Abstract
Electrical load planning and demand response programs are often based on the analysis of individual load-level measurements obtained from houses or buildings. The identification of individual appliances’ power consumption is essential, since it allows improvements, which can reduce the appliances’ power consumption. In this article, the problem of identifying the electrical loads connected to a house, starting from the total electric current measurement, is investigated. The proposed system is capable of extracting the energy demand of each individual device using a nonintrusive load monitoring (NILM) technique. An NILM algorithm based on a convolutional neural network is proposed. The proposed algorithm allows simultaneous detection and classification of events without having to perform double processing. As a result, the calculation times can be reduced. Another important advantage is that only the acquisition of current is required. The proposed measurement system is also described in this article. Measurements are conducted using a test system, which is capable of generating the electrical loads found on a typical house. The most important experimental results are also included and discussed in the article.
Highlights
T HE reduction in electrical energy consumption requires the acquisition of ever more detailed data on individual users’ power consumption
The proposed solution is a Deep learning (DL)-based nonintrusive load monitoring (NILM) system, which adopts a particular type of artificial neural network (ANN), namely, the convolutional neural network (CNN) [20]–[22]
The Convolutional neural network (CNN) network was implemented on a desktop computer using the open-source Python 3.7 from Anaconda [32]
Summary
Fabrizio Ciancetta , Member, IEEE, Giovanni Bucci , Member, IEEE, Edoardo Fiorucci , Senior Member, IEEE, Simone Mari , Student Member, IEEE, and Andrea Fioravanti , Member, IEEE. Abstract— Electrical load planning and demand response programs are often based on the analysis of individual load-level measurements obtained from houses or buildings. The problem of identifying the electrical loads connected to a house, starting from the total electric current measurement, is investigated. The proposed system is capable of extracting the energy demand of each individual device using a nonintrusive load monitoring (NILM) technique. An NILM algorithm based on a convolutional neural network is proposed. Another important advantage is that only the acquisition of current is required. Measurements are conducted using a test system, which is capable of generating the electrical loads found on a typical house.
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