利用基于 LSTM 的深度学习模型加强当地能源社区的电力需求预测

IF 1.7 4区工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Components and Systems Pub Date : 2024-04-04 DOI:10.1080/15325008.2024.2316246

M. Pushpavalli, D. Dhanya, Megha Kulkarni, R. Rajitha Jasmine, B. Umarani, M. RamprasadReddy, Durga Prasad Garapati, Ajay Singh Yadav, A. Rajaram

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引用次数: 0

摘要

在追求准确的电力需求预测的过程中，深度学习算法得到了应用，尽管其前提条件是大量的...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Enhancing Electrical Power Demand Prediction Using LSTM-Based Deep Learning Models for Local Energy Communities

The pursuit of accurate electrical power demand forecasting has led to the application of deep learning algorithms, notably demonstrating promising outcomes despite the prerequisite of substantial ...

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来源期刊

Electric Power Components and Systems 工程技术-工程：电子与电气

CiteScore

2.70

自引率

6.70%

发文量

审稿时长

7.2 months

期刊介绍： Electric Power Components and Systems publishes original theoretical and applied papers of permanent reference value related to the broad field of electric machines and drives, power electronics converters, electromechanical devices, electrical equipment, renewable and sustainable electric energy applications, and power systems. Specific topics covered include: -Electric machines- Solid-state control of electric machine drives- Power electronics converters- Electromagnetic fields in energy converters- Renewable energy generators and systems- Power system planning- Transmission and distribution- Power system protection- Dispatching and scheduling- Stability, reliability, and security- Renewable energy integration- Smart-grid and micro-grid technologies.