Abstract
Population growth and new consumer needs, among other factors, have lead to growing energy demand, without a concomitant increase in energy generation. This way, reduction and rationalization of energy consumption, especially by residential users, have become a global concern generating a need for developing techniques for efficient management and distribution of the available energy. Non-Intrusive Load Monitoring (NILM) techniques have provided valuable information about energy consumption for power generation companies as well as consumers. Such information is important for making decisions related to sustainable use of energy resources. This study proposes an automated system based on Artificial Neural Network for performing some of the NILM tasks. A stacked neural network was developed to extract features of power signals of appliances to identify those in operation during a given period. This information is then used to disaggregate individual appliance loads through the total aggregate signal, and consumption is calculated through numerical integration. The system was tested using real data from two databases about appliances with On/Off, multi-level, and variable consumption patterns collected in low frequency. The performance metrics, resulting from identification and disaggregation tasks, demonstrate the efficiency of the proposed system.
Highlights
A Significant challenge facing the world today is energy conservation
The method was evaluated on Reference Energy Disaggregation Dataset (REDD) data and despite the low F-score of 0.51, it showed a satisfactory result in the task of disaggregation with a Corrected Assigned Energy (CAE) of 0.816 dealing with 6 appliance categories
PROPOSED SYSTEM The system proposed in this paper focuses on some steps of Non-Intrusive Load Monitoring (NILM), as shown in Fig. 1 and is composed by: a) A module to analyze the aggregate power signal for event detection; b) A module formed by an Autoassociative Neural Network [48], [49] to extract appliance features, and a classification Multi-layer Perceptron Neural Network (MLP) to perform appliance identification in a stacked structure and c) A module for load disaggregation based on numerical integration [36]
Summary
A Significant challenge facing the world today is energy conservation This challenge arose due to growing energy demand, as a result of many factors encompassing areas such as population growth, new consumer needs, environmental problems, among others [1], [2]. This scenario will overwhelm the capacity of existing energy resources and systems, generating an energy crisis that would compromise economic development in general. Studies like [4]–[6] indicate that the maximum energy savings can be achieved by providing consumers more explicit information about their daily consumption
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