Quantifying rooftop photovoltaic solar energy potential: A machine learning approach

Solar Energy

Volumn 141, Issue , 2017, Pages 278-296

  Assouline, Dan ^a   Mohajeri, Nahid ^a   Scartezzini, Jean Louis ^a  

^a EPFL   (Switzerland)

Author keywords

Geographic information systems; Large scale solar potential; Machine learning; Solar photovoltaics; Support vector regression

Indexed keywords

ARTIFICIAL INTELLIGENCE; CARBON DIOXIDE; ENERGY RESOURCES; FLOORS; GEOGRAPHIC INFORMATION SYSTEMS; GLOBAL WARMING; INFORMATION SYSTEMS; LEARNING SYSTEMS; MEAN SQUARE ERROR; RENEWABLE ENERGY RESOURCES; ROOFS; SOLAR CONCENTRATORS; SOLAR ENERGY; SUPPORT VECTOR MACHINES;

MACHINE LEARNING APPROACHES; PHOTOVOLTAIC SOLAR ENERGY; ROOT MEAN SQUARE ERRORS; SOLAR PHOTOVOLTAICS; SOLAR POTENTIAL; SUPPORT VECTOR MACHINE (SVMS); SUPPORT VECTOR REGRESSION (SVR); USE OF RENEWABLE ENERGIES;

SOLAR POWER GENERATION;

BUILDING; CARBON EMISSION; ELECTRICITY; EMISSION CONTROL; ENERGY RESOURCE; ENERGY USE; GIS; GLOBAL WARMING; INSTALLATION; MACHINE LEARNING; PHOTOVOLTAIC SYSTEM; REGRESSION ANALYSIS; RENEWABLE RESOURCE; SOLAR POWER; SUPPORT VECTOR MACHINE; URBAN AREA;

SWITZERLAND;

EID: 85006412322     PISSN: 0038092X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.solener.2016.11.045     Document Type: Article

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