Abstract
This paper proposes a novel non-intrusive load monitoring (NILM) method which incorporates appliance usage patterns (AUPs) to improve performance of active load identification and forecasting. In the first stage, the AUPs of a given residence were learned using a spectral decomposition based standard NILM algorithm. Then, learnt AUPs were utilized to bias the priori probabilities of the appliances through a specifically constructed fuzzy system. The AUPs contain likelihood measures for each appliance to be active at the present instant based on the recent activity/inactivity of appliances and the time of day. Hence, the priori probabilities determined through the AUPs increase the active load identification accuracy of the NILM algorithm. The proposed method was successfully tested for two standard databases containing real household measurements in USA and Germany. The proposed method demonstrates an improvement in active load estimation when applied to the aforementioned databases as the proposed method augments the smart meter readings with the behavioral trends obtained from AUPs. Furthermore, a residential power consumption forecasting mechanism, which can predict the total active power demand of an aggregated set of houses, 5 min ahead of real time, was successfully formulated and implemented utilizing the proposed AUP based technique.
Highlights
I N the recent years, Demand Side Management (DSM) has become an essential element of the rapidly developing smart grid; mainly as a result of increasing penetration of intermittent and variable renewable energy sources such as solar photovoltaic (PV) and wind
Since both priori unbiased and biased Non-Intrusive Load Monitoring (NILM) algorithms had used the same power breakdown technique in [48], this slight improvement should be due to the increased appliance combination identification accuracy achieved by the proposed NILM method
According to the actual and predicted total power profiles (See Fig.8), the proposed NILM algorithm has the ability to identify downward steps in the total power demand more efficiently when compared to identifying the upward trends
Summary
I N the recent years, Demand Side Management (DSM) has become an essential element of the rapidly developing smart grid; mainly as a result of increasing penetration of intermittent and variable renewable energy sources such as solar photovoltaic (PV) and wind. DSM tries to reduce/increase the demand either by shifting or reducing the consumption so that the available generation can be utilized efficiently while maintaining a minimum reserve. Direct Load Control (DLC) is one attractive option for DSM which helps the utility to shape the customer energy consumption profile by remotely controlling customers preagreed set of controllable appliances such as, heat, ventilation, air-conditioning and smart (HVACS) systems. Even though a smart meter connected at the consumer premises could make these HVACS loads flexible, unless the grid operator knows the amount of flexible load that is available at a given time, the utilities continue to maintain a large reserve by deloading generators
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