Abstract
The concern of energy price hikes and the impact of climate change because of energy generation and usage forms the basis for residential building energy conservation. Existing energy meters do not provide much information about the energy usage of the individual appliance apart from its power rating. The detection of the appliance energy usage will not only help in energy conservation, but also facilitate the demand response (DR) market participation as well as being one way of building energy conservation. However, energy usage by individual appliance is quite difficult to estimate. This paper proposes a novel approach: an unsupervised disaggregation method, which is a variant of the hidden Markov model (HMM), to detect an appliance and its operation state based on practicable measurable parameters from the household energy meter. Performing experiments in a practical environment validates our proposed method. Our results show that our model can provide appliance detection and power usage information in a non-intrusive manner, which is ideal for enabling power conservation efforts and participation in the demand response market.
Highlights
Electricity is one of the most common and important commodities we use everyday
Both papers adopted the use of artificial neural network (ANN), adding to the many papers that have published to improve the performance of non-intrusive appliance load monitoring (NIALM) using
We investigated how electrical appliances could be detected and identified from an aggregated power based on residential load using multiple conditional factorial hidden Markov model (MCFHMM) algorithm
Summary
Electricity is one of the most common and important commodities we use everyday. Electricity energy demands requested from the consumer sector in a smart grid (SG) are constantly increasing in recent times as a result of a proliferation of electric appliances in the market. DR participation can facilitate domestic energy management as well as mitigate frequency deviation by shedding some power generated (i.e., microgrid) or loads on national grid in return for monetary incentives; while on the other hand, consumption feedback is provided as a self-learning tool To adapt such an energy demand optimization system, detailed end-user energy consumption information is an essential requirement. Notable among them is SG [11] with integrated home automation networks (HAN) [12] With such a system, building an energy management system utilizes real time price information to schedule loads to minimize energy consumption bills and provide economic incentive by participating in the DR market. How to provide an appliance-specific breakdown of energy use in a cost-effective manner without negatively impacting consumers’ standard of living or their productivity Without addressing this issue, residential energy management or conservation is unlikely to achieve widespread success.
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