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Erschienen in:
Placement Optimization for Field Effect Transistors Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

Multivariable Load Prediction Using LSTM

verfasst von : G. Satya Rohan, V. Sailaja, K. Deepa, Abhijith Prakash

Erschienen in: Recent Advances in Hybrid and Electric Automotive Technologies

Verlag: Springer Nature Singapore

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Abstract

Power supply regulation and load forecast are important factors in electric power distribution systems. The advent and ever-expanding adoption of renewables and distributed energy resources in the energy sector have introduced a lot of complexity into the day-to-day operations and maintenance of a wide-area power grid. Implementation of big data analysis and deep learning tools in power distribution systems has enabled predictive maintenance, grid health monitoring, demand forecasting, and reliability analysis, and also provided a host of other features for overall improvement of grid operations. A thorough analysis reflecting presents and future patterns can aid in critical decisions regarding generation capacity, transmission, and distribution systems for a successful load flow system planning. The focus of this paper is on ways to estimate load using the deep learning technique Long short-term memory (implemented by Python programming language).

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Vorheriges Kapitel Estimation of Tire Scaling Coefficients and Simulation of On-Road Vehicle Behaviour
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Metadaten
Titel
Multivariable Load Prediction Using LSTM
verfasst von
G. Satya Rohan
V. Sailaja
K. Deepa
Abhijith Prakash
Copyright-Jahr
2022
Verlag
Springer Nature Singapore
Buch
Recent Advances in Hybrid and Electric Automotive Technologies

Print ISBN: 978-981-19-2092-9

Electronic ISBN: 978-981-19-2091-2

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-981-19-2091-2_24

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