太陽光発電の日射量予測とエネルギー最適化によるネット・ゼロ・エネルギー・ビル達成
Solar irradiance forecasting and energy optimization for achieving nearly net zero energy building (原題)
Naveen Chakkaravarthy, A., Subathra, M. S. P., Jerin Pradeep, P., Manoj Kumar, Nallapaneni
🤖 gxceed AI 要約
日本語
本研究は、インドの標準的な建物を対象に、太陽光発電の日射量予測とBEoptによるエネルギー最適化を行い、エネルギー消費を192.2 MMBtu/年から109.1 MMBtu/年に削減し、7kWの太陽光発電システムでネット・ゼロ・エネルギーを達成することを示した。予測にはランダムフォレストを用い、RMSEは11〜24%で良好な精度を得た。
English
This study forecasts solar irradiance using Random Forest and optimizes building energy with BEopt for a standard Indian building, reducing energy consumption from 192.2 to 109.1 MMBtu/yr and achieving net-zero with a 7 kW PV system. The forecasting model achieved RMSE of 11-24% and R-values of 0.8-0.9.
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ZEB（ネット・ゼロ・エネルギー・ビル）推進や省エネ基準強化の文脈で、太陽光予測と最適化の実証例として参考になる。ただし、日本の気候・建築基準とは異なるため、適用には調整が必要。
In the global GX context
This paper provides empirical evidence on solar forecasting and building optimization for net-zero energy, relevant to global efforts on building decarbonization and renewable integration. It offers a case study from India, contributing to the broader literature on energy efficiency in emerging economies.
👥 読者別の含意
🔬研究者:Provides a methodology for solar irradiance forecasting and building energy optimization that can be compared with other regional studies.
🏢実務担当者:Offers insights into achieving net-zero energy in buildings through PV sizing and energy efficiency measures, useful for design and retrofitting projects.
🏛政策担当者:Highlights the potential of solar forecasting and building optimization in meeting net-zero targets, informing building codes and renewable energy policies.
📄 Abstract（原文）
Solar energy and the concept of passive solar architecture are being increased in several areas to attain the net-zero energy concept. This paved the way for an increase in the need of solar irradiance forecasting for both solar PV applications and Passive Solar Architectural buildings. First, solar irradiance forecasting was done with 131 400 data sets (1-h data for 15 years) which was split into monthly mean for every year. This model was evaluated by forecasting the post-consecutive years one by one with the pre-consecutive years which includes the pre-forecasted years. This model was shown to have RMSE values of 11% to 24% for various seasonal forecasting using the Random Forest Algorithm in WEKA, which gave the annual irradiance results nearer to the PV Sol energy forecasting results. The R-value was in the range of 0.8 to 0.9 for various seasons which is good. Building Energy Optimization was carried out using BEopt 2.8 software designed by NREL. The chosen building was set to the standard parameters in India, and then, the optimization was done with various customized parameters and systems available in India to reduce the energy consumption from 192.2 MMBtu/yr to 109.1 MMBtu/yr with a 7 kW Solar PV System to attain the net-zero energy concept.
🔗 Provenance — このレコードを発見したソース
- base https://doi.org/10.1063/1.5034382first seen 2026-09-01 12:03:29
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