecast-Based Regulation on Energy Savings for Heating in Multi-Family Buildings," Energies, MDPI, vol. 15(19), pages 1-30, October.
  14. Zhihua Ge & Fuxiang Zhang & Shimeng Sun & Jie He & Xiaoze Du, 2018. "Energy Analysis of Cascade Heating with High Back-Pressure Large-Scale Steam Turbine," Energies, MDPI, vol. 11(1), pages 1-15, January.
  15. Noussan, Michel & Jarre, Matteo & Poggio, Alberto, 2017. "Real operation data analysis on district heating load patterns," Energy, Elsevier, vol. 129(C), pages 70-78.
  16. Golmohamadi, Hessam & Larsen, Kim Guldstrand & Jensen, Peter Gjøl & Hasrat, Imran Riaz, 2022. "Integration of flexibility potentials of district heating systems into electricity markets: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
  17. Gu, Jihao & Wang, Jin & Qi, Chengying & Min, Chunhua & Sundén, Bengt, 2018. "Medium-term heat load prediction for an existing residential building based on a wireless on-off control system," Energy, Elsevier, vol. 152(C), pages 709-718.
  18. Joanna Piotrowska-Woroniak & Krzysztof Cieśliński & Grzegorz Woroniak & Jonas Bielskus, 2022. "The Impact of Thermo-Modernization and Forecast Regulation on the Reduction of Thermal Energy Consumption and Reduction of Pollutant Emissions into the Atmosphere on the Example of Prefabricated Build," Energies, MDPI, vol. 15(8), pages 1-32, April.
  19. Niemierko, Rochus & Töppel, Jannick & Tränkler, Timm, 2019. "A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data," Applied Energy, Elsevier, vol. 233, pages 691-708.
  20. Xue, Guixiang & Qi, Chengying & Li, Han & Kong, Xiangfei & Song, Jiancai, 2020. "Heating load prediction based on attention long short term memory: A case study of Xingtai," Energy, Elsevier, vol. 203(C).
  21. Valeria Todeschi & Roberto Boghetti & Jérôme H. Kämpf & Guglielmina Mutani, 2021. "Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
  22. Zhong, Wei & Huang, Wei & Lin, Xiaojie & Li, Zhongbo & Zhou, Yi, 2020. "Research on data-driven identification and prediction of heat response time of urban centralized heating system," Energy, Elsevier, vol. 212(C).
  23. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
  24. Xin Xiao & Qian Hu & Huansong Jiao & Yunfeng Wang & Ali Badiei, 2023. "Simulation and Machine Learning Investigation on Thermoregulation Performance of Phase Change Walls," Sustainability, MDPI, vol. 15(14), pages 1-22, July.
  25. Mohammed Amroune & Ismail Musirin & Tarek Bouktir & Muhammad Murtadha Othman, 2017. "The Amalgamation of SVR and ANFIS Models with Synchronized Phasor Measurements for On-Line Voltage Stability Assessment," Energies, MDPI, vol. 10(11), pages 1-18, October.
  26. Guo, Yabin & Wang, Jiangyu & Chen, Huanxin & Li, Guannan & Liu, Jiangyan & Xu, Chengliang & Huang, Ronggeng & Huang, Yao, 2018. "Machine learning-based thermal response time ahead energy demand prediction for building heating systems," Applied Energy, Elsevier, vol. 221(C), pages 16-27.
  27. Hribar, Rok & Potočnik, Primož & Šilc, Jurij & Papa, Gregor, 2019. "A comparison of models for forecasting the residential natural gas demand of an urban area," Energy, Elsevier, vol. 167(C), pages 511-522.
  28. Ma, Liqun & Ge, Zhihua & Zhang, Fuxiang & Wei, Huimin, 2020. "A novel super high back pressure cascade heating scheme with multiple large-scale turbine units," Energy, Elsevier, vol. 201(C).
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