気候帯を横断した全球ソーラー・ウェザー・レジームを特定する説明可能AI：UMAP-HDBSCANとSHAPの適用

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:気候帯横断でソーラー・レジームを同定する説明可能な教師なし学習パイプラインの方法論的枠組みを提供する。

🏢実務担当者:立地の気候レジームに応じた最適傾斜角やPV設計の判断材料として活用できる。

🏛政策担当者:地域別の太陽資源特性を踏まえた再エネ導入計画・系統統合政策の立案に示唆を与える。

Solar photovoltaic (PV) generation is shaped by recurring combinations of irradiance and atmospheric conditions, yet many machine-learning approaches treat weather observations independently and overlook these repeatable patterns. This article develops an explainable unsupervised learning framework to identify and interpret solar-weather regimes across global climate zones. The study analyses 67,287 hourly meteorological observations from 17 cities representing tropical, subtropical, temperate, and cold climates using a three-stage analytical pipeline: Uniform Manifold Approximation and Projection (UMAP) for nonlinear dimensionality reduction, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) for regime discovery, and an XGBoost proxy classifier with Shapley Additive Explanations (SHAP) for transparent interpretation. Key Findings: Six distinct solar-weather regimes are identified, each characterised by coherent meteorological signatures and systematic variations. SHAP analysis indicates that irradiance partitioning (direct normal irradiance and diffuse horizontal irradiance) together with relative humidity and air temperature are the dominant discriminators of regime membership. Regime-specific photovoltaic design implications are substantial: optimal tilt angles vary by more than 40°, ranging from shallow configurations (≈7–8°) in diffuse-dominated regimes to steep configurations (>50°) under low-sun conditions. Novel Contributions: - Regime-based characterisation of solar-weather variability through joint analysis of irradiance components and meteorological variables - Adaptation of weather-regime concepts from climatology to solar energy applications - Explainable interpretation of unsupervised regime discovery via transparent proxy modelling with SHAP - Consistent multi-climate comparative analysis across 17 globally distributed cities spanning all major climate zones - Direct linkage between atmospheric regimes and photovoltaic system performance implications The proposed framework provides a data-driven basis for solar resource characterisation, actionable regime-level insights, climate-aware photovoltaic system design, and renewable energy planning across diverse climatic settings. Methodology: The analysis employs reanalysis-based hourly meteorological data (temperature, humidity, wind speed, pressure) and solar irradiance variables (GHI, DNI, DHI) from 17 cities. All data were screened to remove missing entries and non-physical values. Variables were standardised prior to unsupervised learning. UMAP enables nonlinear dimensionality reduction whilst preserving neighbourhood structure. HDBSCAN identifies dense regions corresponding to recurrent regimes without requiring predefined cluster numbers. XGBoost with SHAP provides transparent feature attribution and regime interpretation. Publication Status: Published in peer-reviewed journal Solar Energy (Elsevier) Received: 13 January 2026; Revised: 22 August 2026; Accepted: 17 September 2026 Available online: 22 September 2026 Open Access: CC BY license Audience & Applications: This work is relevant to: - Solar resource engineers and researchers - Photovoltaic system designers and energy planners - Climate and atmospheric scientists - Renewable energy policy makers and operators - Machine learning practitioners in energy systems - Utilities and grid operators integrating solar PV The framework supports improved solar resource assessment, climate-adaptive PV design, regime-informed forecasting, grid integration strategies, and renewable energy planning across diverse climatic settings. Keywords: Solar photovoltaic; solar-weather regimes; unsupervised learning; explainable AI; UMAP; HDBSCAN; SHAP; climate zones; renewable energy; irradiance; machine learning Funding: This work was supported by the Faculty of Electrical Engineering and Computer Science, Technical University of Ostrava, under the project "Research Platform for Digital Transformation and Society 5.0" (CZ.02.01.01/00/23_021/0012599), funded by the European Regional Development Fund within the Jan Amos Komenský Operational Program. Authors: Ohn Zin Lin¹*, Hnin Yee Aye¹, Paing Hein Soe², Libor Štěpanec¹, Eftichios Koutroulis³, Dagmar Juchelkova¹ ¹ Department of Applied Electronics, Faculty of Electrical Engineering and Computer Science, Technical University of Ostrava, Czech Republic ² Department of Mechanical and Industrial Engineering, Northeastern University, Boston, USA ³ School of Electrical and Computer Engineering, Technical University of Crete, Greece Citation (APA): Lin, O.Z., Aye, H.Y., Soe, P.H., Štěpanec, L., Koutroulis, E., & Juchelkova, D. (2026). Explainable AI for identifying global solar-weather regimes using UMAP-HDBSCAN and SHAP across climate zones. Solar Energy, 319, 115147. https://doi.org/10.1016/j.solener.2026.115147 Associated Dataset: The underlying meteorological and solar irradiance data are openly available on Zenodo under CC BY 4.0 license at https://doi.org/10.5281/zenodo.17570100 Corresponding Author: Ohn Zin Lin (ohn.zin.lin@vsb.cz) Department of Applied Electronics, Faculty of Electrical Engineering and Computer Science Technical University of Ostrava, 17. listopadu 2172/15, Ostrava 70800, Moravian-Silesian, Czech Republic

• Zenodo https://zenodo.org/records/22907547first seen 2026-09-24 04:12:46 · last seen 2026-09-28 04:33:06