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| 基于改进熵值法的客户停电敏感度识别方法研究 |
| Research on customer outage sensitivity identification method based on improved entropy method |
| 投稿时间：2018-11-30 修订日期：2019-03-10 |
| DOI： |
| 中文关键词: 熵值法 停电敏感 自适应神经元 梯度下降 半监督学习 综合评估 |
| 英文关键词: Entropy, power outage sensitivity, adaline, gradient descent, semi-supervised, comprehensive assessment |
| 基金项目: |
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| 中文摘要: |
| 面临售电市场的放开，为提升电网企业的竞争力，客户需求得到了高度重视。停电问题是影响客户感知的首要因素。对客户停电敏感度的识别，是对客户开展精准服务的重要条件之一。提高客户停电敏感度的识别精度，是提高服务水平的关键。本文提供一种提高停电敏感度模型精度的方法，基于客户信息、行为等多维度指标，在传统熵值法基础上，利用自适应线性神经元算法修正权重，将无监督学习转化为半监督学习，提升权重赋值科学性。 |
| 英文摘要: |
| Facing the opening of electric power market, customer demand has been highly valued in order to enhance the competitiveness of power grid enterprises. Power outage is the primary factor affecting customer perception. The identification of customer outage sensitivity is one of the important conditions for accurate customer service. Improving the identification accuracy of customer outage sensitivity is the key to improving service level. This paper provides a method to improve the accuracy of outage sensitivity model. Based on multi-dimensional indicators such as customer information and behavior, and on the basis of traditional entropy method, adaline algorithm is used to modify the weights, and unsupervised learning is transformed into semi-supervised learning, so as to enhance the scientificity of weight assignment. |
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