Abstract
Energy management systems strive to use energy resources efficiently, save energy, and reduce carbon output. This study proposes transient feature analyses of the transient response time and transient energy on the power signatures of non-intrusive demand monitoring and load identification to detect the power demand and load operation. This study uses the wavelet transform (WT) of the time-frequency domain to analyze and detect the transient physical behavior of loads during the load identification. The experimental results show the transient response time and transient energy are better than the steady-state features to improve the recognition accuracy and reduces computation requirements in non-intrusive load monitoring (NILM) systems. The discrete wavelet transform (DWT) is more suitable than short-time Fourier transform (STFT) for transient load analyses.
Highlights
The traditional method of load monitoring is based on supervisory control and data acquisition (SCADA)
To mitigate the disadvantages of steady-state signatures in previous research, this study proposes a new analysis method for load identification in an non-intrusive load monitoring (NILM) system
The purpose of the multi-layer feedforward neural network (MFNN) in this study is to identify the load of an NILM system
Summary
The traditional method of load monitoring is based on supervisory control and data acquisition (SCADA). Sensors are installed at every load, and a communication network is required for monitoring, communication, and control. The traditional load monitoring method is too complicated to implement in an ordinary household [1]. Schweppe and Hart proposed non-intrusive load monitoring (NILM) [2]. An NILM system does not require a voltage or current sensor at every load, and it determines the operating schedule of the major electric loads in a building at the power service entrance. Compared with the traditional load monitoring system, NILM systems offer simple hardware installation. An NILM system is a wide load monitoring scheme and a powerful energy management system [3,4,5,6].
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