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Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment

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Cited by:

  1. Sungjin Lee & Soo Cho & Seo-Hoon Kim & Jonghun Kim & Suyong Chae & Hakgeun Jeong & Taeyeon Kim, 2020. "Deep Neural Network Approach for Prediction of Heating Energy Consumption in Old Houses," Energies, MDPI, vol. 14(1), pages 1-14, December.
  2. Vangelis Marinakis, 2020. "Big Data for Energy Management and Energy-Efficient Buildings," Energies, MDPI, vol. 13(7), pages 1-18, March.
  3. Panagiotis Kapsalis & Giorgos Kormpakis & Konstantinos Alexakis & Dimitrios Askounis, 2022. "Leveraging Graph Analytics for Energy Efficiency Certificates," Energies, MDPI, vol. 15(4), pages 1-12, February.
  4. Bartlomiej Kawa & Piotr Borkowski, 2023. "Integration of Machine Learning Solutions in the Building Automation System," Energies, MDPI, vol. 16(11), pages 1-18, June.
  5. Kamel, Ehsan & Sheikh, Shaya & Huang, Xueqing, 2020. "Data-driven predictive models for residential building energy use based on the segregation of heating and cooling days," Energy, Elsevier, vol. 206(C).
  6. Mobarak Abumohsen & Amani Yousef Owda & Majdi Owda, 2023. "Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms," Energies, MDPI, vol. 16(5), pages 1-31, February.
  7. Hammerschmitt, Bruno Knevitz & Guarda, Fernando Guilherme Kaehler & Lucchese, Felipe Cirolini & Abaide, Alzenira da Rosa, 2022. "Complementary thermal energy generation associated with renewable energies using Artificial Intelligence," Energy, Elsevier, vol. 254(PB).
  8. Li, Pengshun & Zhang, Yi & Zhang, Yi & Zhang, Kai & Jiang, Mengyan, 2021. "The effects of dynamic traffic conditions, route characteristics and environmental conditions on trip-based electricity consumption prediction of electric bus," Energy, Elsevier, vol. 218(C).
  9. Davut Solyali, 2020. "A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus," Sustainability, MDPI, vol. 12(9), pages 1-34, April.
  10. Melillo, Andreas & Durrer, Roman & Worlitschek, Jörg & Schütz, Philipp, 2020. "First results of remote building characterisation based on smart meter measurement data," Energy, Elsevier, vol. 200(C).
  11. 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).
  12. Yang, Yunpeng & Yang, Weixin & Chen, Hongmin & Li, Yin, 2020. "China’s energy whistleblowing and energy supervision policy: An evolutionary game perspective," Energy, Elsevier, vol. 213(C).
  13. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
  14. Chen, Zhiwen & Deng, Qiao & Ren, Hao & Zhao, Zhengrun & Peng, Tao & Yang, Chunhua & Gui, Weihua, 2022. "A new energy consumption prediction method for chillers based on GraphSAGE by combining empirical knowledge and operating data," Applied Energy, Elsevier, vol. 310(C).
  15. 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).
  16. Ahmad, Tanveer & Chen, Huanxin, 2018. "Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment," Energy, Elsevier, vol. 160(C), pages 1008-1020.
  17. 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.
  18. Yuan, Jun & Nian, Victor & He, Junliang & Yan, Wei, 2019. "Cost-effectiveness analysis of energy efficiency measures for maritime shipping using a metamodel based approach with different data sources," Energy, Elsevier, vol. 189(C).
  19. Li, Guannan & Zhan, Lei & Fang, Xi & Gao, Jiajia & Xu, Chengliang & He, Xin & Deng, Jiahui & Xiong, Chenglong, 2024. "Performance comparison on improved data-driven building energy prediction under data shortage scenarios in four perspectives: Data generation, incremental learning, transfer learning, and physics-info," Energy, Elsevier, vol. 312(C).
  20. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
  21. Peplinski, McKenna & Dilkina, Bistra & Chen, Mo & Silva, Sam J. & Ban-Weiss, George A. & Sanders, Kelly T., 2024. "A machine learning framework to estimate residential electricity demand based on smart meter electricity, climate, building characteristics, and socioeconomic datasets," Applied Energy, Elsevier, vol. 357(C).
  22. Hesen Zuo & Wengang Zheng & Mingfei Wang & Xin Zhang, 2023. "Prediction of Heat and Cold Loads of Factory Mushroom Houses Based on EWT Decomposition," Sustainability, MDPI, vol. 15(21), pages 1-19, October.
  23. Shi, Jihao & Li, Junjie & Usmani, Asif Sohail & Zhu, Yuan & Chen, Guoming & Yang, Dongdong, 2021. "Probabilistic real-time deep-water natural gas hydrate dispersion modeling by using a novel hybrid deep learning approach," Energy, Elsevier, vol. 219(C).
  24. Wang, Chao-fan & Liu, Kui-xing & Peng, Jieyang & Li, Xiang & Liu, Xiu-feng & Zhang, Jia-wan & Niu, Zhi-bin, 2025. "High-precision energy consumption forecasting for large office building using a signal decomposition-based deep learning approach," Energy, Elsevier, vol. 314(C).
  25. Ahmad, Tanveer & Huanxin, Chen & Zhang, Dongdong & Zhang, Hongcai, 2020. "Smart energy forecasting strategy with four machine learning models for climate-sensitive and non-climate sensitive conditions," Energy, Elsevier, vol. 198(C).
  26. Ahmad, Tanveer & Zhang, Dongdong & Huang, Chao, 2021. "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, Elsevier, vol. 231(C).
  27. Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine learning-based optimal design of a phase change material integrated renewable system with on-site PV, radiative cooling and hybrid ventilations—study of modelling and application in five clima," Energy, Elsevier, vol. 192(C).
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