Multi-objective optimization for energy consumption, daylighting and thermal comfort performance of rural tourism buildings in north China
暂无分享,去创建一个
Yong Sun | Li Zhu | Binghua Wang | Li Zhu | Yong Sun | Binghua Wang
[1] Y. Shahbazi,et al. An early-stage design optimization for office buildings’ façade providing high-energy performance and daylight , 2019, Indoor and Built Environment.
[2] Li Yang,et al. Building energy efficiency in China rural areas: Situation, drawbacks, challenges, corresponding measures and policies , 2014 .
[3] Ondrej Krejcar,et al. Multi-objective energy and daylight optimization of amorphous shading devices in buildings , 2019, Solar Energy.
[4] Toke Rammer Nielsen,et al. Building energy optimization in the early design stages: A simplified method , 2015 .
[5] Zhujun Jiang,et al. Energy consumption in China's rural areas: A study based on the village energy survey , 2017 .
[6] Di Wang,et al. Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design , 2015 .
[7] Jing Liu,et al. Building energy efficiency in rural China , 2014 .
[8] Mehmet Fatih Tasgetiren,et al. OPTIMUS: Self-Adaptive Differential Evolution with Ensemble of Mutation Strategies for Grasshopper Algorithmic Modeling , 2019, Algorithms.
[9] Farshad Kheiri,et al. A review on optimization methods applied in energy-efficient building geometry and envelope design , 2018, Renewable and Sustainable Energy Reviews.
[10] John Mardaljevic,et al. Dynamic Daylight Performance Metrics for Sustainable Building Design , 2006 .
[11] Manolis Maragoudakis,et al. Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data , 2019, Algorithms.
[12] Kyle Konis,et al. Passive performance and building form: An optimization framework for early-stage design support , 2016 .
[13] David Moreno,et al. Design optimisation of perforated solar façades in order to balance daylighting with thermal performance , 2017 .
[14] Benjamín Barán,et al. Performance metrics in multi-objective optimization , 2015, 2015 Latin American Computing Conference (CLEI).
[15] John Mardaljevic,et al. Useful daylight illuminances: A replacement for daylight factors , 2006 .
[16] Jan Carmeliet,et al. Building energy optimization: An extensive benchmark of global search algorithms , 2019, Energy and Buildings.
[17] Ilker Etikan,et al. Sampling and Sampling Methods , 2017 .
[18] Jessica Granderson,et al. Assessment of Automated Measurement and Verification (M&V) Methods , 2015 .
[19] Jack P. C. Kleijnen,et al. EUROPEAN JOURNAL OF OPERATIONAL , 1992 .
[20] Yi-Ming Wei,et al. Residential energy-related carbon emissions in urban and rural China during 1996–2012: From the perspective of five end-use activities , 2015 .
[21] B. Lane. Rural Tourism: an Overview , 2009 .
[22] Donatien Njomo,et al. Thermal comfort: A review paper , 2010 .
[23] Son H. Kim,et al. China's building energy demand: Long-term implications from a detailed assessment , 2012 .
[24] Marco Manzan,et al. FAST energy and daylight optimization of an office with fixed and movable shading devices , 2017 .
[25] Xu Juan,et al. Analysis on energy consumption of rural building based on survey in northern China , 2018, Energy for Sustainable Development.
[26] Chengcheng Xu,et al. Thermal comfort and thermal adaptive behaviours in traditional dwellings: A case study in Nanjing, China , 2018, Building and Environment.
[27] Yi Wang,et al. A multi-objective optimization methodology for window design considering energy consumption, thermal environment and visual performance , 2019, Renewable Energy.
[28] Marco Laumanns,et al. SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .
[29] Daniel Uribe,et al. Optimization of a fixed exterior complex fenestration system considering visual comfort and energy performance criteria , 2017 .
[30] Z. Zhuang,et al. Field survey on indoor thermal environment of rural residences with coupled Chinese kang and passive solar collecting wall heating in Northeast China , 2007 .
[31] Ertunga C. Özelkan,et al. Bi-objective optimization of building enclosure design for thermal and lighting performance , 2015 .
[32] S. Pan,et al. Advances and challenges in sustainable tourism toward a green economy. , 2018, The Science of the total environment.
[33] Rattan Lal,et al. Structural change and carbon emission of rural household energy consumption in Huantai, northern China , 2013 .
[34] Thomas Wortmann,et al. Genetic evolution vs. function approximation: Benchmarking algorithms for architectural design optimization , 2018, J. Comput. Des. Eng..
[35] Nguyen Tan Dzung. Application of Multi-Objective Optimization by the Utopian Point Method to Determining the Technological Mode of Gac Oil Extraction , 2012 .
[36] Farshad Kowsary,et al. Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO) , 2016 .
[37] Michela Turrin,et al. Performative computational architecture using swarm and evolutionary optimisation: A review , 2019, Building and Environment.
[38] Shuo Li,et al. Facade design optimization for naturally ventilated residential buildings in Singapore , 2007 .
[39] Jean-Louis Scartezzini,et al. Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand , 2017, Energy and Buildings.
[40] Soolyeon Cho,et al. Design optimization of building geometry and fenestration for daylighting and energy performance , 2019, Solar Energy.
[41] Yufei Tan,et al. A glazed transpired solar wall system for improving indoor environment of rural buildings in northeast China , 2016 .
[42] Daisuke Sumiyoshi,et al. A simplified model for dynamic analysis of the indoor thermal environment of rooms with a Chinese kang , 2017 .
[43] Zahra Jalali,et al. Design and optimization of form and facade of an office building using the genetic algorithm , 2020, Science and Technology for the Built Environment.