Energy Loss Mechanisms in Photovoltaic Systems: Assessment and Mitigation Approaches
太陽光発電システムにおけるエネルギー損失メカニズム：評価と緩和アプローチ (AI 翻訳)
Cem Emeksiz, Muhammed Musa Fındık
🤖 gxceed AI 要約
日本語
本論文は、太陽光発電システムにおけるエネルギー損失のメカニズムを分析し、その評価と緩和手法を論じる。特に、人工知能・機械学習を活用した損失予測と故障検出の有効性を強調し、従来の解析手法と比較した利点を示す。また、現状の学術・産業的解決策を概観し、今後の研究・応用の方向性を提示する。
English
This paper analyzes energy loss mechanisms in photovoltaic systems and discusses assessment and mitigation approaches. It highlights the use of artificial intelligence and machine learning for loss prediction and fault detection, offering advantages over traditional methods. The study reviews current academic and industrial solutions and presents future research directions.
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重要視されており、効率向上は発電コスト低減や導入拡大に直結する。本論文の損失分析とAI予測は、日本のPV運用・保守現場での実装が期待される技術である。
In the global GX context
Globally, photovoltaic capacity is expanding rapidly, making efficiency improvements critical for levelized cost of electricity reduction. The integration of AI for loss prediction can enhance operational optimization and fault management across diverse climatic conditions.
👥 読者別の含意
🔬研究者:Useful for researchers in photovoltaic efficiency and AI-based fault detection, providing a structured review of loss mechanisms and data-driven methods.
🏢実務担当者:PV plant operators and engineers can leverage AI approaches for real-time monitoring and predictive maintenance to reduce energy losses.
🏛政策担当者:Policymakers may consider supporting R&D in AI-driven renewable energy optimization to accelerate decarbonization targets.
📄 Abstract（原文）
Energy is one of the most critical inputs for the socio-economic development and sustainable prosperity of humanity. Historically, fossil fuels like coal, oil, and natural gas formed the backbone of the global energy supply. However, their limited reserves and environmental impacts have raised serious sustainability concerns. According to the International Energy Agency, fossil resources still met approximately 81% of the global primary energy demand in 2019. This dependence drives greenhouse gas emissions, the climate crisis, environmental degradation, and import reliance. Consequently, countries are increasingly turning to renewable sources to ensure energy security and reduce carbon footprints. Solar energy stands out for its high potential, modularity, ease of technological integration, and zero emissions. Photovoltaic (PV) systems, converting solar radiation directly into electricity, are widely adopted. However, PV systems face various losses in real-world conditions, generally classified as PV array capture losses and system losses. Irradiance irregularities, temperature increases, and module mismatches cause capture losses, while cable resistance, inverter inefficiencies, and transformer losses represent system losses. Dust accumulation, partial shading, and environmental factors also directly impact efficiency. Therefore, the accurate prediction and early detection of these losses are critical for investors and operators. Recently, artificial intelligence and machine learning approaches have been increasingly applied to model these complex losses and predict faults. Unlike traditional analytical methods, data-driven algorithms provide more flexible and precise solutions under non-linear dynamic field conditions. This study analyzes energy losses in PV systems, discusses current academic and industrial solutions, and presents future projections for research and sectoral applications.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.66248/cumfad.1968672first seen 2026-07-10 04:59:23 · last seen 2026-07-10 05:28:26
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。