IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v207y2017icp324-335.html
   My bibliography  Save this item

Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Sivaneasan, Balakrishnan & Kandasamy, Nandha Kumar & Lim, May Lin & Goh, Kwang Ping, 2018. "A new demand response algorithm for solar PV intermittency management," Applied Energy, Elsevier, vol. 218(C), pages 36-45.
  2. Smarra, Francesco & Jain, Achin & de Rubeis, Tullio & Ambrosini, Dario & D’Innocenzo, Alessandro & Mangharam, Rahul, 2018. "Data-driven model predictive control using random forests for building energy optimization and climate control," Applied Energy, Elsevier, vol. 226(C), pages 1252-1272.
  3. Zhang, Xiang & Saelens, Dirk & Roels, Staf, 2022. "Estimating dynamic solar gains from on-site measured data: An ARX modelling approach," Applied Energy, Elsevier, vol. 321(C).
  4. Marwan, Marwan, 2020. "The impact of probability of electricity price spike and outside temperature to define total expected cost for air conditioning," Energy, Elsevier, vol. 195(C).
  5. da Fonseca, André L.A. & Chvatal, Karin M.S. & Fernandes, Ricardo A.S., 2021. "Thermal comfort maintenance in demand response programs: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
  6. Hu, Maomao & Xiao, Fu & Wang, Shengwei, 2021. "Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
  7. Wang, C. & Zhu, Y. & Qu, J. & Hu, H.D., 2018. "Automatic air temperature control in a container with an optic-variable wall," Applied Energy, Elsevier, vol. 224(C), pages 671-681.
  8. Ding, Yan & Lyu, Yacong & Lu, Shilei & Wang, Ran, 2022. "Load shifting potential assessment of building thermal storage performance for building design," Energy, Elsevier, vol. 243(C).
  9. Lim, Dae Kyu & Ahn, Byoung Ha & Jeong, Ji Hwan, 2018. "Method to control an air conditioner by directly measuring the relative humidity of indoor air to improve the comfort and energy efficiency," Applied Energy, Elsevier, vol. 215(C), pages 290-299.
  10. Rama Curiel, José Adrián & Thakur, Jagruti, 2022. "A novel approach for Direct Load Control of residential air conditioners for Demand Side Management in developing regions," Energy, Elsevier, vol. 258(C).
  11. Hu, Maomao & Xiao, Fu, 2018. "Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm," Applied Energy, Elsevier, vol. 219(C), pages 151-164.
  12. Wei, Zhichen & Calautit, John, 2023. "Predictive control of low-temperature heating system with passive thermal mass energy storage and photovoltaic system: Impact of occupancy patterns and climate change," Energy, Elsevier, vol. 269(C).
  13. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
  14. Sui, Quan & Wei, Fanrong & Zhang, Rui & Lin, Xiangning & Tong, Ning & Wang, Zhixun & Li, Zhengtian, 2019. "Optimal use of electric energy oriented water-electricity combined supply system for the building-integrated-photovoltaics community," Applied Energy, Elsevier, vol. 247(C), pages 549-558.
  15. Yoon, Ah-Yun & Kim, Young-Jin & Zakula, Tea & Moon, Seung-Ill, 2020. "Retail electricity pricing via online-learning of data-driven demand response of HVAC systems," Applied Energy, Elsevier, vol. 265(C).
  16. Pang, Wei & Yu, Hongwen & Zhang, Yongzhe & Yan, Hui, 2019. "Solar photovoltaic based air cooling system for vehicles," Renewable Energy, Elsevier, vol. 130(C), pages 25-31.
  17. Zhang, Xu & Sun, Yongjun & Gao, Dian-ce & Zou, Wenke & Fu, Jianping & Ma, Xiaowen, 2022. "Similarity-based grouping method for evaluation and optimization of dataset structure in machine-learning based short-term building cooling load prediction without measurable occupancy information," Applied Energy, Elsevier, vol. 327(C).
  18. Kirchem, Dana & Lynch, Muireann Á & Casey, Eoin & Bertsch, Valentin, 2019. "Demand response within the energy-for-water-nexus: A review," Papers WP637, Economic and Social Research Institute (ESRI).
  19. Wang, Huilong & Wang, Shengwei, 2021. "A hierarchical optimal control strategy for continuous demand response of building HVAC systems to provide frequency regulation service to smart power grids," Energy, Elsevier, vol. 230(C).
  20. Simon Heslop & Baran Yildiz & Mike Roberts & Dong Chen & Tim Lau & Shayan Naderi & Anna Bruce & Iain MacGill & Renate Egan, 2022. "A Novel Temperature-Independent Model for Estimating the Cooling Energy in Residential Homes for Pre-Cooling and Solar Pre-Cooling," Energies, MDPI, vol. 15(23), pages 1-18, December.
  21. Li, Kangping & Li, Zhenghui & Huang, Chunyi & Ai, Qian, 2024. "Online transfer learning-based residential demand response potential forecasting for load aggregator," Applied Energy, Elsevier, vol. 358(C).
  22. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
  23. Hu, Maomao & Xiao, Fu & Jørgensen, John Bagterp & Wang, Shengwei, 2019. "Frequency control of air conditioners in response to real-time dynamic electricity prices in smart grids," Applied Energy, Elsevier, vol. 242(C), pages 92-106.
  24. Yang, Wangwang & Shi, Jing & Li, Shujian & Song, Zhaofang & Zhang, Zitong & Chen, Zexu, 2022. "A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior," Applied Energy, Elsevier, vol. 307(C).
  25. Hu, Maomao & Xiao, Fu, 2020. "Quantifying uncertainty in the aggregate energy flexibility of high-rise residential building clusters considering stochastic occupancy and occupant behavior," Energy, Elsevier, vol. 194(C).
  26. Schmidt, Mischa & Åhlund, Christer, 2018. "Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 742-756.
  27. Li, Sihui & Peng, Jinqing & Wang, Meng & Wang, Kai & Li, Houpei & Lu, Chujie, 2024. "Approaching nearly zero energy of PV direct air conditioners by integrating building design, load flexibility and PCM," Renewable Energy, Elsevier, vol. 221(C).
  28. Malik, Anam & Haghdadi, Navid & MacGill, Iain & Ravishankar, Jayashri, 2019. "Appliance level data analysis of summer demand reduction potential from residential air conditioner control," Applied Energy, Elsevier, vol. 235(C), pages 776-785.
  29. Meng, Qinglong & Wei, Ying'an & Fan, Jingjing & Li, Yanbo & Zhao, Fan & Lei, Yu & Sun, Hang & Jiang, Le & Yu, Lingli, 2024. "Peak regulation strategies for ground source heat pump demand response of based on load forecasting: A case study of rural building in China," Renewable Energy, Elsevier, vol. 224(C).
  30. Wang, Lingshi & Liu, Xiaobing & Yang, Zhiyao & Gluesenkamp, Kyle R., 2020. "Experimental study on a novel three-phase absorption thermal battery with high energy density applied to buildings," Energy, Elsevier, vol. 208(C).
  31. Li, Yanfei & O'Neill, Zheng & Zhang, Liang & Chen, Jianli & Im, Piljae & DeGraw, Jason, 2021. "Grey-box modeling and application for building energy simulations - A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
  32. Milad Afzalan & Farrokh Jazizadeh, 2021. "Quantification of Demand-Supply Balancing Capacity among Prosumers and Consumers: Community Self-Sufficiency Assessment for Energy Trading," Energies, MDPI, vol. 14(14), pages 1-21, July.
  33. Zhang, Zhihui & Jing, Rui & Lin, Jian & Wang, Xiaonan & van Dam, Koen H. & Wang, Meng & Meng, Chao & Xie, Shan & Zhao, Yingru, 2020. "Combining agent-based residential demand modeling with design optimization for integrated energy systems planning and operation," Applied Energy, Elsevier, vol. 263(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.