Abstract
The increased awareness in reducing energy consumption and encouraging response from the use of smart meters have triggered the idea of non-intrusive load monitoring (NILM). The purpose of NILM is to obtain useful information about the usage of electrical appliances usually measured at the main entrance of electricity to obtain aggregate power signal by using a smart meter. The load operating states based on the on/off loads can be detected by analysing the aggregate power signals. This paper presents a comparative study for evaluating the performance of artificial intelligence techniques in classifying the type and operating states of three load types that are usually available in commercial buildings, such as fluorescent light, air-conditioner and personal computer. In this NILM study, experiments were carried out to collect information of the load usage pattern by using a commercial smart meter. From the power parameters captured by the smart meter, effective signal analysis has been done using the time time (TT)-transform to achieve accurate load disaggregation. Load feature selection is also considered by using three power parameters which are real power, reactive power and the TT-transform parameters. These three parameters are used as inputs for training the artificial intelligence techniques in classifying the type and operating states of the loads. The load classification results showed that the proposed extreme learning machine (ELM) technique has successfully achieved high accuracy and fast learning compared with artificial neural network and support vector machine. Based on validation results, ELM achieved the highest load classification with 100% accuracy for data sampled at 1 minute time interval.
Highlights
Monitoring energy consumption of electrical appliances in various building is an important part of energy management with advantages such as knowing energy consumption behavior, and for troubleshooting
The training and testing results for the extreme learning machine (ELM), artificial neural network (ANN) and support vector machine (SVM) are presented in the following sub-sections
This paper has presented a comparative study for evaluating the performance of artificial intelligence techniques in classifying the type and operating states of three load types, namely, fluorescent light, air-conditioner and personal computer for non-intrusive load monitoring (NILM)
Summary
Monitoring energy consumption of electrical appliances in various building is an important part of energy management with advantages such as knowing energy consumption behavior, and for troubleshooting. Non-intrusive load monitoring (NILM) based on monitoring electrical loads at one point of power measurement is introduced This method is economical because it does not require installation of many meters and has potential for better energy management. The idea of implementing NILM is to separate the aggregate energy obtained from smart meters to the energy of individual electrical equipment [2]. By implementing the NILM method, consumers are able to monitor the operation of all electrical loads used in a building without incurring high installation cost of meters. A performance comparison is made on the use of artificial intelligence techniques to monitor three types of electrical appliances with different load profile conditions. The compared artificial intelligence techniques are the extreme learning machine (ELM), ANN and SVM techniques
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