Evaluation of the new model of renewable energy economic growth under the background of big data
ビッグデータを背景とした再生可能エネルギー経済成長の新モデルの評価 (AI 翻訳)
Lihong Meng
🤖 gxceed AI 要約
日本語
ビッグデータを活用し、再生可能エネルギー（RE）の経済成長評価のための新しいモデルを提案。従来の静的な負荷曲線や平均出力仮定に代わり、多源異種データを統合し、優先ディスパッチルールと動的制約修正を採用。実験ではRE吸収量1582MW、抑制率8.32%を達成し、従来手法を上回る。これら技術指標を経済成長に結び付ける変換フレームワークを構築し、化石燃料削減、産業中断損失低減、雇用創出への貢献を示した。
English
This paper proposes a novel big data-driven model for evaluating renewable energy (RE) economic growth, integrating multi-source heterogeneous data and dynamic constraint correction. The model achieves 1582 MW RE absorption and 8.32% curtailment, outperforming traditional methods. It establishes a conversion framework linking technical indicators (absorption, curtailment) to economic growth via fossil fuel reduction, improved stability, and job creation, providing a computable bridge between RE performance and economic indicators.
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
日本では再生可能エネルギーの導入拡大と経済成長の両立が課題。本モデルはビッグデータを活用し、RE吸収率向上と経済効果を定量的に結び付ける手法を提供しており、日本のエネルギー政策や企業の投資判断に応用可能。
In the global GX context
This paper offers a quantitative framework linking renewable energy absorption and curtailment to economic growth indicators, relevant for global energy transition planning. It demonstrates how big data can improve RE integration and provides a tool for policymakers and investors to assess the economic benefits of renewable energy deployment.
👥 読者別の含意
🔬研究者:Provides a novel evaluation model and conversion framework that can be further refined or applied to other regions.
🏢実務担当者:For energy companies and grid operators, the model offers a data-driven approach to optimize RE dispatch and quantify economic impacts.
🏛政策担当者:Offers a methodology to assess the economic growth benefits of renewable energy policies, supporting evidence-based decision-making.
📄 Abstract（原文）
In the era of big data, this paper proposes a novel evaluation model for renewable energy (RE) economic growth, namely, the “big data-driven time-series production simulation and absorption evaluation model.” Unlike traditional methods that rely on static load curves and average output assumptions, this model integrates multi-source heterogeneous data (historical meteorology, real-time generation, and grid operation) and employs a priority dispatch rule with dynamic constraint correction. The experimental results show that the proposed model achieves an RE absorption value of 1582 MW and a curtailment ratio of 8.32%, outperforming traditional algorithms (1336 MW and 14.32%, respectively). To link these technical indicators to economic growth, this paper establishes a conversion framework: (1) higher RE absorption reduces fossil fuel consumption; (2) lower curtailment ratio improves power supply stability, reducing industrial interruption losses; and (3) expanded RE deployment creates green employment opportunities in equipment manufacturing and maintenance sectors. Based on this framework, the proposed model contributes to economic growth through enhanced energy security, cost savings, and job creation. The core contribution of this paper is to provide a computable bridge between RE absorption performance and economic growth indicators.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1063/5.0326323first seen 2026-05-15 19:45:32
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