Algorithm

Control (management)

Engineering

Philosophy

Factorial experiment

Statistics

Fractional factorial design

Hidden Markov model

Applied mathematics

Smart grid

Computer science

Mathematical optimization

A priori and a posteriori

Load management

Mathematics

Maximum a posteriori estimation

Fuzzy logic

Epistemology

Artificial intelligence

Maximum likelihood

Control theory (sociology)

Iterative method

Machine learning

Electrical engineering

IEEE Transactions on Smart Grid

With the promotion and application of smart grid, the technology of non-intrusive load monitoring (NILM) has gained more and more attention in recent years. Different from direct device monitoring, it identifies the type of appliances using the aggregated load profile measured at a single metering point, which is more convenient and flexible, and thus has the potential to be extended to all households equipped with smart meters. This paper proposes an extensible and comprehensive model to solve the load disaggregation problem, which employs the additive factorial approximate maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> (AFAMAP) based on iterative fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${c}$ </tex-math></inline-formula> -means (IFCM). To make sure that the model is adaptive to other households, the hidden Markov models (HMMs) are applied to obtain the independent load model of each appliance, and the IFCM is used to determine the number of hidden states adaptively. Finally, the AFAMAP is utilized to decompose the aggregated power consumption based on the independent load models built by HMM. Simulation studies are conducted on the open database of Almanac of Minutely Power dataset (AMPds), and the results have demonstrated that the proposed model is more accurate in comparison with the other models.

Non-Intrusive Load Monitoring Using Additive Factorial Approximate Maximum <i>a Posteriori</i> Based on Iterative Fuzzy $c$ -Means

Non-Intrusive Load Monitoring Using Additive Factorial Approximate Maximum a Posteriori Based on Iterative Fuzzy $c$ -Means