基于混合神经网络的电力客户细分研究

Research on Power Customer Segmentation Based on Hybrid Neural Network

随着我国电力市场改革的逐步深化,电力市场需求多元化的特性逐步凸显。制定有针对性的营销策略,满足不同客户的用电需求,实现个性化与差异化的服务,提高电网公司的核心竞争力,扩大电能在社会消费终端中的占有率,已成为电力企业一项迫切任务。为了制定电力客户的用电服务,电力客户细分就显得尤为重要。论文提出了H-LSTM(Hy?brid-Long Short-Term Memory)的混合神经网络方法来进行电力客户细分,通过用电客户的特征指标,时间之间的联系,进行对电力客户的分类。通过对电力大客户的供电营销数据进行实验,和决策树、原始LSTM神经网络等方法进行了比较,实验结果表明,H-LSTM的电力客户细分方法准确度更高,具有实际应用价值。

With the gradual deepening of China's power market reform,the characteristics of diversified demand in the power market have gradually emerged. Formulating targeted marketing strategies to meet different customers' electricity demand,achieving personalized and differentiated services,improving the core competitiveness of power grid companies,and increasing the share of electric energy in social consumer terminals have become a power company urgent task. In order to formulate electricity customers' power services,power customer segmentation is particularly important. In this paper,the neural network method of H-LSTM(Hy.brid-Long Short-Term Memory)is proposed to subdivide the power customers,and the power customers are classified by the char. acteristics of customers and time. Through experiments on power supply marketing data of major power customers,comparisons are made with decision trees and the original LSTM neural network. The experimental results show that the power customer segmentation method of H-LSTM is more accurate and has practical application value.