A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure
Author
Abstract
Suggested Citation
DOI: 10.1016/j.apenergy.2018.12.065
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Yu, Mengmeng & Hong, Seung Ho, 2017. "Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach," Applied Energy, Elsevier, vol. 203(C), pages 267-279.
- Amy Hillier & Tony Smith & Carolyn C Cannuscio & Allison Karpyn & Karen Glanz, 2015. "A Discrete Choice Approach to Modeling Food Store Access," Environment and Planning B, , vol. 42(2), pages 263-278, April.
- Yu, Mengmeng & Hong, Seung Ho, 2016. "Supply–demand balancing for power management in smart grid: A Stackelberg game approach," Applied Energy, Elsevier, vol. 164(C), pages 702-710.
- Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
- Vuelvas, José & Ruiz, Fredy & Gruosso, Giambattista, 2018. "Limiting gaming opportunities on incentive-based demand response programs," Applied Energy, Elsevier, vol. 225(C), pages 668-681.
- Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
- Zhang, Ni & Yan, Yu & Su, Wencong, 2015. "A game-theoretic economic operation of residential distribution system with high participation of distributed electricity prosumers," Applied Energy, Elsevier, vol. 154(C), pages 471-479.
- Bianchini, Gianni & Casini, Marco & Vicino, Antonio & Zarrilli, Donato, 2016. "Demand-response in building heating systems: A Model Predictive Control approach," Applied Energy, Elsevier, vol. 168(C), pages 159-170.
- Shariatzadeh, Farshid & Mandal, Paras & Srivastava, Anurag K., 2015. "Demand response for sustainable energy systems: A review, application and implementation strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 343-350.
- Jin, Ming & Feng, Wei & Marnay, Chris & Spanos, Costas, 2018. "Microgrid to enable optimal distributed energy retail and end-user demand response," Applied Energy, Elsevier, vol. 210(C), pages 1321-1335.
- Zhu, Lijing & Zhang, Qi & Lu, Huihui & Li, Hailong & Li, Yan & McLellan, Benjamin & Pan, Xunzhang, 2017. "Study on crowdfunding’s promoting effect on the expansion of electric vehicle charging piles based on game theory analysis," Applied Energy, Elsevier, vol. 196(C), pages 238-248.
- Maria Kamargianni & Moshe Ben-Akiva & Amalia Polydoropoulou, 2014. "Incorporating social interaction into hybrid choice models," Transportation, Springer, vol. 41(6), pages 1263-1285, November.
- Huanmei Qin & Jianqiang Gao & Hongzhi Guan & Hongbo Chi, 2017. "Estimating heterogeneity of car travelers on mode shifting behavior based on discrete choice models," Transportation Planning and Technology, Taylor & Francis Journals, vol. 40(8), pages 914-927, November.
- Jin, Ming & Feng, Wei & Liu, Ping & Marnay, Chris & Spanos, Costas, 2017. "MOD-DR: Microgrid optimal dispatch with demand response," Applied Energy, Elsevier, vol. 187(C), pages 758-776.
- Andoni, Merlinda & Robu, Valentin & Früh, Wolf-Gerrit & Flynn, David, 2017. "Game-theoretic modeling of curtailment rules and network investments with distributed generation," Applied Energy, Elsevier, vol. 201(C), pages 174-187.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Xu, Xiaoxiao & Yu, Hao & Sun, Qiuwen & Tam, Vivian W.Y., 2023. "A critical review of occupant energy consumption behavior in buildings: How we got here, where we are, and where we are headed," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
- Liu, Xiaoqi & Lee, Seungjae & Bilionis, Ilias & Karava, Panagiota & Joe, Jaewan & Sadeghi, Seyed Amir, 2021. "A user-interactive system for smart thermal environment control in office buildings," Applied Energy, Elsevier, vol. 298(C).
- Guo, Qingbin & Wang, Yong & Dong, Xiaobin, 2022. "Effects of smart city construction on energy saving and CO2 emission reduction: Evidence from China," Applied Energy, Elsevier, vol. 313(C).
- Hernández, José L. & de Miguel, Ignacio & Vélez, Fredy & Vasallo, Ali, 2024. "Challenges and opportunities in European smart buildings energy management: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
- Tushar, Wayes & Yuen, Chau & Saha, Tapan K. & Morstyn, Thomas & Chapman, Archie C. & Alam, M. Jan E. & Hanif, Sarmad & Poor, H. Vincent, 2021. "Peer-to-peer energy systems for connected communities: A review of recent advances and emerging challenges," Applied Energy, Elsevier, vol. 282(PA).
- Tien, Paige Wenbin & Wei, Shuangyu & Liu, Tianshu & Calautit, John & Darkwa, Jo & Wood, Christopher, 2021. "A deep learning approach towards the detection and recognition of opening of windows for effective management of building ventilation heat losses and reducing space heating demand," Renewable Energy, Elsevier, vol. 177(C), pages 603-625.
- Tien, Paige Wenbin & Wei, Shuangyu & Calautit, John Kaiser & Darkwa, Jo & Wood, Christopher, 2022. "Real-time monitoring of occupancy activities and window opening within buildings using an integrated deep learning-based approach for reducing energy demand," Applied Energy, Elsevier, vol. 308(C).
- Su, Bing & Wang, Shengwei, 2020. "An agent-based distributed real-time optimal control strategy for building HVAC systems for applications in the context of future IoT-based smart sensor networks," Applied Energy, Elsevier, vol. 274(C).
- Jafari, Hamed & Safarzadeh, Soroush & Azad-Farsani, Ehsan, 2022. "Effects of governmental policies on energy-efficiency improvement of hydrogen fuel cell cars: A game-theoretic approach," Energy, Elsevier, vol. 254(PC).
- Ali Ghofrani & Esmat Zaidan & Mohsen Jafari, 2021. "Reshaping energy policy based on social and human dimensions: an analysis of human-building interactions among societies in transition in GCC countries," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-26, December.
- Elnour, Mariam & Fadli, Fodil & Himeur, Yassine & Petri, Ioan & Rezgui, Yacine & Meskin, Nader & Ahmad, Ahmad M., 2022. "Performance and energy optimization of building automation and management systems: Towards smart sustainable carbon-neutral sports facilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
- Rajat Gupta & Sahar Zahiri & Johanna Morey, 2023. "Enhancing User Engagement in Local Energy Initiatives Using Smart Local Energy Engagement Tools: A Meta Study," Energies, MDPI, vol. 16(7), pages 1-25, March.
- Minyoung Lee & Joohyoung Jeon & Hongchul Lee, 2022. "Explainable AI for domain experts: a post Hoc analysis of deep learning for defect classification of TFT–LCD panels," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1747-1759, August.
- Piselli, Cristina & Pisello, Anna Laura, 2019. "Occupant behavior long-term continuous monitoring integrated to prediction models: Impact on office building energy performance," Energy, Elsevier, vol. 176(C), pages 667-681.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
- Lu, Qing & Lü, Shuaikang & Leng, Yajun, 2019. "A Nash-Stackelberg game approach in regional energy market considering users’ integrated demand response," Energy, Elsevier, vol. 175(C), pages 456-470.
- Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
- Lu, Renzhi & Hong, Seung Ho & Zhang, Xiongfeng, 2018. "A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach," Applied Energy, Elsevier, vol. 220(C), pages 220-230.
- Zhou, Kaile & Peng, Ning & Yin, Hui & Hu, Rong, 2023. "Urban virtual power plant operation optimization with incentive-based demand response," Energy, Elsevier, vol. 282(C).
- Abdulaal, Ahmed & Moghaddass, Ramin & Asfour, Shihab, 2017. "Two-stage discrete-continuous multi-objective load optimization: An industrial consumer utility approach to demand response," Applied Energy, Elsevier, vol. 206(C), pages 206-221.
- Lu, Qing & Yu, Hao & Zhao, Kangli & Leng, Yajun & Hou, Jianchao & Xie, Pinjie, 2019. "Residential demand response considering distributed PV consumption: A model based on China's PV policy," Energy, Elsevier, vol. 172(C), pages 443-456.
- Zheng, Ling & Zhou, Bin & Cao, Yijia & Wing Or, Siu & Li, Yong & Wing Chan, Ka, 2022. "Hierarchical distributed multi-energy demand response for coordinated operation of building clusters," Applied Energy, Elsevier, vol. 308(C).
- Golpîra, Hêriş, 2020. "Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach," Energy, Elsevier, vol. 209(C).
- Bhatti, Bilal Ahmad & Broadwater, Robert, 2019. "Energy trading in the distribution system using a non-model based game theoretic approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
- Wen, Lulu & Zhou, Kaile & Li, Jun & Wang, Shanyong, 2020. "Modified deep learning and reinforcement learning for an incentive-based demand response model," Energy, Elsevier, vol. 205(C).
- Dutton, Spencer & Marnay, Chris & Feng, Wei & Robinson, Matthew & Mammoli, Andrea, 2019. "Moore vs. Murphy: Tradeoffs between complexity and reliability in distributed energy system scheduling using software-as-a-service," Applied Energy, Elsevier, vol. 238(C), pages 1126-1137.
- Zhang, Xiaoyan & Zhu, Shanying & He, Jianping & Yang, Bo & Guan, Xinping, 2019. "Credit rating based real-time energy trading in microgrids," Applied Energy, Elsevier, vol. 236(C), pages 985-996.
- Luo, Zhe & Hong, SeungHo & Ding, YueMin, 2019. "A data mining-driven incentive-based demand response scheme for a virtual power plant," Applied Energy, Elsevier, vol. 239(C), pages 549-559.
- Xu, Bo & Wang, Jiexin & Guo, Mengyuan & Lu, Jiayu & Li, Gehui & Han, Liang, 2021. "A hybrid demand response mechanism based on real-time incentive and real-time pricing," Energy, Elsevier, vol. 231(C).
- Zheng, Lingwei & Zhou, Xingqiu & Qiu, Qi & Yang, Lan, 2020. "Day-ahead optimal dispatch of an integrated energy system considering time-frequency characteristics of renewable energy source output," Energy, Elsevier, vol. 209(C).
- Bhatti, Bilal Ahmad & Broadwater, Robert, 2020. "Distributed Nash Equilibrium Seeking for a Dynamic Micro-grid Energy Trading Game with Non-quadratic Payoffs," Energy, Elsevier, vol. 202(C).
- Fan, Songli & Ai, Qian & Piao, Longjian, 2018. "Bargaining-based cooperative energy trading for distribution company and demand response," Applied Energy, Elsevier, vol. 226(C), pages 469-482.
- Jiang, Qian & Mu, Yunfei & Jia, Hongjie & Cao, Yan & Wang, Zibo & Wei, Wei & Hou, Kai & Yu, Xiaodan, 2022. "A Stackelberg Game-based planning approach for integrated community energy system considering multiple participants," Energy, Elsevier, vol. 258(C).
- Wang, Yongli & Liu, Zhen & Wang, Jingyan & Du, Boxin & Qin, Yumeng & Liu, Xiaoli & Liu, Lin, 2023. "A Stackelberg game-based approach to transaction optimization for distributed integrated energy system," Energy, Elsevier, vol. 283(C).
More about this item
Keywords
Artificial intelligence for humans-in-the-loop cyber-physical systems; Human-building interaction; Deep learning; Discrete choice models; Game theory;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:237:y:2019:i:c:p:810-821. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.