Hybrid Energy Storage Dataset
ハイブリッドエネルギー貯蔵データセット (AI 翻訳)
Ziya07
🤖 gxceed AI 要約
日本語
本データセットは、ハイブリッド再生可能エネルギー貯蔵システム（HESS）の運用データを5分間隔で収録し、太陽光・風力発電、系統電力、蓄電池・スーパーキャパシタの状態、水素生成、負荷需要、供給電力、電力損失、効率クラスを含む。付属のJupyter Notebookは、LSTM-GRUによる多タスク学習（回帰と分類）とSHAP/LIMEによる説明可能性分析を実装している。合成データであり、個人情報を含まない。
English
This dataset contains 5-minute interval operational data from a synthetic Hybrid Energy Storage System (HESS), including solar/wind generation, grid power, battery/supercapacitor states, hydrogen production, load demand, supplied power, power loss, and efficiency classes. The accompanying Jupyter Notebook implements multi-task LSTM-GRU models for regression and classification, with SHAP/LIME explainability. Synthetic data, no personal information.
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
日本の再生可能エネルギー導入拡大に伴い、蓄電池・水素等のハイブリッド貯蔵システムの効率評価は重要。AIによる効率予測と説明可能性は、SSBJ開示や再エネ投資判断に有用。
In the global GX context
As global grids integrate more renewables, hybrid storage efficiency is critical. AI-based efficiency prediction with explainability supports TCFD/ISSB-aligned climate risk assessment and transition finance decisions.
👥 読者別の含意
🔬研究者:Provides a reproducible dataset and multi-task LSTM-GRU framework for energy efficiency prediction with explainability.
🏢実務担当者:Useful for developing AI-driven energy management systems to optimize storage operations and reduce power loss.
🏛政策担当者:Highlights the potential of AI in renewable integration and efficiency, informing policy on clean energy infrastructure.
📄 Abstract（原文）
This repository contains the dataset and implementation code used in the study entitled “Explainable Multi-Task LSTM–GRU Framework for Power Loss Prediction and Energy Efficiency Assessment in Hybrid Renewable Energy Storage Systems.” The dataset contains time-ordered observations recorded at 5-minute intervals and represents the operational behaviour of a synthetic Hybrid Energy Storage System (HESS). It includes renewable energy generation, grid power, energy storage conditions, hydrogen production, load demand, supplied power, power loss, and efficiency-class information. The main variables include solar power, wind power, grid power, battery state of charge, supercapacitor charge, hydrogen production, load demand, power supplied, and power loss. The efficiency classes are defined using the following power-loss thresholds: High efficiency: power loss ≤ 2 kW Medium efficiency: power loss between 2 and 4 kW Low efficiency: power loss > 4 kW The accompanying Jupyter Notebook implements the preprocessing, time-series sliding-window construction, multi-task LSTM and GRU architectures, regression and classification analyses, statistical evaluation, and SHAP- and LIME-based explainability analyses reported in the associated manuscript. Original dataset source: Ziya (Kaggle username: ziya07), “Hybrid Energy Storage Dataset,” Kaggle. https://www.kaggle.com/datasets/ziya07/hybrid-energy-storage-dataset Accessed: 3 August 2026. The original dataset is distributed on Kaggle under the CC0: Public Domain license. The dataset is archived here together with the implementation code to preserve the exact version used in the study and to support transparency, accessibility, and reproducibility. The dataset is synthetic and does not contain personal, confidential, human-subject, or geographically identifiable information.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/21805274first seen 2026-08-06 04:13:20
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。