Optimum Power Forecasting Technique for Hybrid Renewable Energy Systems Using Deep Learning

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

Electric Power Components and Systems Pub Date : 2024-03-07 DOI:10.1080/15325008.2024.2316251

Shashank Singh, V. Subburaj, K. Sivakumar, R. Anil Kumar, M. S. Muthuramam, Ravi Rastogi, Vishal Ratansing Patil, A. Rajaram

引用次数: 0

Abstract

Power forecasting in large-scale electrical systems, comprising photovoltaic (PV), solar, and wind power, faces challenges due to geographical diffusion and temporal variations. Despite numerous st...

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使用深度学习的混合可再生能源系统最佳功率预测技术

大规模电力系统（包括光伏（PV）、太阳能和风能）的功率预测面临着地域扩散和时间变化带来的挑战。尽管有大量的研究表明，在大规模电力系统中进行电能预测并不容易。

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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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.