ADDNF: Simulation Code and Datasets for "A Dynamic DEMATEL-Neuro-Fuzzy Framework for Identifying Transition Barriers and Optimizing Energy Management in BESS-Based Renewable Energy Systems"
ADDNF: 「BESSベース再生可能エネルギーシステムにおける移行障壁の特定とエネルギー管理最適化のための動的DEMATEL-ニューロファジーフレームワーク」のシミュレーションコードとデータセット (AI 翻訳)
Rana Amirova
🤖 gxceed AI 要約
日本語
本リポジトリは、BESSベースの再生可能エネルギーシステムにおける移行障壁（再生可能エネルギーの断続性、SOC不安定性、バッテリー劣化など）を動的DEMATELモデルで定量化し、適応型ニューロファジー制御を最適化するADDNFフレームワークのコードとデータを提供する。実データ（NSRDB Baku、NASAバッテリー劣化データ）を用いた20シードの感度分析により、提案手法の有効性を示している。
English
This repository provides the code and datasets for the ADDNF framework, which quantifies transition barriers (renewable intermittency, SOC instability, battery degradation) via a dynamic DEMATEL model and optimizes an adaptive neuro-fuzzy controller for BESS-based renewable systems. Using real NSRDB Baku and NASA battery degradation data, a 20-seed sensitivity analysis demonstrates the method's effectiveness.
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に伴い、蓄電池システムの効率的運用と系統安定化が重要課題。本フレームワークは、移行障壁の定量化と制御最適化を統合する点で、日本のFIT終了後の自立型エネルギー管理や、電力系統の需給調整に示唆を与える。
In the global GX context
Globally, the integration of BESS with renewable energy is critical for grid stability and energy transition. This framework offers a novel approach to quantify transition barriers and optimize control, relevant for regions with high renewable penetration and battery degradation concerns.
👥 読者別の含意
🔬研究者:Provides a reproducible simulation framework for integrating barrier analysis with neuro-fuzzy control in BESS systems.
🏢実務担当者:Offers a method to optimize BESS operation and manage battery degradation, potentially reducing operational costs.
🏛政策担当者:Highlights the importance of considering transition barriers in renewable energy policy and grid planning.
📄 Abstract（原文）
This repository contains the complete simulation code, source datasets, and publication-quality figures accompanying the manuscript "A Dynamic DEMATEL-Neuro-Fuzzy Framework for Identifying Transition Barriers and Optimizing Energy Management in BESS-Based Renewable Energy Systems" (R. Amirova, Azerbaijan Technical University). The proposed Adaptive DEMATEL-Driven Neuro-Fuzzy Framework (ADDNF) quantifies interacting transition barriers (renewable intermittency, SOC instability, battery degradation, forecasting uncertainty, and others) online through a Dynamic DEMATEL model, compressing them into an Adaptive Transition Barrier Index (ATBI) that drives an adaptive neuro-fuzzy controller with an online-verified Lyapunov stability guarantee and a Battery Health Preservation Module. Contents:- simulation_code/: Python simulation and plotting code (data_loader.py, sim_addnf.py, analysis.py, sensitivity.py) that reproduces all figures, tables, and the 20-seed sensitivity analysis reported in the manuscript, using real NSRDB Baku (Azerbaijan) irradiance/temperature data and NASA RW21-RW24 battery degradation data (ADDNF_NSRDB_NASA_Datasets.xlsx).- figures_600dpi_png/ and figures_600dpi_pdf/: the 9 manuscript figures at 600 DPI (PNG) and as resolution-independent vector graphics (PDF). All results are fully reproducible from the included code and data (see README.md for instructions). This is a simulation study grounded in real irradiance, temperature, and battery-degradation data; declared modeling assumptions (PV array size, load profile, nominal battery capacity) are documented in the accompanying manuscript. Keywords: DEMATEL, neuro-fuzzy control, battery energy storage systems (BESS), Lyapunov stability, transition barrier index, battery degradation, photovoltaic systems, renewable energy management
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.21394424first seen 2026-08-05 04:45:01
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