标题： 使用深度循环神经网络预测电力消耗 显示英文标题

标题： Predicting Electricity Consumption using Deep Recurrent Neural Networks

摘要： 电力消耗在过去几十年中呈指数级增长。 这种增长给电力分销商带来了沉重负担。 因此，预测未来的电力消耗需求将使电力分销商占据优势。 预测电力消耗需要许多参数。 本文提出了两种方法，一种使用循环神经网络（RNN），另一种使用长短期记忆（LSTM）网络，这些模型仅考虑过去的电力消耗来预测未来的电力消耗。 这些模型在公开的伦敦智能电表数据集上进行了测试。 为了评估RNN和LSTM网络预测电力消耗的适用性，它们被用于预测给定时间段内单个房屋和一组房屋的电力消耗。 预测的时间范围包括每日、每三个月和13个月，涵盖了短期、中期和长期预测。 这两种RNN和LSTM网络的平均均方根误差为0.1。 显示英文摘要

摘要： Electricity consumption has increased exponentially during the past few decades. This increase is heavily burdening the electricity distributors. Therefore, predicting the future demand for electricity consumption will provide an upper hand to the electricity distributor. Predicting electricity consumption requires many parameters. The paper presents two approaches with one using a Recurrent Neural Network (RNN) and another one using a Long Short Term Memory (LSTM) network, which only considers the previous electricity consumption to predict the future electricity consumption. These models were tested on the publicly available London smart meter dataset. To assess the applicability of the RNN and the LSTM network to predict electricity consumption, they were tested to predict for an individual house and a block of houses for a given time period. The predictions were done for daily, trimester and 13 months, which covers short term, mid-term and long term prediction. Both the RNN and the LSTM network have achieved an average Root Mean Square error of 0.1.