BOOK-CHAPTER

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

Yao ChengChang XuDaisuke MashimaVrizlynn L. L. ThingYongdong Wu

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

Metrics

61
Cited By
19.93
FWCI (Field Weighted Citation Impact)
29
Refs
1.00
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

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
© 2026 ScienceGate Book Chapters — All rights reserved.