Data-driven predictive models for residential building energy use based on the segregation of heating and cooling days

Data-driven models can estimate the buildings’ energy consumption using machine learning algorithms. This approach works based on the correlation between energy consumption and various inputs such as weather data, occupancy schedules, heating, air conditioning, and physical properties of buildings....

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Vydáno v:	Energy (Oxford) Ročník 206; s. 118045
Hlavní autoři:	Kamel, Ehsan, Sheikh, Shaya, Huang, Xueqing
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Elsevier Ltd 01.09.2020 Elsevier BV
Témata:	air Air conditioning Algorithms Buildings climatic zones computer simulation Control systems Cooling Data-driven predictive model energy Energy consumption Feature selection heat Heating Heating and cooling days Hot water heating Humid climates Learning algorithms Machine learning Meteorological data Model accuracy Occupancy Physical properties Prediction models Predictive control Recursive methods Regression regression analysis Residential areas Residential buildings Residential energy Schedules Seasonal variations Sensors Ventilation Energy consumption Feature selection Data-driven predictive model Residential buildings Heating and cooling days
ISSN:	0360-5442, 1873-6785
On-line přístup:	Získat plný text
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