Abstract
Non-intrusive load decomposition can decompose the power consumption of a single appliance from the household bus data, which is of great significance for users to adjust their own power consumption strategy. In order to solve the problem of large amount of computation in hyperparameter optimization of load decomposition model based on deep residual network, a Group Bayesian optimization method is proposed. This method can obtain better hyperparameter combination with less computational cost. In addition, in order to solve the problem of irrelevant activation of the model decomposition results, an improved post-processing method is proposed to improve the comprehensive performance of the model. Finally, the public data set REFIT is used to verify the proposed method, and the results show that the proposed method has a low decomposition error.
Highlights
One of the urgent needs of smart grid and energy Internet is to obtain the electricity consumption data of individual electrical appliance, based on which users can understand the energy consumption law of each electrical appliance and reduce energy consumption pertinently
We introduce the concept of group optimization on the basis of the traditional Bayesian optimization method, and proposes a Group Bayesian optimization method to realize hyperparameter optimization of deep residual network (ResNet), obtaining a better load decomposition model with a small computational cost; at the same time, a post-processing method for the decomposition results of the model is creatively proposed to eliminate unreasonable activation and further improve the comprehensive performance of the model
It can be found that the deep ResNet we proposed has achieved optimal values in Mean Absolute Error (MAE), signal aggregation error (SAE) and NDE metrics compared with ordinary Convolutional Neural Network (CNN)
Summary
One of the urgent needs of smart grid and energy Internet is to obtain the electricity consumption data of individual electrical appliance, based on which users can understand the energy consumption law of each electrical appliance and reduce energy consumption pertinently. This is an important step towards transparency and intelligence of the power grid. The current measurement technology can only automatically read the total power consumption data, and it is difficult to further obtain a user's internal load information. ILD technology needs to install measuring instruments on all the electrical appliances of the user, which leads to high investment cost and difficult maintenance. NILD puts forward higher requirements for software algorithms, which has become the biggest obstacle for NILD to put into practical application
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