A Whole System Assessment of Novel Deep Learning Approach on Short-Term Load Forecasting

被引：22

作者：

Shi, Heng ^{[1
]}

Xu, Minghao ^{[1
]}

Ma, Qiuyang ^{[1
]}

Zhang, Chi ^{[1
]}

Li, Ran ^{[1
]}

Li, Furong ^{[1
]}

机构：

[1] Univ Bath, Dept Elect & Elect Engn, Bath BA2 7AY, Avon, England

来源：

PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON APPLIED ENERGY | 2017年 / 142卷

关键词：

Load Forecasting; deep learning; multi-system levels; aggregation and disaggregat on; smart metering; deep recurrent neural network; UNIT COMMITMENT;

D O I：

10.1016/j.egypro.2017.12.423

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Deep learning has been proven of great potential in various time-series forecasting applications. To exploit the potential and extendibility of deep learning in electricity load forecasting, this paper for the first time presents a comprehensive deep learning assessment on performing load forecasting at different levels through the power systems. The assessment is demonstrated via two extreme cases: 1) regional aggregated demand with an example of New England electricity load data, and 2) disaggregated household demand with examples of 100 individual households from Ireland. The state-of-the-art deep recurrent neural network is implemented for this assessment. Compared with the shallow neural network, the proposed deep model has improved the forecasting accuracy in terms of MAPE by 23% at aggregated level and RMSE by 5% at disaggregated level. (C) 2017 The Authors. Published by Elsevier Ltd.

引用

页码：2791 / 2796

页数：6

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