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|Title:||Short-term residential load forecasting based on LSTM recurrent neural network||Authors:||Kong, Weicong
Dong, Zhao Yang
Hill, David J.
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2019||Source:||Kong, W., Dong, Z. Y., Jia, Y., Hill, D. J., Xu, Y. & Zhang, Y. (2019). Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions On Smart Grid, 10(1), 841-851. https://dx.doi.org/10.1109/TSG.2017.2753802||Journal:||IEEE Transactions on Smart Grid||Abstract:||As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households.||URI:||https://hdl.handle.net/10356/151360||ISSN:||1949-3053||DOI:||10.1109/TSG.2017.2753802||Rights:||© 2017 IEEE. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Journal Articles|
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