[1]
|
Department of Climate Change (2015) Enhanced Actions of Climate Change: China’s Intended Nationally Determined Contributions. National Development & Reform Commission of China.
http://www4.unfccc.int/submissions/indc/Submission%20Pages/submissions.aspx
|
[2]
|
孙弘历, 林波荣, 王者, 林智荣. 成都地区居住建筑不同供暖末端能耗与满意率调研[J]. 暖通空调, 2018, 48(2): 30-34+96.
|
[3]
|
Chen, S., Li, N., Yoshino, H., Guan, J. and Levine, M.D. (2011) Statistical Analyses on Winter Energy Consumption Characteristics of Residential Buildings in Some Cities of China. Energy and Buildings, 43, 1063-1070.
https://doi.org/10.1016/j.enbuild.2010.09.022
|
[4]
|
柴盼, 刘旭, 翁庙成, 刘方. 重庆某住宅小区能耗特征与节能潜力研究[J]. 制冷与空调(四川), 2014(6): 694-697.
|
[5]
|
Chen, J., Wang, X. and Steemers, K. (2013) A Statistical Analysis of a Residential Energy Consumption Survey Study in Hangzhou, China. Energy and Buildings, 66, 193-202. https://doi.org/10.1016/j.enbuild.2013.07.045
|
[6]
|
唐峰, 王晓磊, 罗一哲, 周琪. 夏热冬冷地区住宅建筑能耗长期测试及使用行为模拟分析[J]. 建筑节能, 2016, 44(4): 104-107.
|
[7]
|
余晓平, 付祥钊, 廖小烽. 浅析夏热冬冷地区低能耗住宅技术路线[J]. 重庆建筑大学学报, 2008, 30(6): 116-119, 45.
|
[8]
|
邱童, 徐强, 王博, 胡立峰. 夏热冬冷地区城镇居住建筑能耗水平分析[J]. 建筑科学, 2013, 29(6): 23-26.
|
[9]
|
D’hulstr, Labeeuw, W., Beusen, B., et al. (2015) Demand Response Flexibility and Flexibility Potential of Residential Smart Appliances: Experiences from Large Pilot Test in Belgium. Applied Energy, 155, 79-90.
https://doi.org/10.1016/j.apenergy.2015.05.101
|
[10]
|
高钢烽. 夏热冬冷地区住宅节能改造能耗模拟分析[J]. 低温建筑技术, 2009(10): 115-117.
|
[11]
|
刘倩, 张旭. 上海某住宅建筑围护结构能耗模拟与节能性分析[J]. 建筑科学, 2007(12): 24-26, 38.
|
[12]
|
刘祥, 邱烷南, 张华玲. 重庆地区现有居住实态下住宅的能耗研究[J]. 制冷与空调(四川), 2014(6): 623-627.
|
[13]
|
肖书博, 李念平. 夏热冬冷地区节能建筑能耗模拟与实测数据对比分析[C]//全国暖通空调制冷2010年学术年会. 杭州, 2010: 295.
|
[14]
|
袁玥. 基于机器学习的办公建筑暖通空调系统能耗预测及优化调度[D]: [硕士学位论文]. 武汉: 华中科技大学, 2019.
|
[15]
|
高庆龙, 杨柳, 刘加平, 冯雅. 居住建筑外墙传热系数优化研究[J]. 四川建筑科学研究, 2009, 35(1): 245-248.
|
[16]
|
唐鸣放, 窦枚, 王科. 重庆旧居住区建筑节能改造技术方案及效果分析[J]. 建筑技术, 2011, 42(10): 939-941.
|
[17]
|
高英博, 顾中煊, 罗淑湘, 等. 能耗预测导向的建筑能耗异常数据识别与修复[J]. 科学技术与工程, 2019, 19(35): 298-304.
|
[18]
|
吴蔚沁. 基于机器学习算法的建筑能耗监测数据异常识别及修复方法[J]. 建设科技, 2017(9): 60-62.
|
[19]
|
崔治国, 曹勇, 武根峰, 刘辉, 仇志飞, 陈传玮. 基于机器学习算法的建筑能耗监测数据预处理技术研究[J]. 建筑科学, 2018, 34(2): 94-99.
|
[20]
|
肖赋, 范成, 王盛卫. 基于数据挖掘技术的建筑系统性能诊断和优化[J]. 化工学报, 2014, 65(S2): 181-187.
|
[21]
|
杨石, 罗淑湘, 杜明. 基于数据挖掘的公共建筑能耗监管平台数据处理方法[J]. 暖通空调, 2015, 45(2): 82-86.
|
[22]
|
谢宜鑫. 基于机器学习的建筑空调能耗数据挖掘和模式识别[D]: [硕士学位论文]. 北京: 北京交通大学, 2019.
|
[23]
|
田玮, 魏来, 李占勇, 孟庆新, 宋继田, 杨松. 基于机器学习的建筑能耗模型适用性研究[J]. 天津科技大学学报, 2016, 31(3): 54-59.
|
[24]
|
丁子祥. 基于机器学习方法的建筑能耗预测研究[D]: [硕士学位论文]. 济南: 山东建筑大学, 2018.
|
[25]
|
Robinson, C., Dilkina, B., Hubbs, J., et al. (2017) Machine Learning Approaches for Estimating Commercial Building Energy Consumption. Applied Energy, 208, 889-904. https://doi.org/10.1016/j.apenergy.2017.09.060
|
[26]
|
Lam, J.C. and Li, D.H.W. (2003) Electricity Consumption Characteristics in Shopping Malls in Subtropical Climates. Energy Conversion and Management, 44, 1391-1398. https://doi.org/10.1016/S0196-8904(02)00167-X
|
[27]
|
刘文凤. 数据挖掘在公共建筑能耗分析中的应用研究[D]: [博士学位论文]. 重庆: 重庆大学, 2010.
|
[28]
|
邬棋帆. 医院建筑能耗分析诊断模型研究[D]: [硕士学位论文]. 西安: 西安建筑科技大学, 2018.
|
[29]
|
Javed, A., Larijani, H. and Wixted, A. (2018) Improving Energy Consumption of a Commercial Building with IoT and Machine Learning. IT Professional, 20, 30-38. https://doi.org/10.1109/MITP.2018.053891335
|
[30]
|
Rahman, A., Srikumar, V. and Smith, A.D. (2018) Predicting Electricity Consumption for Commercial and Residential Buildings Using Deep Recurrent Neural Networks. Applied En-ergy, 212, 372-385.
https://doi.org/10.1016/j.apenergy.2017.12.051
|
[31]
|
Deng, H.F., Fannon, D. and Eckelman, M.J. (2018) Predictive Modeling for US Commercial Building Energy Use: A Comparison of Existing Statistical and Machine Learning Algo-rithms Using CBECS Microdata. Energy and Buildings, 163, 34-43. https://doi.org/10.1016/j.enbuild.2017.12.031
|
[32]
|
Samir, T., Jessica, G. and Samuel, F. (2018) Gradient Boosting Machine for Modeling the Energy Consumption of Commercial Buildings. Energy and Buildings, 158, 1533-1543. https://doi.org/10.1016/j.enbuild.2017.11.039
|
[33]
|
Naji, S., Keivani, A., Shamshirband, S., et al. (2016) Estimating Building Energy Consumption Using Extreme Learning Machine Method. Energy, 97, 506-516. https://doi.org/10.1016/j.energy.2015.11.037
|
[34]
|
程亚豪, 陈焕新, 王江宇. 基于机器学习的住宅能耗预测[J]. 制冷与空调, 2019, 19(5): 35-40.
|
[35]
|
李信仪. 居住建筑区域能耗模型研究[D]: [博士学位论文]. 重庆: 重庆大学, 2018.
|
[36]
|
Vázquez-Canteli, J.R., Ulyanin, S., Kämpf, J., et al. (2019) Fusing Tensor Flow with Building Energy Sim-ulation for Intelligent Energy Management in Smart Cities. Sustainable Cities and Society, 45, 243-257.
https://doi.org/10.1016/j.scs.2018.11.021
|
[37]
|
Bourhnane, S., et al. (2020) Machine Learning for Energy Con-sumption Prediction and Scheduling in Smart Buildings. SN Applied Sciences, 2, Article No. 297.
|
[38]
|
Pham, A.-D., Ngo, N.-T., Truong, T.T.H., Huynh, N.-T. and Truong, N.-S. (2020) Predicting Energy Consumption in Multiple Build-ings Using Machine Learning for Improving Energy Efficiency and Sustainability. Journal of Cleaner Production, 260, Article ID: 121082. https://doi.org/10.1016/j.jclepro.2020.121082
|
[39]
|
Zeng, A., Ho, H. and Yu, Y. (2020) Predic-tion of Building Electricity Usage Using Gaussian Process Regression. Journal of Building Engineering, 28, Article ID: 101054. https://doi.org/10.1016/j.jobe.2019.101054
|