Support vector machine based prediction of photovoltaic module and power station parameters-Reference-Cited by-同舟云学术

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Support vector machine based prediction of photovoltaic module and power station parameters

Published:2020-02-04 Issue:3 Volume:17 Page:219-232
ISSN:1543-5075
Container-title:International Journal of Green Energy
language:en
Short-container-title:International Journal of Green Energy

Author:

Ahmad Ashfaq¹²,Jin Yi¹,Zhu Changan¹,Javed Iqra³⁴,Waqar Akram M.¹,Buttar Noman Ali⁵

Affiliation:

1. Department of Precision Machinery & Instrumentation, University of Science and Technology of China, Hefei, China

2. Department of Electronics & Electrical Systems, The University of Lahore, Lahore, Pakistan

3. Department of Informatics and Systems, School of Science and Technology, University of Management and Technology, Lahore, Pakistan

4. Department of Mechanical Engineering, University of Malaya, Kuala Lumpur, Malaysia

5. School of Agricultural Equipment Engineering, Jiangsu University, Zhenjiang, China

Publisher

Informa UK Limited

Subject

Renewable Energy, Sustainability and the Environment

Link

https://www.tandfonline.com/doi/pdf/10.1080/15435075.2020.1722131

Reference37 articles.

1. Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production

2. Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques

3. Automatic parameter selection for polynomial kernel

4. Locally recurrent neural networks for wind speed prediction using spatial correlation

5. A New Strategy for Accurately PredictingI-VElectrical Characteristics of PV Modules Using a Nonlinear Five-Point Model

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4. Hybrid Photovoltaic Output Forecasting Model with Temporal Convolutional Network Using Maximal Information Coefficient and White Shark Optimizer;Sustainability;2024-07-17

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