Individual Electrical Appliances Research Articles

Toward Energy-Awareness Smart Building: Discover the Fingerprint of Your Electrical Appliances

Energy efficiency raises significant concerns as it is one of the most promising ways to mitigate climate change. Disaggregation and identification of individual electrical appliances activities are one of the essentials for energy preservation especially for smart buildings. This paper proposes a lightweight electrical appliance activity detection approach for smart building, which leverages a single smart metering device to establish a learning and detection processing for multiple appliances. In this system, data interpolation and transition detection algorithm are proposed to effectively reduce the cost of model training and optimize the detection accuracy. The concept of appliance fingerprint is proposed and a variety of fingerprints, including appliance-based and context-based, are defined to depict fine-grained appliance characteristics. Based on these fingerprints, the paper proposes a multisource fingerprint-weighting KNN (FWKNN) classification algorithm and presents a boosting framework for continuous online learning and detection. A prototype system is implemented and demonstrated in IBM Bluemix PaaS cloud platform. Experimental result and analysis prove that FWKNN outperforms other benchmark methods in detection accuracy.

A systematic approach in appliance disaggregation using k-nearest neighbours and naive Bayes classifiers for energy efficiency

One of the ways to achieve energy efficiency in various residential electrical appliances is with energy usage feedback. Research work done showed that with energy usage feedback, behavioural changes by consumers to reduce electricity consumption contribute significantly to energy efficiency in residential energy usage. In order to improve on the appliance-level energy usage feedback, appliance disaggregation or non-intrusive appliance load monitoring (NIALM) methodology is utilized. NIALM is a methodology used to disaggregate total power consumption into individual electrical appliance power usage. In this paper, the electrical signature features from the publicly available REDD data set are extracted by the combination of identifying the ON or OFF events of appliances and goodness-of-fit (GOF) event detection algorithm. The k-nearest neighbours (k-NN) and naive Bayes classifiers are deployed for appliances’ classification. It is observed that the size of the training sets effects classification accuracy of the classifiers. The novelty of this paper is a systematic approach of NIALM using few training examples with two generic classifiers (k-NN and naive Bayes) and one feature (power) with the combination of ON-OFF based approach and GOF technique for event detection. In this work, we demonstrated that the two trained classifiers are able to classify the individual electrical appliances with satisfactory accuracy level in order to improve on the feedback for energy efficiency.

A Preliminary Study on the Cost Implications and Validity of Executing a Control Strategy on Individual Appliance Usage

This study evaluates a number of power-saving measures that were applied on individual electric appliances to investigate their contributions to energy savings of the electricity network. In order to carry out the study, power-saving measures were applied on all appliances under study for certain period periods at the Universiti Malaysia Sarawak (UNIMAS). The appliances are the air conditioner (AC), computers, lightings and closed-circuit television (CCTV). As a means of validating the accuracy of models developed for electricity costs, a comparison of was done between measurements taken from the electricity network and those taken from PowerLogic PM5350 power meter (PM5350) installed for the purpose of this research. The results from model analysis show significant cost savings of 39.9%, 20.3%, 8% and 0.6% when control strategies were applied to AC, lightings, computers and CCTV

A non-intrusive appliance load monitoring for efficient energy consumption based on Naive Bayes classifier

This paper reports the use of low cost off-the-shelf components to develop a wireless energy monitoring system for the purpose of non-intrusive appliance load monitoring. The system comprises hardware design where a standard open source WiFi-connected embedded system board is used as the core component, an analog front end for interfacing between the voltage and current sensors and the embedded board and also a server to store data for further processing. The software side consists of algorithms for performing data partitioning and Naive Bayes classifier. Non-intrusive appliance load monitoring (NIALM) is a methodology used to disaggregate total power consumption into individual electrical appliance power usage. In this paper, the average power consumptions of electrical appliances are obtained from the system setup through the identification of switching ON or OFF events of appliances. The Naive Bayes classifier is deployed for appliances’ classification. This work demonstrates the feasibility of utilizing low cost and standard components to develop a wireless energy monitoring system. It is also observed that the developed classifier algorithm is able to classify individual electrical appliances with satisfactory accuracy level using few training examples. The novelty of this paper is that it developed a data set consisting of 4 types of appliances from the application of embedded networking device. This paper also shows that satisfactory classification accuracies can be achieved using few training examples with generic classifiers (Naive Bayes) and one feature (the average power). It provides an example of how NIALM can offer feedback to appliances’ energy consumption and therefore promote energy efficiency.

Assessment of the impact of information feedback of prepaid meter on energy consumption of city residential buildings using bottom-up load modeling approach

This paper evaluates the impact of information feedback of prepaid meter as a demand side management (DSM) tool in reducing the energy consumption of residential buildings in the city of Ibadan, Nigeria. The bottom-up modeling approach which has the capability to determining the total energy consumption without historical data of energy use is adopted for the study. First, active residential occupancy pattern of different individual household population classes are obtained from literature, then the individual electrical appliances are aggregated in such a way that reflects the income pattern of the households to form single individual household load profiles. The load profiles of the various individual households are aggregated to determine the overall residential load profile of the entire city. The city of Ibadan, southwestern Nigeria is used as the study area. Information feedback from the prepaid meter is modeled by setting priorities to groups of appliances as they affects the quality of life of the end user. The rate of energy consumption of the loads is limited using weighted load constraint multipliers. Some of the key results of a the study show that information feedback from prepaid meter for the city of Ibadan would enhanced total energy consumption savings of 167.2MWh for weekdays and savings of 209.8MWh for weekend. This translates to weekday cost saving of $16,056 and weekend cost savings of $20,143.

A systematic approach to ON-OFF event detection and clustering analysis of non-intrusive appliance load monitoring

The aim of non-intrusive appliance load monitoring (NIALM) is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a systematic approach to ON-OFF event detection and clustering analysis for NIALM were presented. From the aggregate power consumption data set, the data are passed through median filtering to reduce noise and prepared for the event detection algorithm. The event detection algorithm is to determine the switching of ON and OFF status of electrical appliances. The goodness-of-fit (GOF) methodology is the event detection algorithm implemented. After event detection, the events detected were paired into ON-OFF pairing appliances. The results from the ON-OFF pairing algorithm were further clustered in groups utilizing the K-means clustering analysis. The K-means clustering were implemented as an unsupervised learning methodology for the clustering analysis. The novelty of this paper is the determination of the time duration an electrical appliance is turned ON through combination of event detection, ON-OFF pairing and K-means clustering. The results of the algorithm implementation were discussed and ideas on future work were also proposed.

屋外からの家庭内電気機器消費電力推定における機械学習手法の性能評価

A non-intrusive monitoring system estimates the behavior of individual electric appliances from the measurement of the total household load demand curve. The total load demand curve is measured at the entrance of the power line into the house. The power consumption of individual appliances can be estimated using several machine learning techniques by analyzing the characteristic frequency contents from the load curve of the hosehold. In this paper, we present results of applying several regression methods such as multi-layered perceptrons (MLP), radial basis function networks (RBFN) and Support Vector regressors (SVR) to estimate the power consumption of an air conditioner. Our experiments show RBFN can achieve the best accuracy for the non-intrusive monitoring system.