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 Residential Building Energy Consumption: a Review of Energy Data Availability, Characteristics, and Energy Performance Prediction Methods
Tác giả hoặc Nhóm tác giả: Huyen Do, Kristen S Cetin
Nơi đăng: Current Sustainable/Renewable Energy Reports; Số: 5;Từ->đến trang: 76-85;Năm: 2018
Lĩnh vực: Kỹ thuật; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
Purpose of ReviewResidential energy performance prediction has historically received less attention, as compared to commercial buildings. This likely is in part due to the limited availability of residential energy data, as well as the relative challenge of predicting energy consumption of buildings that are more highly dependent on occupant behavior. The purpose of this effort is to assess the types and characteristics of energy and non-energy data available for algorithm developed and methods that have been developed to predict residential consumption.Recent FindingsWhile there are several large residential building energy datasets, data availability is still generally very limited. Most energy prediction methods used recently include data-driven approaches, as well as combinations of multiple methods; however, many methods have not been tested for residential buildings, or at a range of energy data frequencies.SummaryThe literature points to the need for the availability of more residential building data sources to be able to assess and improve models, and further testing is needed including those models that have not yet been significantly used for residential buildings.
ABSTRACT
Purpose of ReviewResidential energy performance prediction has historically received less attention, as compared to commercial buildings. This likely is in part due to the limited availability of residential energy data, as well as the relative challenge of predicting energy consumption of buildings that are more highly dependent on occupant behavior. The purpose of this effort is to assess the types and characteristics of energy and non-energy data available for algorithm developed and methods that have been developed to predict residential consumption.Recent FindingsWhile there are several large residential building energy datasets, data availability is still generally very limited. Most energy prediction methods used recently include data-driven approaches, as well as combinations of multiple methods; however, many methods have not been tested for residential buildings, or at a range of energy data frequencies.SummaryThe literature points to the need for the availability of more residential building data sources to be able to assess and improve models, and further testing is needed including those models that have not yet been significantly used for residential buildings.
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