Machine learning-based cooling load prediction and optimal control for mechanical ventilative cooling in high-rise buildings
暂无分享,去创建一个
[1] Qingyan Chen,et al. Impact of climate change heating and cooling energy use in buildings in the United States , 2014 .
[2] U. Berardi,et al. Assessing the impact of climate change on building heating and cooling energy demand in Canada , 2020, Renewable and Sustainable Energy Reviews.
[3] Jn Hacker,et al. Constructing design weather data for future climates , 2005 .
[4] Ali Malkawi,et al. Estimating natural ventilation potential for high-rise buildings considering boundary layer meteorology , 2017 .
[5] Martin Belusko,et al. Modelling the cooling energy of night ventilation and economiser strategies on façade selection of commercial buildings , 2013 .
[6] Andreas K. Athienitis,et al. Experimental study of the thermal performance of a large institutional building with mixed-mode cooling and hybrid ventilation , 2012 .
[7] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[8] Lisa Guan,et al. Preparation of future weather data to study the impact of climate change on buildings , 2009 .
[9] Sašo Medved,et al. Generalized model-based predictive weather control for the control of free cooling by enhanced night-time ventilation , 2016 .
[10] Gail Brager,et al. Performance, prediction, optimization, and user behavior of night ventilation , 2018 .
[11] Sung-Bae Cho,et al. Predicting residential energy consumption using CNN-LSTM neural networks , 2019, Energy.
[12] Andreas K. Athienitis,et al. A study of hybrid ventilation in an institutional building for predictive control , 2018 .
[13] Jie Zhao,et al. EnergyPlus model-based predictive control within design–build–operate energy information modelling infrastructure , 2015 .
[14] Mary Ann Piette,et al. Building thermal load prediction through shallow machine learning and deep learning , 2020, Applied Energy.
[15] Dahai Qi,et al. Investigation of mechanical ventilation for cooling in high-rise buildings , 2020 .
[16] B. Olesen,et al. Using thermostats for indoor climate control in offices: The effect on thermal comfort and heating/cooling energy use , 2019, Energy and Buildings.
[17] Shengwei Wang,et al. Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques , 2014 .
[18] Abolfazl Hayati,et al. On the Performance of Night Ventilation in a Historic Office Building in Nordic Climate , 2020, Energies.
[19] Jiejin Cai,et al. Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks , 2009 .
[20] Maria Kolokotroni,et al. Cooling-energy reduction in air-conditioned offices by using night ventilation , 1999 .
[21] Xiaoshu Lü,et al. A novel dynamic modeling approach for predicting building energy performance , 2014 .
[22] Fu Xiao,et al. Data mining in building automation system for improving building operational performance , 2014 .
[23] Xiaohong Liu,et al. Prediction of the impacts of climate change on energy consumption for a medium-size office building with two climate models , 2017 .
[24] Saifur Rahman,et al. Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques , 2019, Applied Energy.
[25] Dahai Qi,et al. A Review of High-Rise Ventilation for Energy Efficiency and Safety , 2020 .
[26] Jiejin Cai,et al. Applying support vector machine to predict hourly cooling load in the building , 2009 .
[27] Joaquim Melendez,et al. Short-term load forecasting in a non-residential building contrasting models and attributes , 2015 .
[28] Xin Wang,et al. Optimization of night mechanical ventilation strategy in summer for cooling energy saving based on inverse problem method , 2018 .
[29] Fu Xiao,et al. A short-term building cooling load prediction method using deep learning algorithms , 2017 .
[30] Chen Ren,et al. Development and application of linear ventilation and temperature models for indoor environmental prediction and HVAC systems control , 2019, Sustainable Cities and Society.
[31] Nabil Nassif,et al. A robust CO2-based demand-controlled ventilation control strategy for multi-zone HVAC systems , 2012 .
[32] Mohammad. Rasul,et al. Energy conservation measures in an institutional building in sub-tropical climate in Australia , 2010 .
[33] Andreas Athienitis,et al. Multizone modelling of a hybrid ventilated high-rise building based on full-scale measurements for predictive control , 2019, Indoor and Built Environment.
[34] Yang Zhao,et al. Deep learning-based feature engineering methods for improved building energy prediction , 2019, Applied Energy.
[35] Ngoc-Tri Ngo. Early predicting cooling loads for energy-efficient design in office buildings by machine learning , 2019, Energy and Buildings.
[36] J. Lukes,et al. Impact of global warming on performance of ground source heat pumps in US climate zones , 2015 .
[37] Sašo Medved,et al. Multi-objective optimization of a building free cooling system, based on weather prediction , 2012 .
[38] Jianjun Hu,et al. Model predictive control strategies for buildings with mixed-mode cooling , 2014 .
[39] Michael Donn,et al. Energy use and height in office buildings , 2018, Building Research & Information.
[40] Eric Wai Ming Lee,et al. An intelligent approach to assessing the effect of building occupancy on building cooling load predi , 2011 .
[41] Lorenz T. Biegler,et al. Dynamic optimization based integrated operation strategy design for passive cooling ventilation and active building air conditioning , 2014 .
[42] Nora El-Gohary,et al. A review of data-driven building energy consumption prediction studies , 2018 .
[43] Sašo Medved,et al. Parametric study on the advantages of weather-predicted control algorithm of free cooling ventilation system , 2014 .
[44] Xiangjiang Zhou,et al. Optimal operation of a large cooling system based on an empirical model , 2004 .
[45] Fu-Sheng Gao,et al. Night ventilation control strategies in office buildings , 2009 .
[46] V. Pope,et al. The impact of new physical parametrizations in the Hadley Centre climate model: HadAM3 , 2000 .
[47] Sedat Akkurt,et al. Artificial neural networks applications in building energy predictions and a case study for tropical climates , 2005 .
[48] C A Short,et al. Fire and smoke control in naturally ventilated buildings , 2006 .
[49] Pan Dongmei,et al. Forecasting performance comparison of two hybrid machine learning models for cooling load of a large-scale commercial building , 2019, Journal of Building Engineering.