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

Yao Cheng; Chang Xu; Daisuke Mashima; Vrizlynn L. L. Thing; Yongdong Wu

doi:10.1007/978-3-319-69179-4_51

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

Yao Cheng Chang Xu Daisuke Mashima Vrizlynn L. L. Thing Yongdong Wu

Year: 2017 Lecture notes in computer science Pages: 727-740 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-319-69179-4_51

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Keywords:

Computer science Granularity Artificial neural network Demand forecasting Smart grid Gradient boosting Boosting (machine learning) Artificial intelligence Reliability (semiconductor) Support vector machine Term (time) Data mining Machine learning Power (physics) Operations research Random forest Engineering

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Energy Load and Power Forecasting

Physical Sciences → Engineering → Electrical and Electronic Engineering

Smart Grid Energy Management

Physical Sciences → Engineering → Electrical and Electronic Engineering

Image and Signal Denoising Methods

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

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

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