E3S Web Conf.
Volume 246, 2021Cold Climate HVAC & Energy 2021
|Number of page(s)||6|
|Section||Measured Energy Use|
|Published online||29 March 2021|
- P. O. of the E. Union, “Going climate-neutral by 2050 : a strategic long-term vision for a prosperous, modern, competitive and climate-neutral EU economy.,” Jul. 16, 2019. http://op.europa.eu/en/publication-detail/-/publication/92f6d5bc-76bc-11e9-9f05-01aa75ed71a1 (accessed Feb. 01, 2021). [Google Scholar]
- X. Cao, X. Dai, and J. Liu, “Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade,” Energy Build., vol. 128, pp. 198–213, Sep. 2016, doi: 10.1016/j.enbuild.2016.06.089. [Google Scholar]
- P. Bertoldi, “GreenBuilding: Enhanced Energy Efficiency for Non-residential Buildings,” EU Science Hub - European Commission, Jul. 30, 2012. https://ec.europa.eu/jrc/en/publication/thematic-reports/greenbuilding-enhanced-energy-efficiency-non-residential-buildings (accessed Feb. 01, 2021). [Google Scholar]
- Y. Ding, Q. Zhang, T. Yuan, and F. Yang, “Effect of input variables on cooling load prediction accuracy of an office building,” Appl. Therm. Eng., vol. 128, pp. 225–234, Jan. 2018, doi: 10.1016/j.applthermaleng.2017.09.007. [Google Scholar]
- Y. Ding, Q. Zhang, T. Yuan, and K. Yang, “Model input selection for building heating load prediction: A case study for an office building in Tianjin,” Energy Build., vol. 159, pp. 254–270, Jan. 2018, doi: 10.1016/j.enbuild.2017.11.002. [Google Scholar]
- Y. Wei et al., “Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks,” Appl. Energy, vol. 240, pp. 276–294, Apr. 2019, doi: 10.1016/j.apenergy.2019.02.056. [Google Scholar]
- H. Zhong, J. Wang, H. Jia, Y. Mu, and S. Lv, “Vector field-based support vector regression for building energy consumption prediction,” Appl. Energy, vol. 242, pp. 403–414, May 2019. [Google Scholar]
- J. Joe and P. Karava, “A model predictive control strategy to optimize the performance of radiant floor heating and cooling systems in office buildings,” Appl. Energy, vol. 245, pp. 65–77, Jul. 2019. [Google Scholar]
- G. Darivianakis, A. Georghiou, R. S. Smith, and J. Lygeros, “The Power of Diversity: Data-Driven Robust Predictive Control for Energy Efficient Buildings and Districts,” ArXiv160705441 Cs Math, Jul. 2016, Accessed: Feb. 01, 2021. [Online]. Available: http://arxiv.org/abs/1607.05441. [Google Scholar]
- K. Sun, T. Hong, S. C. Taylor-Lange, and M. A. Piette, “A pattern-based automated approach to building energy model calibration,” Appl. Energy, vol. 165, pp. 214–224, Mar. 2016. [Google Scholar]
- Y. Zhang, X. Bai, F. P. Mills, and J. C. V. Pezzey, “Rethinking the role of occupant behavior in building energy performance: A review,” Energy Build., vol. 172, pp. 279–294, Aug. 2018. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.