An Empirical Analysis of Machine Learning Algorithms for Solar Power Forecasting in a High Dimensional Uncertain Environment-Reference-Cited by-同舟云学术

An Empirical Analysis of Machine Learning Algorithms for Solar Power Forecasting in a High Dimensional Uncertain Environment

Published:2022-11-14 Issue: Volume: Page:1-16
ISSN:0256-4602
Container-title:IETE Technical Review
language:en
Short-container-title:IETE Technical Review

Author:

Rai Amit¹,Shrivastava Ashish²^ORCID,Jana K. C.¹

Affiliation:

1. Electrical Engineering Department, Indian Institute of Technology (ISM), Dhanbad, India

2. Skill Faculty of Engineering and Technology, Shri Vishwakarma Skill University, Gurugram, India

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/02564602.2022.2136270

Reference59 articles.

1. IEA, International Renewable Energy Agency, United Nations Statistics Division, The World Bank, and World Health Organization, “The energy progress report,” IEA, IRENA, UNSD, WB, WHO (2019), Track. SDG 7 Energy Progress Report. 2019, Washingt. DC, 2019.

2. S. F. Singer, “World energy outlook,” Symposium Papers – Energy Model., 567–75, 1982.

3. Solar energy in progress and future research trends

4. BP, “Energy outlook 2022 edition 2022 explores the key uncertainties surrounding the energy transition,” 2022, p. 109.

5. IRENA, Renewable Capacity Statistics 2019. Abu Dhabi: International Renewable Energy Agency (IRENA), 2019. Available: https://www.irena.org/publications/2019/Mar/Renewable-Capacity-Statistics-2019.

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