One of the key techniques toward energy efficiency and conservation is nonintrusive 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 (SW) 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.

An optimal energy mix of various renewable energy sources and storage devices is critical for a profitable and reliable hybrid microgrid system. This work proposes a hybrid optimization method to assess the optimal energy mix of wind, photovoltaic, and battery for a hybrid system development. This study considers the hybridization of a Non-dominant Sorting Genetic Algorithm II (NSGA II) and the Grey Wolf Optimizer (GWO). The objective function was formulated to simultaneously minimize the total energy cost and loss of power supply probability. A comparative study among the proposed hybrid optimization method, Non-dominant Sorting Genetic Algorithm II, and multi-objective Particle Swarm Optimization (PSO) was performed to examine the efficiency of the proposed optimization method. The analysis shows that the applied hybrid optimization method performs better than other multi-objective optimization algorithms alone in terms of convergence speed, reaching global minima, lower mean (for minimization objective), and a higher standard deviation. The analysis also reveals that by relaxing the loss of power supply probability from 0% to 4.7%, an additional cost reduction of approximately 12.12% can be achieved. The proposed method can provide improved flexibility to the stakeholders to select the optimum combination of generation mix from the offered solutions.

Energy monitoring is inevitable towards achieving energy efficiency and conservation. Load disaggregation is one of the techniques towards effective energy monitoring. In the said domain, Non-Intrusive Appliance Load Monitoring (NIALM) is an attractive method where aggregated load data are acquired from a single metering point and segregated appliance level load is estimated using effective software techniques. This paper presents a low complexity event-based NIALM technique based on supervised machine learning. In this paper, the emphasis is on the disaggregation of Air Conditioning (AC) unit and Electric Vehicle (EV) charging loads due to their high significance for the overall power grid stability improvement. A comprehensive digital simulation has been carried out to validate the performance of the proposed approach and intended appliances are aptly classified having an outcome of 97% for same Data ID and 95% for different Data ID in terms of precision, recall, and f-score performance metrics.

Non-intrusive load monitoring (NILM) is widely regarded as a key method for energy disaggregation to estimate the energy consumption and operating time of individual equipment and appliances. Over the past few years, NILM has achieved substantially high accuracy through the incorporation of several machine learning (ML) and deep learning (DL) algorithms. This article reviews ML/DL-based approaches that have been applied to address NILM-related problems. This review focuses on five research questions: First, which problems associated with NILM systems have been addressed in recent studies using ML and DL models, and how well have the ML and DL models addressed NILM-related problems? Second, which ML and DL models have been the most explored for NILM, and how do the ML and DL algorithms perform on different NILM datasets? Third, how has model interpretability been addressed in ML/DL-based NILM methods, and what techniques have been proposed to improve the transparency of predictions? Fourth, what are the practical requirements of ML-based NILM systems in real-world settings? Finally, what are the main challenges of ML-based NILM systems in terms of datasets, models, and their implementation? This review also provides recommendations for addressing the identified research gaps and emerging trends and technologies in NILM and presents directions for future research.

In order to tackle today's energy and sustainability issues, there are two paths to be followed by the world community. Either they have to establish new generation plants with an expense of millions of dollars or to look deeper into the existing system to design and deploy techniques that can lead to a significant amount of energy saving. Today, researchers are extensively working towards energy efficiency and conservation via developing different techniques. In the said domain, energy monitoring is one of the key techniques which is an attractive and popular research topic in the field of sustainable energy. It is single-point sensing or commonly known as Non-Intrusive Load Monitoring (NILM) that is used to extract the appliance level energy consumption. For a NILM system, event detection plays a key role and is a pre-requisite for the later stages of the system. In this paper a simple and low complexity event detection algorithm is proposed. Digital simulation and sensitivity studies have been carried out using real world data to check the performance and sensitivity of the proposed algorithm. Furthermore, the results of the proposed algorithm are compared with the existing event detection algorithm for evaluation purposes.