PowerLSTM: Power Demand Forecasting Using Long Short-Term Memory Neural Network

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PowerLSTM: Power Demand Forecasting Using Long Short-Term Memory Neural Network
Title:
PowerLSTM: Power Demand Forecasting Using Long Short-Term Memory Neural Network
Other Titles:
International Conference on Advanced Data Mining and Applications
Keywords:
Publication Date:
14 October 2017
Citation:
Cheng Y., Xu C., Mashima D., Thing V.L.L., Wu Y. (2017) PowerLSTM: Power Demand Forecasting Using Long Short-Term Memory Neural Network. In: Cong G., Peng WC., Zhang W., Li C., Sun A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science, vol 10604. Springer, Cham
Abstract:
Power demand forecasting is a critical task to achieve efficiency and reliability in the smart grid in terms of demand response and resource allocation. This paper proposes PowerLSTM, a power demand forecasting model based on Long Short-Term Memory (LSTM) neural network. We calculate the feature significance and compact our model by capturing the features with the most important weights. Based on our preliminary study using a public dataset, compared to two recent works based on Gradient Boosting Tree (GBT) and Support Vector Regression (SVR), PowerLSTM demonstrates a decrease of 21.80% and 28.57% in forecasting error, respectively. Our study also reveals that metering/forecasting granularity at once every 30 min can bring higher accuracy than other practical granularity options.
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PublisherCopyrights
Funding Info:
Description:
This is a post-peer-review, pre-copyedit version of an article published in International Conference on Advanced Data Mining and Applications. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-69179-4_51
ISBN:
978-3-319-69179-4
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