Cooling load disaggregation using a NILM method based on random forest for smart buildings

This publication has 44 references indexed in Scilit:

• Nonintrusive disaggregation of residential air-conditioning loads from sub-hourly smart meter data
  Energy and Buildings, 2014
• Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques
  Applied Energy, 2014
• Spatiotemporal lighting load disaggregation using light intensity signal
  Energy and Buildings, 2014
• Multi-appliance recognition system with hybrid SVM/GMM classifier in ubiquitous smart home
  Information Sciences, 2013
• Modern development of an Adaptive Non-Intrusive Appliance Load Monitoring system in electricity energy conservation
  Applied Energy, 2012
• Modeling of end-use energy consumption in the residential sector: A review of modeling techniques
  Renewable and Sustainable Energy Reviews, 2009
• A review on buildings energy consumption information
  Energy and Buildings, 2008
• Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses
  Energy and Buildings, 1999
• Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms
  Energy and Buildings, 1996
• Nonintrusive appliance load monitoring
  Proceedings of the IEEE, 1992