A Review of Methods for Estimating the Power Generation of Invisible Solar Sites

Lai, Yi-Hui; Wu, Yuan‐Kang

doi:10.1109/is3c50286.2020.00115

Cited by 2 publications

(1 citation statement)

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“…Invisible power can lead to the underestimation of power demand during extreme weather conditions, in addition to impacting the stability of the power system. A review of methods conducted utilizing historical data discusses studies using fuzzy models, ANNs, SVR [96]. The authors note the importance of employing simplified approaches that do not require the historical records of many variables due to the difficulty of their collection for grid operators.…”

Section: Solar Power Forecastingmentioning

confidence: 99%

Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions

Yousef,

Rocha-Meneses

2023

Energies

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This review paper provides a summary of methods in which artificial intelligence (AI) techniques have been applied in the management of variable renewable energy (VRE) systems, and an outlook to future directions of research in the field. The VRE types included are namely solar, wind and marine varieties. AI techniques, and particularly machine learning (ML), have gained traction as a result of data explosion, and offer a method for integration of multimodal data for more accurate forecasting in energy applications. The VRE management aspects in which AI techniques have been applied include optimized power generation forecasting and integration of VRE into power grids, including the aspects of demand forecasting, energy storage, system optimization, performance monitoring, and cost management. Future directions of research in the applications of AI for VRE management are proposed and discussed, including the issue of data availability, types and quality, in addition to explainable artificial intelligence (XAI), quantum artificial intelligence (QAI), coupling AI with the emerging digital twins technology, and natural language processing.

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Section: Solar Power Forecastingmentioning

confidence: 99%