论文标题
使用定制的LSTM和GRU模型对家庭用电的短期预测
Short-term Prediction of Household Electricity Consumption Using Customized LSTM and GRU Models
论文作者
论文摘要
随着电力系统的发展，随着它变得越来越智能和交互式系统，同时随着可再生能源的更大渗透的灵活性的提高，对短期解决方案的需求预测将不可避免地变得越来越重要，对于设计和管理未来的网格，尤其是在个人家庭层面上。由于相当多的挥发性和不确定的因素，因此很难向单个能源用户投射电力需求，而不是大规模的住宅负载的总体消耗。本文提出了一个定制的GRU（封闭式复发单元）和长期记忆（LSTM）体系结构，以解决这个具有挑战性的问题。 LSTM和GRU是相对较新的，并且是最精心设计的深度学习方法。电力消耗数据集是从单个家庭智能电表中获得的。比较表明，在这种情况下，LSTM模型比替代预测技术对家庭级预测的性能更好。为了将基于NN的模型与基于常规统计技术的模型形成鲜明对比，基于ARIMA的模型还通过LSTM和GRU模型成果进行了基准，并在这项研究中进行了基准测试，以显示在收集的时间序列数据上提出的模型的性能。
With the evolution of power systems as it is becoming more intelligent and interactive system while increasing in flexibility with a larger penetration of renewable energy sources, demand prediction on a short-term resolution will inevitably become more and more crucial in designing and managing the future grid, especially when it comes to an individual household level. Projecting the demand for electricity for a single energy user, as opposed to the aggregated power consumption of residential load on a wide scale, is difficult because of a considerable number of volatile and uncertain factors. This paper proposes a customized GRU (Gated Recurrent Unit) and Long Short-Term Memory (LSTM) architecture to address this challenging problem. LSTM and GRU are comparatively newer and among the most well-adopted deep learning approaches. The electricity consumption datasets were obtained from individual household smart meters. The comparison shows that the LSTM model performs better for home-level forecasting than alternative prediction techniques-GRU in this case. To compare the NN-based models with contrast to the conventional statistical technique-based model, ARIMA based model was also developed and benchmarked with LSTM and GRU model outcomes in this study to show the performance of the proposed model on the collected time series data.
