生成モデルによる再生可能エネルギー発電量予測
Forecasting Renewable Energy Production with Generative Models (原題)
Chandrani Ray Chowdhury
🤖 gxceed AI 要約
日本語
本稿は、再生可能エネルギー発電の不確実性に対処するため、生成モデルを用いた確率的予測手法を概説する。従来のML/DLの限界を克服し、マイクログリッドの安定運用や経済性向上に寄与する。実践的なケーススタディと将来の研究方向も提示する。
English
This chapter reviews generative models for probabilistic forecasting of renewable energy production, addressing the limitations of traditional ML/DL in handling stochastic, nonlinear data. It demonstrates how probabilistic forecasts support microgrid stability, economic operation, and future research, including stochastic calculus and physics-aware modeling.
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大量導入に伴い、系統安定化と需給調整が喫緊の課題。本稿の確率的予測は、FIT後の市場競争下での発電事業者の収益最適化や、マイクログリッド運用に有用。
In the global GX context
Globally, the transition to renewable energy requires advanced forecasting for grid stability and market integration. This work aligns with ISSB/TCFD disclosure needs by providing tools for managing climate-related risks and opportunities in energy operations.
👥 読者別の含意
🔬研究者:Provides a comprehensive overview of generative models for probabilistic renewable forecasting, highlighting research gaps and future directions.
🏢実務担当者:Offers practical case studies and methods for improving microgrid stability and economic operation through probabilistic forecasts.
🏛政策担当者:Supports policy on renewable integration by demonstrating forecasting techniques that enhance grid reliability and cost-effectiveness.
📄 Abstract（原文）
Renewable energy generation is uncertain and intermittent due to its dependence on weather dynamics. This variability causes grid instability, energy imbalance, and operational challenges, particularly in microgrids. Therefore, accurate forecasting is crucial for energy management, grid stability, demand–response coordination, revenue optimization, storage planning, and cost reduction. However, traditional machine learning (ML) and deep learning (DL) approaches have limitations: they struggle with stochastic, nonlinear renewable energy data and produce only point forecasts. Generative models overcome these challenges by enabling probabilistic forecasting and multi-scenario generation. In this chapter, we present an operational view of renewable energy forecasting by describing the transition from traditional ML and DL to generative models. We then discuss practical scenarios and case studies and demonstrate how probabilistic forecasting supports microgrid stability, economic operation, and future research directions. Furthermore, we introduce basic stochastic calculus concepts for renewable energy forecasting and review forecasting scenarios from the perspective of multivariate, stochastic, and physics-aware modeling.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.21819326first seen 2026-08-27 04:54:24 · last seen 2026-08-27 04:56:29
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