Energy management technology of demand-side is a key process of the smart grid that helps achieve a more efficient use of generation assets by reducing the energy demand of users during peak loads. In the context of a smart grid and smart metering, this paper proposes a hybrid model of energy disaggregation through deep feature learning for non-intrusive load monitoring to classify home appliances based on the information of main meters. In addition, a deep neural model of supervised energy disaggregation with a high accuracy for giving awareness to end users and generating detailed feedback from demand-side with no need for expensive smart outlet sensors was introduced. A new functional API model of deep learning (DL) based on energy disaggregation was designed by combining a one-dimensional convolutional neural network and recurrent neural network (1D CNN-RNN). The proposed model was trained on Google Colab’s Tesla graphics processing unit (GPU) using Keras. The residential energy disaggregation dataset was used for real households and was implemented in Tensorflow backend. Three different disaggregation methods were compared, namely the convolutional neural network, 1D CNN-RNN, and long short-term memory. The results showed that energy can be disaggregated from the metrics very accurately using the proposed 1D CNN-RNN model. Finally, as a work in progress, we introduced the DL on the Edge for Fog Computing non-intrusive load monitoring (NILM) on a low-cost embedded board using a state-of-the-art inference library called uTensor that can support any Mbed enabled board with no need for the DL API of web services and internet connectivity.
Power line impedance is a very important parameter on the design of power line communications (PLC) modem architecture. Variations on the impedance of the power line affect the communications circuit performance. In order to determine impedance of the power lines, measurements were carried out in Turkey at frequencies ranging from 10 to 170 kHz, (CENELEC A,B,C,D bands). Measurements were conducted in three categories: rural, urban and the industrial power lines. Experimental results are presented in graphical form. The measured impedances were determined as 3-17 ohms, 1-17 ohms, and 1-21 ohms for rural, urban and the industrial lines, respectively. A set of the formulas between impedance and frequency are developed on the power lines using the regression analysis from the obtained empirical data. Signal attenuations on the power lines in the CENELEC band are also measured for rural, urban and industrial regions. Attenuation measurements are repeated for phase-neutral, phase-ground and the neutral-ground conductors. Signal attenuations are found to be 4-30 dB, for different power lines. To establish validity of obtained results for the design of PLC systems, the results are compared with previous investigations. The effects of some household appliances such as TV, PC, UPS, lighting and cooling systems on the impedances and the attenuations for power line communications systems are observed. Some suggestions and proposals are presented for PLC modem designers.
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