Neural NILM: Deep neural networks applied to energy disaggregation

BuildSys 2015 - Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built

Volumn , Issue , 2015, Pages 55-64

  Kelly, Jack ^a   Knottenbelt, William ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Deep learning; Energy conservation; Energy disaggregation; Feature learning; Neural networks; NILM

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMBINATORIAL OPTIMIZATION; ELECTRIC POWER UTILIZATION; EMBEDDED SYSTEMS; ENERGY CONSERVATION; ENERGY EFFICIENCY; HIDDEN MARKOV MODELS; HOUSES; IMAGE CLASSIFICATION; LEARNING SYSTEMS; MARKOV PROCESSES; NETWORK ARCHITECTURE; OPTIMIZATION; RECURRENT NEURAL NETWORKS; SPEECH RECOGNITION;

AUTOMATIC SPEECH RECOGNITION; CLASSIFICATION PERFORMANCE; DEEP LEARNING; DISAGGREGATION; ELECTRICITY-CONSUMPTION; FEATURE LEARNING; LONG SHORT TERM MEMORY; NILM;

NEURAL NETWORKS;

EID: 84959046782     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2821650.2821672     Document Type: Conference Paper

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