Occupant workstation level energy-use prediction in commercial buildings: Developing and assessing a new method to enable targeted energy efficiency programs

Energy and Buildings

Volumn 127, Issue , 2016, Pages 1133-1145

  Khosrowpour, Ardalan ^a   Gulbinas, Rimas ^b   Taylor, John E ^a  

^a VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

^b Department of Obstetrics Gynecology   (United States)

Author keywords

Commercial buildings; Energy efficiency; Machine learning; Occupant behavior; Support vector machine

Indexed keywords

ARTIFICIAL INTELLIGENCE; BUILDINGS; ENERGY UTILIZATION; FORECASTING; INTELLIGENT BUILDINGS; LEARNING ALGORITHMS; LEARNING SYSTEMS; OFFICE BUILDINGS; SUPPORT VECTOR MACHINES;

BEHAVIOR-BASED; BUILDING MANAGEMENT SYSTEM; COMMERCIAL BUILDING; ENERGY EFFICIENCY PROGRAMS; ENERGY EFFICIENT; ENERGY USE PATTERNS; OCCUPANT BEHAVIOR; PREDICTION ACCURACY;

ENERGY EFFICIENCY;

EID: 84977143048     PISSN: 03787788     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enbuild.2016.05.071     Document Type: Article

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