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サイト選定
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、太陽光発電の最適設置地点選定と10年間の発電量予測を行う枠組みを提案。NASA POWERデータと物理的PVモデルを用い、Random Forestで高い予測精度（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や投資判断を支援する。
English
This study integrates satellite data and machine learning to identify optimal solar installation sites and forecast 10-year energy output. Using NASA POWER data and physical PV modeling, Random Forest 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
日本の再生可能エネルギー導入拡大やFIP制度下での発電所立地選定に有用。SSBJ開示における再生可能エネルギー調達戦略や、地域密着型の太陽光発電計画にAI活用の具体例を提供する。
In the global GX context
Supports global renewable energy transition by providing an AI-driven framework for solar site selection and yield prediction, aligning with TCFD/ISSB climate metrics and transition finance needs for credible decarbonization pathways.
👥 読者別の含意
🔬研究者:Provides a reproducible ML-geospatial methodology for solar forecasting and site ranking.
🏢実務担当者:Useful for renewable project developers and corporate PPAs to optimize site selection and energy yield estimates.
🏛政策担当者:Informs evidence-based renewable energy planning and investment incentives.
📄 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.21702487first seen 2026-08-20 04:36:13
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