Abstract
In order to extract more information that affects customer arrears behavior, the feature extraction method is used to extend the low-dimensional features to the high-dimensional features for the warning problem of user arrears risk model of electric charge recovery (ECR). However, there are many irrelevant or redundant features in data, which affect prediction accuracy. In order to reduce the dimension of the feature and improve the prediction result, an improved hybrid feature selection algorithm is proposed, integrating nonlinear inertia weight binary particle swarm optimization with shrinking encircling and exploration mechanism (NBPSOSEE) with sequential backward selection (SBS), namely, NBPSOSEE-SBS, for selecting the optimal feature subset. NBPSOSEE-SBS can not only effectively reduce the redundant or irrelevant features from the feature subset selected by NBPSOSEE but also improve the accuracy of classification. The experimental results show that the proposed NBPSOSEE-SBS can effectively reduce a large number of redundant features and stably improve the prediction results in the case of low execution time, compared with one state-of-the-art optimization algorithm, and seven well-known wrapper-based feature selection approaches for the risk prediction of ECR for power customers.
Highlights
With the rapid development of global energy market, smart grid [1] in power industry has been built continuously, and the scale of information data accumulated by power system is becoming larger and larger
In order to prove the effectiveness and superiority of the proposed algorithm, two groups of comparative experiments are set up and use logistic regression model [66, 67] to realize the risk prediction of Electric charge recovery (ECR) for power customers in June, July, and August 2018. e first group of experiments verifies the effectiveness of the NBPSOSEE. e second group of experiments proves the superiority of the proposed hybrid feature selection algorithm based on NBPSOSEE with sequential backward selection (SBS), called NBPSOSEESBS
binary whale optimization algorithm (BWOA), binary artificial bee colony algorithm (BABC), BGA, binary grey wolf optimization (BGWO), binary particle swarm optimization (BPSO), chaotic binary particle swarm optimization (CBPSO), HI-BQPSO, and LBPSOSEE search for optimal fitness values after 97, 37, 61, 84, 68, 35, 48, and 43 iterations, respectively. e NBPSOSEE obtains the optimal fitness value until 15 iterations and can still search for the best fitness value after 17, 20, and 98 iterations
Summary
With the rapid development of global energy market, smart grid [1] in power industry has been built continuously, and the scale of information data accumulated by power system is becoming larger and larger. As the main income of power companies, electric tariff plays a decisive role in the development of power enterprises. In the whole process of power marketing, the risk of arrears’ users has always existed, which hinders the development of power enterprises. Electric charge recovery (ECR) has always been a difficult problem that power supply enterprises need to solve urgently. It is the most important management part of power meter reading, verification, and checking. It causes power customers to occupy the funds of power enterprises, which is not conducive to the fund management of power companies
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