With the employment of smart meters, massive data on consumer behaviour can be collected by retailers. From the collected data, the retailers may obtain the household profile information and implement demand response. While retailers prefer to acquire a model as accurate as possible among different customers, there are two major challenges. First, different retailers in the retail market do not share their consumer's electricity consumption data as these data are regarded as their assets, which has led to the problem of data island. Second, the electricity load data are highly heterogeneous since different retailers may serve various consumers. To this end, a fully distributed short-term load forecasting framework based on a consensus algorithm and Long Short-Term Memory (LSTM) is proposed, which may protect the customer's privacy and satisfy the accurate load forecasting requirement. Specifically, a fully distributed learning framework is exploited for distributed training, and a consensus technique is applied to meet confidential privacy. Case studies show that the proposed method has comparable performance with centralised methods regarding the accuracy, but the proposed method shows advantages in training speed and data privacy.

翻译：利用智能仪,零售商可以收集大量关于消费者行为的数据,零售商可以从所收集的数据中获得家庭概况信息,并采取需求对策。零售商倾向于获得一个尽可能准确的模型,但面临两大挑战。第一,零售市场的不同零售商不分享消费者的消费数据,因为这些数据被视为其资产,从而导致了数据岛问题。第二,电力负荷数据高度不一,因为不同的零售商可能为不同的消费者服务。为此,提议了一个基于协商一致算法和长短期内存(LSTM)的全面分布的短期负载预报框架,这可能保护客户的隐私,满足准确的负载预报要求。具体地说,利用一个完全分布的学习框架进行分配培训,并采用协商一致技术满足保密隐私。案例研究表明,拟议的方法在准确性方面与集中的方法具有可比性,但拟议的方法显示了培训速度和数据隐私方面的优势。