article Open AccessTop 1% cited
Data driven prediction models of energy use of appliances in a low-energy house
Energy and Buildings · 2017 · Vol. 140 · pp. 81–97
Luis M. Candanedo✉(University of Mons)Véronique Feldheim(University of Mons)Dominique Deramaix(University of Mons)
Building Energy and Comfort OptimizationSmart Grid Energy ManagementEnergy Load and Power ForecastingRandom forestWind speedSupport vector machinePredictive modellingLinear regressionGradient boostingData setEnergy (signal processing)Artificial neural networkComputer science
Funding
- European Commission
Citations
527
FWCI
37.72
field-weighted impact
References
68
Percentile
100%
vs. same field & year
Citations per year
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References
Occupant behavior modeling for building performance simulation: Current state and future challenges
Energy and Buildings · 2015 · 847 citations
Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools
Energy and Buildings · 2012 · 758 citations
Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior
Energy · 2013 · 577 citations
Applying support vector machines to predict building energy consumption in tropical region
Energy and Buildings · 2004 · 787 citations
A high-resolution domestic building occupancy model for energy demand simulations
Energy and Buildings · 2008 · 547 citations
A review on the prediction of building energy consumption
Renewable and Sustainable Energy Reviews · 2012 · 1,868 citations
A review on buildings energy consumption information
Energy and Buildings · 2007 · 6,392 citations
Accurate occupancy detection of an office room from light, temperature, humidity and CO 2 measurements using statistical learning models
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