機械学習と地理空間データを用いた太陽光発電予測と最適立地選定
Solar Power Generation Prediction and Optimal Site Selection using Machine Learning and Geospatial Data (原題)
P. Vibin Sri Balaji, Srinivasan R
🤖 gxceed AI 要約
日本語
本研究は、衛星データと機械学習を統合し、太陽光発電の最適立地選定と10年間の発電量予測を行う枠組みを提案する。NASA POWERデータとランダムフォレスト回帰を用い、高い予測精度（R²≈0.91）を達成。地理的に適応可能な汎用フレームワークで、再生可能エネルギー計画と投資判断を支援する。
English
This study proposes an AI-based framework integrating satellite data and machine learning to identify optimal solar plant sites and forecast 10-year energy output. Using NASA POWER data and Random Forest regression, it achieves high predictive accuracy (R²≈0.91). The geographically adaptable framework supports renewable energy planning and investment decisions.
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
日本では再生可能エネルギー導入拡大が進む中、本手法は自治体や企業の太陽光発電立地選定や発電量予測に活用でき、FIT後の自立化や再エネ調達の効率化に寄与する。SSBJ開示における再エネ調達戦略の裏付けとしても有用。
In the global GX context
Globally, this framework supports the transition to renewable energy by providing a scalable, data-driven approach to solar site selection and generation forecasting. It aligns with ISSB and CSRD disclosure needs by enabling more accurate and transparent renewable energy planning and investment decisions.
👥 読者別の含意
🔬研究者:Provides a validated ML methodology for solar forecasting and site selection that can be extended or compared with other regions.
🏢実務担当者:Offers a practical tool for identifying optimal solar sites and forecasting output, useful for project development and PPA negotiations.
🏛政策担当者:Demonstrates how AI can support strategic renewable energy planning and policy development.
📄 Abstract（原文）
The global transition towards renewable energy lacks intelligent frameworks for solar plant locations optimization and long-term energy generation forecasting. This research presents a comprehensive AI based methodology for identifying optimal SOLAR plant installation sites and forecasting energy output for a period of 10-year. The proposed methodology integrates satellite-derived meteorological data from NASA POWER database, photovoltaic performance modelling, multi-parameter feature engineering, Random Forest regression, and suitability-based spatial ranking. Annual energy generation is computed in MWh using physical PV system parameters including panel area, efficiency, and performance ratio. A generalized and geographically adaptable framework is developed to enable scalable renewable energy planning. The initial Experimentation projects high predictive accuracy (R² ≈ 0.91) and low error margins, validating the effectiveness of the approach. The framework supports strategic energy infrastructure planning, investment decision-making, and sustainable policy development.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.21702488first seen 2026-08-20 04:37:12
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