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hyperGrid: A Provenance-First Visualisation Platform for Synthetic Irish Electricity Demand (SYNTH_GRID_IE)

hyperGrid: アイルランドの合成電力需要データ（SYNTH_GRID_IE）のためのプロブナンス第一の可視化プラットフォーム (AI 翻訳)

O Regan, Brian

Zenodoプレプリント2026-06-01#その他Origin: EU対象セクター: power
DOI: 10.5281/zenodo.20617465
原典: https://zenodo.org/records/20617465

🤖 gxceed AI 要約

日本語

本論文は、アイルランドの建物およびデータセンターの電力需要を模擬した大規模合成データセット(SYNTH_GRID_IE)を可視化するウェブプラットフォームhyperGridを紹介する。プラットフォームはデータの来歴(provenance)を重視し、各データポイントに合成/実測などの分類ラベルを付与することで誤解を防ぐ。各種ダッシュボードとJSON APIを提供し、再生可能エネルギー統合や送電網分析に活用可能。

English

This paper presents hyperGrid, a web platform for exploring SYNTH_GRID_IE, a large synthetic dataset of hourly Irish electricity demand for 2022-2023. The platform emphasizes data provenance by labeling each figure as REAL, CALIBRATED, SYNTHETIC, or COMPUTED to prevent misinterpretation. It offers multiple views including system overview, sector breakdown, time-series explorer, and choropleth map, along with a JSON read API. The dataset covers approximately 88% of national demand from modeled buildings and data centers, and includes synthetic wind and solar farm registers.

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

日本のエネルギー分野では、合成データを用いた系統解析や再生可能エネルギー導入計画の検討が進んでいる。本プラットフォームのデータ来歴表示のアプローチは、日本の電力データの透明性向上や、オープンデータの信頼性確保に参考となる。

In the global GX context

For global energy research, this platform demonstrates a transparent approach to handling synthetic data, crucial for reproducibility and trust in energy system models. The methodology of provenance labeling and pre-aggregated rollups can be adopted by other grid operators or research groups developing similar databases for renewable integration studies.

👥 読者別の含意

🔬研究者:Energy system modelers can use the dataset and platform for validation of models or as a benchmark for synthetic data generation.

🏢実務担当者:Grid operators and renewable energy developers may find the synthetic demand data useful for planning and stress testing without relying on real operational data.

📄 Abstract（原文）

hyperGrid is a provenance-first web platform for exploring SYNTH_GRID_IE, a weather-driven, hourly synthetic dataset of Irish building and data-centre electricity demand covering 2022–2023. The dataset (build "run8") comprises 437,529,982 hourly rows across 25,006 modelled buildings, a synthetic national reference series, synthetic wind and solar farm registers, a feeder-hour sample, and a synthetic nowcast ensemble spanning the 26 Republic of Ireland counties. hyperGrid provides the read-only serving layer for this dataset: a dark-themed dashboard and a JSON read API over small, pre-aggregated rollup tables, so the 437-million-row time series is never queried per request. Its distinguishing feature is that the dataset's honesty and labelling rules are first-class user-interface elements — every figure carries its provenance class (REAL, CALIBRATED, SYNTHETIC, or COMPUTED), a scope qualifier (totals cover modelled building and data-centre sectors, approximately 88% of national demand), and the build context (global_seed 20240101), so synthetic or calibrated values can never be mistaken for measured EirGrid data. Views include a system overview, sector breakdown, a national time-series explorer, a county choropleth map, a buildings drill-down, feeder-stress sample, a nowcast fan, and a consolidated data-provenance page. The platform is built in PHP (server-rendered pages plus a JSON read API), with vanilla JavaScript, Chart.js, and Leaflet on the client, packaged with Docker. Display-shaped rollups are produced offline by a DuckDB bridge over a Backblaze B2 Parquet lake and served from MariaDB. Developed by the Energy Informatics Group (EIG), International Energy Research Centre, Tyndall National Institute, University College Cork.

🔗 Provenance — このレコードを発見したソース

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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。