AI-Driven Solar Radiation Forecasting for Optimizing Energy Yield Across Diverse Geographical Locations
多様な地理的位置におけるエネルギー収量最適化のためのAI駆動の日射予測 (AI 翻訳)
Raed A. Shalwala
🤖 gxceed AI 要約
日本語
本論文は、気候変動緩和における太陽エネルギーの重要性と、日射変動に起因する課題を論じる。物理モデル、統計手法、機械学習を含む予測手法を分類し、機械学習による時系列分析の進展を強調する。さらに、ハイブリッド再生可能システムとタスク指向の協調最適化を提案する。
English
This paper discusses the importance of solar energy in climate change mitigation and challenges due to solar irradiance variability. It classifies forecasting methods (physical, statistical, machine learning) and highlights machine learning's role in improving accuracy with regional data. It proposes task-oriented coordinated optimization for hybrid renewable systems.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では太陽光発電が再生可能エネルギー主力電源として期待される一方、天候変動による出力変動が課題である。本論文のAI予測手法は、発電事業者や系統運用者にとって発電量の最適化や安定供給に資する知見を提供し、GX推進に寄与する。
In the global GX context
Globally, solar energy integration faces variability challenges. This paper's AI-driven forecasting methods can enhance grid management and energy yield, supporting the energy transition. The proposed coordinated optimization framework is relevant for hybrid renewable systems worldwide.
👥 読者別の含意
🔬研究者:Provides a structured review of solar forecasting methods and highlights machine learning advancements, useful for those exploring AI applications in renewable energy.
🏢実務担当者:Offers insights on selecting forecasting models for solar farm optimization and grid integration, aiding operational efficiency.
📄 Abstract（原文）
Solar energy is critical in the mitigation of climate change and this helps in reduction of carbon emissions into the atmosphere caused by fossil fuels. Nonetheless, the fluctuation in the generation of solar energy presents difficulties in the effectiveness of combining and functioning of solar technologies in the market. Correct solar resource forecasts are therefore essential in the proper management and strategic planning in power systems. Numerous methodologies of forecasting have appeared, primarily they are classified into physical models, statistical methods and machine learning methods. The first and second categories use familiar physical phenomena, and need less data to train the model. It is worth noting that simply classifying methods by machine learning methods are not comprehensive enough; even the duration of the forecast horizon is a decisive factor. Machine learning has largely influenced solar photovoltaic (PV) forecasting by making the use of solutions that take into account time-series data by now possible, especially since the availability of regional data is increasing. The choice of a model depends on the application and system characteristics, i.e. there is no single application-system characteristics that will be universally applicable. With the energy transition on the verge of happening, it is necessary to consider hybrid renewable and distributed generation systems since individual technology-related limitations are more evident with an increasing market integration. A solution is offered through task-oriented coordinated optimization, which will exclude the necessity of a strictly delineated hybridization framework. DOI : https://doi.org/10.52783/pst.3434
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.52783/pst.3434first seen 2026-06-05 04:56:04
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