Machine learning for estimation of building energy consumption and performance: a review
作者: Saleh SeyedzadehFarzad Pour RahimianIvan GleskMarc Roper
作者单位: 1Faculty of Engineering, University of Strathclyde
2Faculty of Engineering & Environment, Northumbria University
3Faculty of Computer and Information Sciences, University of Strathclyde
刊名: Visualization in Engineering, 2018, Vol.6 (1), pp.1-20
来源数据库: Springer Nature Journal
DOI: 10.1186/s40327-018-0064-7
关键词: Building energy consumptionBuilding energy efficiencyEnergy benchmarkingMachine learning
英文摘要: Abstract(#br)Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy efficiency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most effective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy efficiency at a very early design stage. On the other hand,efficient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, artificial intelligence (AI) in general and machine learning...
全文获取路径: Springer Nature  (合作)