Non-Intrusive Load Monitoring of Residential Water-Heating Circuit Using Ensemble Machine Learning Techniques

Abstract

The recent advancement in computational capabilities and deployment of smart meters have caused non-intrusive load monitoring to revive itself as one of the promising techniques of energy monitoring. Toward effective energy monitoring, this paper presents a non-invasive load inference approach assisted by feature selection and ensemble machine learning techniques. For evaluation and validation purposes of the proposed approach, one of the major residential load elements having solid potential toward energy efficiency applications, i.e., water heating, is considered. Moreover, to realize the real-life deployment, digital simulations are carried out on low-sampling real-world load measurements: New Zealand GREEN Grid Database. For said purposes, MATLAB and Python (Scikit-Learn) are used as simulation tools. The employed learning models, i.e., standalone and ensemble, are trained on a single household’s load data and later tested rigorously on a set of diverse households’ load data, to validate the generalization capability of the employed models. This paper presents a comprehensive performance evaluation of the presented approach in the context of event detection, feature selection, and learning models. Based on the presented study and corresponding analysis of the results, it is concluded that the proposed approach generalizes well to the unseen testing data and yields promising results in terms of non-invasive load inference.

Toward Robust Non-Intrusive Load Monitoring via Probability Model Framed Ensemble Method

“…The key is the effective design of the ensemble strategy, where the reliability and variability of individual classifiers matter. Researchers from New Zealand have conducted some preliminary explorations by using ensemble machine learning techniques in NILM [31]. Although only water heating is considered and the ensemble design is constrained to the problem scenario, which highly limits the potentials for widespread applications, such an attempt is an encouraging exploration.…”

“…Such attempts are with high difficulty and failure rates. So, although ensemble method has been demonstrated to be effective in NILM problem [31], the explorations of related studies are limited.…”

Voltage Control-Based Ancillary Service Using Deep Reinforcement Learning

“…Half of the TCLs are switched on at the beginning of a simulation, while the other half is off. This corresponds to values of switch = 1 for TCLs 1-10 and switch = 0 for TCLs 11-20 in the 20 TCL setup, where the switch value changes according to Equation (9). If the temperature crosses upper threshold θ max , the TCL changes the state to switched off, whereas if the temperature crosses the lower threshold θ min , the TCL state is changed to switched on.…”

“…Together with the contribution of the renewable energy sources to the smart grid transformation, a distributed smart grid is changed by the wide adoption of technologies related to the IoT paradigm [5,6]. Therefore, new solutions are necessary to solve the highlighted challenges [7][8][9]. To tackle the needs of grid improvement, one of the proposed advances in this paper is an approach to utilize a deep reinforcement method for the design of a controller considering the stochastic behavior of the thermostatically controlled loads and comfort of consumers without load disconnection.…”

An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity

“…In [34], a multiscale wavelet packet tree is applied to collect comprehensive energy consumption features, and an ensemble bagging tree is adopted as a classifier, where the performance is compared with various machine learning schemes. In [35], an event detection and disaggregation framework based on an ensemble approach is proposed, whose disaggregation target is the water heating operation. Both of the above works focus on event-based load monitoring.…”