Non-Intrusive Identification of Loads by Random Forest and Fireworks Optimization

Abstract

The control of expenses related to electricity has been showing significant growth, especially in residential environments. Monitoring of electrical loads that are turning on and off from home are often performed using smart plugs, providing to the consumers' information about operation intervals and power consumed by each device. Despite a practical solution to control and reduce electricity costs, it has a high cost due to the number of meters required. The high-cost problem can be worked around by using a non-intrusive load monitoring proposal (NILM), where voltage and current measurements are taken at the home entrance, in counterpart demand an extra processing step. In this extra step, various actions like computation of powers, identification of the occurrence of events, and identification of the status of which equipment (on/ off) must be made. The purpose of this work was the elaboration of a heuristic type event detector using floating analysis windows for locating stability zones on power signals after indicating a power change above a predetermined value. For that, tests of the best arrangement of event identifier data to identify which load has been added or removed from the monitored circuit are made. The proposed hybrid approach optimizes the processes using the Fireworks Algorithm (FWA) were used in the Random Forest classifier to improve classification performance. The proposed event identifier and classifier tests were performed on the dataset BLUED, which contains data collected at a North-American residence over one week. The event identifier results were compared with other publications that used different approaches, and the results of the classifications were compared to each other, using various data entry forms, and as an ideal classifier.

Energy Management in Smart Buildings and Homes: Current Approaches, a Hypothetical Solution, and Open Issues and Challenges

“…In [64], the SM data collected from over 500 homes has been analyzed and classified into different categories (such as the average morning consumption, the peak of consumption, etc.) using the random forest classifier [68]. This extracted data is correlated with the weather data (ex.…”

Non-Intrusive Load Monitoring Using a CNN-LSTM-RF Model Considering Label Correlation and Class-Imbalance

“…Apart from deep learning-based methods for NILM, there have been many studies focused on realizing NILM by machine learning methods. Support Vector Machines (SVM) [28], Decision Trees (DT) [29], combinatorial optimization (CO) [30] and Random Forest (RF) [31] have been used to solve event-based NILM. The Hidden Markov Model (HMM) and its modification have been designed to solve state-based NILM.…”

A Nonintrusive Load Identification Model Based on Time-Frequency Features Fusion

“…There are also some novel methods that can be used in NILM research, such as the hidden Markov model [13]- [15], machine learning models [16], [17], deep neural networks [18]- [22], etc. Lam et al [23] proposed a classification method for electrical equipment based on the V-I trajectory shape using high-frequency data from the PLAID [24] and WHITED [25] datasets.…”