Abstract
Non-intrusive load monitoring (NILM), monitoring single-appliance consumption level by decomposing the aggregated energy consumption, is a novel and economic technology that is beneficial to energy utilities and energy demand management strategies development. Hardware costs of high-frequency sampling and algorithm’s computational complexity hampered NILM large-scale application. However, low sampling data shows poor performance in event detection when multiple appliances are simultaneously turned on. In this paper, we contribute an iterative disaggregation approach that is based on appliance consumption pattern (ILDACP). Our approach combined Fuzzy C-means clustering algorithm, which provide an initial appliance operating status, and sub-sequence searching Dynamic Time Warping, which retrieves single energy consumption based on the typical power consumption pattern. Results show that the proposed approach is effective to accurately disaggregate power consumption, and is suitable for the situation where different appliances are simultaneously operated. Also, the approach has lower computational complexity than Hidden Markov Model method and it is easy to implement in the household without installing special equipment.
Highlights
With the development of science and technology, the electricity consumption is constantly growing, which stimulate the development of Smart Grid [1], a new efficient, reliable, flexible and optimized electric power system [2]
To solve the above problems, we proposed an iterative load disaggregation approach based on appliance consumption pattern (ILDACP), which will be implemented in a reduced invasiveness way
An open source toolkit using factorial Hidden Markov Model (FHMM) for Non-intrusive load monitoring (NILM) is provided to allow for empirical comparisons to be made between energy disaggregation algorithms across multiple data sets
Summary
With the development of science and technology, the electricity consumption is constantly growing, which stimulate the development of Smart Grid [1], a new efficient, reliable, flexible and optimized electric power system [2]. To solve the above problems, we proposed an iterative load disaggregation approach based on appliance consumption pattern (ILDACP), which will be implemented in a reduced invasiveness way. Time Warping (DTW) to identify the on/off states of each appliance in sequence and sub-sequence searching DTW (SSDTW) to accurately disaggregate total power into single-level base on the different consumption pattern of each appliance. DTW is applied to identify the series from selected highest power level cluster (label the cluster) and SSDTW to estimate the consumption trajectory of single appliance. The proposed algorithm is tested on the data in AMPds database and three kinds of performance metrics are utilized to estimate the capability to identify the appliances operating states, power consumption, and the accuracy of trace retrieve.
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