A Forecast-to-Deployment Framework for Sustainable Rooftop Hybrid Renewable Energy Planning: A City-Scale Assessment of Dhaka
持続可能な屋上ハイブリッド再生可能エネルギー計画のための予測から展開へのフレームワーク：ダッカ市の都市規模評価 (AI 翻訳)
Hossain ML, Huda MN, Shams SMN, Ullah SM
🤖 gxceed AI 要約
日本語
本研究は、LSTM予測モデルとSAMシミュレーションを統合し、ダッカ市の屋上太陽光・風力・ハイブリッドシステムの技術経済評価を行うフレームワークを提案。商用ハイブリッド構成でLCOE 5.81 BDT/kWh、年間128.16 MWh、CO2削減79.46 tCO2を達成し、都市規模展開で夜間電力供給の可能性を示した。
English
This study proposes a forecast-to-deployment framework integrating LSTM-based renewable energy forecasting with SAM simulations for rooftop PV, wind, and hybrid systems in Dhaka. The optimized commercial hybrid achieves LCOE of 5.81 BDT/kWh, 128.16 MWh/year, and 79.46 tCO2 mitigation. City-scale deployment under various scenarios could provide 4.4–46.5 MWh/night of electricity, supporting 220–2325 households during non-solar periods.
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
日本の都市でも屋上太陽光・風力のハイブリッド導入が進む中、本フレームワークは予測と経済性評価を統合した意思決定支援手法として参考になる。特に夜間電力供給の強化策として風力統合の有効性を示しており、日本の地域エネルギー計画にも応用可能。
In the global GX context
This framework bridges the gap between renewable energy forecasting and practical deployment, offering a replicable approach for cities worldwide. The integration of wind with rooftop PV without battery storage provides a cost-effective solution for nighttime renewable electricity, relevant for urban energy transition in many developing and developed contexts.
👥 読者別の含意
🔬研究者:Provides a validated methodology linking ML forecasting to techno-economic assessment for urban renewable planning.
🏢実務担当者:Offers a decision-support tool for city planners and energy developers to evaluate rooftop hybrid systems under different adoption scenarios.
🏛政策担当者:Highlights the potential of rooftop wind-PV hybrids to enhance nighttime renewable electricity, informing urban energy policy and carbon mitigation targets.
📄 Abstract（原文）
<title>Abstract</title> <p>The increasing demand for sustainable urban energy systems requires practical frameworks that transform accurate renewable energy forecasts into economically viable deployment strategies. Although recent advances in renewable energy forecasting have significantly improved prediction accuracy, few studies have translated validated forecasting outputs into techno-economic assessments to support city-scale rooftop renewable energy planning. This study presents a forecast-to-deployment framework that integrates validated renewable energy forecasting outputs with System Advisor Model (SAM) simulations to evaluate rooftop photovoltaic (PV), wind, and hybrid renewable energy systems in Dhaka, Bangladesh. Building upon a previously validated Long Short-Term Memory (LSTM) forecasting model, hourly renewable energy density predictions were employed as meteorological inputs for representative residential (250 m²) and commercial (800 m²) rooftop configurations. Grid-connected PV, wind, and hybrid systems were assessed in terms of annual electricity generation, levelized cost of electricity (LCOE), spatial efficiency, carbon emission mitigation, renewable energy availability, and city-scale deployment potential under 15%, 35%, and 60% rooftop adoption scenarios. The optimized commercial hybrid configuration generated 128.16 MWh/year with the lowest LCOE of 5.81 BDT / kWh, while mitigating approximately 79.46 tCO₂/year. Compared with PV-only systems, rooftop wind integration significantly enhanced nighttime renewable electricity availability without the additional investment and efficiency losses associated with battery storage. City-scale deployment could provide 4.4–46.5 MWh/night of renewable electricity, supplying approximately 220–2325 households during non-solar periods. The proposed framework bridges validated renewable energy forecasting and practical rooftop deployment by transforming forecasted renewable energy resources into engineering decision-support for sustainable urban energy planning, carbon mitigation, and distributed renewable energy development in rapidly urbanizing cities.</p>
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.21203/rs.3.rs-10244199/v1first seen 2026-07-16 04:43:27
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