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Short-term prediction of electric demand in building sector via hybrid support vector regression

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Cited by:

  1. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
  2. Vangelis Marinakis, 2020. "Big Data for Energy Management and Energy-Efficient Buildings," Energies, MDPI, vol. 13(7), pages 1-18, March.
  3. Jeong, Dongyeon & Park, Chiwoo & Ko, Young Myoung, 2021. "Short-term electric load forecasting for buildings using logistic mixture vector autoregressive model with curve registration," Applied Energy, Elsevier, vol. 282(PB).
  4. Seyed Morteza Moghimi & Thomas Aaron Gulliver & Ilamparithi Thirumai Chelvan, 2024. "Energy Management in Modern Buildings Based on Demand Prediction and Machine Learning—A Review," Energies, MDPI, vol. 17(3), pages 1-20, January.
  5. Andreea Valeria Vesa & Tudor Cioara & Ionut Anghel & Marcel Antal & Claudia Pop & Bogdan Iancu & Ioan Salomie & Vasile Teodor Dadarlat, 2020. "Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs," Sustainability, MDPI, vol. 12(4), pages 1-23, February.
  6. William Mounter & Chris Ogwumike & Huda Dawood & Nashwan Dawood, 2021. "Machine Learning and Data Segmentation for Building Energy Use Prediction—A Comparative Study," Energies, MDPI, vol. 14(18), pages 1-42, September.
  7. Chen, Yibo & Zhang, Fengyi & Berardi, Umberto, 2020. "Day-ahead prediction of hourly subentry energy consumption in the building sector using pattern recognition algorithms," Energy, Elsevier, vol. 211(C).
  8. Gulay, Emrah & Sen, Mustafa & Akgun, Omer Burak, 2024. "Forecasting electricity production from various energy sources in Türkiye: A predictive analysis of time series, deep learning, and hybrid models," Energy, Elsevier, vol. 286(C).
  9. Wei, Ziqing & Zhang, Tingwei & Yue, Bao & Ding, Yunxiao & Xiao, Ran & Wang, Ruzhu & Zhai, Xiaoqiang, 2021. "Prediction of residential district heating load based on machine learning: A case study," Energy, Elsevier, vol. 231(C).
  10. Fan, Cheng & Xiao, Fu & Song, Mengjie & Wang, Jiayuan, 2019. "A graph mining-based methodology for discovering and visualizing high-level knowledge for building energy management," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
  11. Li, Yanying & Che, Jinxing & Yang, Youlong, 2018. "Subsampled support vector regression ensemble for short term electric load forecasting," Energy, Elsevier, vol. 164(C), pages 160-170.
  12. Fan, Cheng & Xiao, Fu & Yan, Chengchu & Liu, Chengliang & Li, Zhengdao & Wang, Jiayuan, 2019. "A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning," Applied Energy, Elsevier, vol. 235(C), pages 1551-1560.
  13. Li, Xinyue & Chen, Shuqin & Li, Hongliang & Lou, Yunxiao & Li, Jiahe, 2023. "A behavior-orientated prediction method for short-term energy consumption of air-conditioning systems in buildings blocks," Energy, Elsevier, vol. 263(PD).
  14. 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.
  15. Mehmanpazir, Farhad & Khalili-Damghani, Kaveh & Hafezalkotob, Ashkan, 2019. "Modeling steel supply and demand functions using logarithmic multiple regression analysis (case study: Steel industry in Iran)," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
  16. Yang, Youlong & Che, Jinxing & Deng, Chengzhi & Li, Li, 2019. "Sequential grid approach based support vector regression for short-term electric load forecasting," Applied Energy, Elsevier, vol. 238(C), pages 1010-1021.
  17. Chitalia, Gopal & Pipattanasomporn, Manisa & Garg, Vishal & Rahman, Saifur, 2020. "Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 278(C).
  18. Shangfu Wei & Xiaoqing Bai, 2022. "Multi-Step Short-Term Building Energy Consumption Forecasting Based on Singular Spectrum Analysis and Hybrid Neural Network," Energies, MDPI, vol. 15(5), pages 1-21, February.
  19. Haizhou Fang & Hongwei Tan & Ningfang Dai & Zhaohui Liu & Risto Kosonen, 2023. "Hourly Building Energy Consumption Prediction Using a Training Sample Selection Method Based on Key Feature Search," Sustainability, MDPI, vol. 15(9), pages 1-23, May.
  20. Li, Tao & Liu, Xiangyu & Li, Guannan & Wang, Xing & Ma, Jiangqiaoyu & Xu, Chengliang & Mao, Qianjun, 2024. "A systematic review and comprehensive analysis of building occupancy prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
  21. Fazlipour, Zahra & Mashhour, Elaheh & Joorabian, Mahmood, 2022. "A deep model for short-term load forecasting applying a stacked autoencoder based on LSTM supported by a multi-stage attention mechanism," Applied Energy, Elsevier, vol. 327(C).
  22. Jiakang Wang & Hui Liu & Guangji Zheng & Ye Li & Shi Yin, 2023. "Short-Term Load Forecasting Based on Outlier Correction, Decomposition, and Ensemble Reinforcement Learning," Energies, MDPI, vol. 16(11), pages 1-16, May.
  23. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
  24. 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.
  25. Manoj Manivannan & Behzad Najafi & Fabio Rinaldi, 2017. "Machine Learning-Based Short-Term Prediction of Air-Conditioning Load through Smart Meter Analytics," Energies, MDPI, vol. 10(11), pages 1-17, November.
  26. Chen, Yibo & Gao, Junxi & Yang, Jianzhong & Berardi, Umberto & Cui, Guoyou, 2023. "An hour-ahead predictive control strategy for maximizing natural ventilation in passive buildings based on weather forecasting," Applied Energy, Elsevier, vol. 333(C).
  27. 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.
  28. Jaewook Lee & Mohamed Boubekri & Feng Liang, 2019. "Impact of Building Design Parameters on Daylighting Metrics Using an Analysis, Prediction, and Optimization Approach Based on Statistical Learning Technique," Sustainability, MDPI, vol. 11(5), pages 1-21, March.
  29. Diego M. Jiménez-Bravo & Juan F. De Paz & Gabriel Villarrubia & Javier Bajo, 2018. "Dealing with Demand in Electric Grids with an Adaptive Consumption Management Platform," Complexity, Hindawi, vol. 2018, pages 1-14, March.
  30. Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
  31. Gulay, Emrah & Duru, Okan, 2020. "Hybrid modeling in the predictive analytics of energy systems and prices," Applied Energy, Elsevier, vol. 268(C).
  32. Liang, Xinbin & Chen, Siliang & Zhu, Xu & Jin, Xinqiao & Du, Zhimin, 2023. "Domain knowledge decomposition of building energy consumption and a hybrid data-driven model for 24-h ahead predictions," Applied Energy, Elsevier, vol. 344(C).
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