Photovoltaic and solar power forecasting for smart grid energy management
Open accessAbstract
Due to the challenge of climate and energy crisis, renewable energy generation including solar generation has experienced significant growth. Increasingly high penetration level of photovoltaic (PV) generation arises in smart grid. Solar power is intermittent and variable, as the solar source at the ground level is highly dependent on cloud cover variability, atmospheric aerosol levels, and other atmosphere parameters. The inherent variability of large-scale solar generation introduces significant challenges to smart grid energy management. Accurate forecasting of solar power/irradiance is critical to secure economic operation of the smart grid. This paper provides a comprehensive review of the theoretical forecasting methodologies for both solar resource and PV power. Applications of solar forecasting in energy management of smart grid are also investigated in detail.
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Since weather fundamentally drives both variable generation and electricity demand, ML algorithms forecasting these quantities should draw from innovations in climate modeling and weather forecasting and in hybrid physics-plus-ML modeling techniques.
“since weather fundamentally drives both variable generation and electricity demand, ML algorithms forecasting these quantities should draw from innovations in climate modeling and weather forecasting (Section 8) and in hybrid physics-plus-ML modeling techniques [161,818,822].”
From Tackling climate change with machine learning (2023) · cites this paper as [822]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Wan, C., Zhao, J., Song, Y., Xu, Z., Lin, J., & Hu, Z.
- Affiliations
- Tsinghua University (CN)
- Hong Kong Polytechnic University (HK)
- Venue
- CSEE Journal of Power and Energy Systems
- Publisher
- Power System Technology Press
- Published
- 2015-12
- Volume
- 1
- Issue
- 4
- Pages
- 38-46
- ISSN
- 2096-0042 (ISSN-L)
- OpenAlex
- W2524035252
- Cited by
- 654
- References
- 67
- Citations by year
- 2026: 41 · 2025: 63 · 2024: 79 · 2023: 75 · 2022: 65 · 2021: 82 · 2020: 74 · 2019: 61 · 2018: 55 · 2017: 43 · 2016: 16
- FWCI
- 37.28
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- CSEE Journal of Power and Energy Systems (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Solar Radiation and Photovoltaics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Photovoltaic System Optimization Techniques
- Keywords
- photovoltaic power forecasting, solar irradiance forecasting, smart grid energy management, renewable energy integration, cloud cover variability, atmospheric aerosols, forecasting methods
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