Event-Detection Algorithms for Low Sampling Nonintrusive Load Monitoring Systems Based on Low Complexity Statistical Features

Abstract

One of the key techniques towards energy efficiency and conservation is Non-Intrusive Load Monitoring (NILM) which lies in the domain of energy monitoring. Event detection is a core component of event-based NILM systems. This paper proposes two new low-complexity and computationally fast algorithms that detect the variations of load data and return the time occurrences of the corresponding events. The proposed algorithms are based on the phenomenon of a sliding window that tracks the statistical features of the acquired aggregated load data. The performance of the proposed algorithms is evaluated using real-world data and a comparative analysis has been carried out with one of the recently proposed event detection algorithms. Based on the simulations and sensitivity analysis it is shown that the proposed algorithm can provide the results of up to 93% and 88% in terms of recall and precision respectively.

IoT Based Approach for Load Monitoring and Activity Recognition in Smart Homes

“…This analysis enables the possibility of identifying less efficient or malfunctioning devices and implementing the appropriate actions intended for reducing consumption. In this context, consumers become a key factor; they not only participate effectively in the sustainable smart grid system, but they can also have a direct feedback on the statistics concerning power consumption in real-time [2]. Additional useful information could also be inferred from appliance data such as consumers' behavior patterns, including occupation, sleep patterns, and other activities.…”

“…The aggregated signal can be very noisy, and only a few electrical appliances could be detected, depending on the sampling frequency. Even with advanced artificial intelligence (AI) algorithms, it could be possible to monitor only a few major appliances: e.g., oven, washing machine, airconditioner, and electric vehicle (EV) [2], [3]. When facing these kinds of scenarios in terms of the type of appliance used, performance remains inconclusive on different datasets [4].…”

“…This technique can be divided into two sub-categories: one that refers to a model in which energy consumption profiles are obtained at device level using submeasurement sensors attached to appliances. The second is smart appliances (SA) which are devices with built-in capabilities to monitor and report their energy usage [2]. Although these solutions can be relatively expensive, they provide higher efficiency and reliability than NILMs.…”

State Characteristic Clustering for Nonintrusive Load Monitoring with Stochastic Behaviours in Smart Grids

“…In order to directly obtain the operating state of loads, modified event detection methods are applied for recent eventbased approaches [18][19][20][21][22]. For an event detection algorithm, the intuitive idea is to determine transient events by using a trigger threshold in detecting power signals [18].…”

“…e generality of the algorithm benefited from the improved cross-prediction approach, without knowing historical data. Also, for low sampling NILM systems, two novel event detection algorithms, variance and mean absolute deviation, are proposed [21]. By balancing the optimal window width and optimal performance, the aggregated active power data of real-world dataset can be captured based on a sliding window.…”