低圧配電網におけるメーター背後低炭素技術のプライバシー保護型検出・位置特定
Privacy-Preserving Detection and Localisation of Behind-the-Meter Low-Carbon Technologies in Low-Voltage Networks (原題)
Saad Khan, Ahmed A. Aboushady, Firdous Nazir, Mohamed Farrag
🤖 gxceed AI 要約
日本語
本研究は、低圧配電網における未登録の低炭素技術（PV、EV、ヒートポンプ）をプライバシー保護しつつ検出・分類・位置特定する枠組みを提案。GDPR準拠の計測データを用い、LSTMオートエンコーダ、統計的ルールベース分類器、デジタルツインを組み合わせる。IEEE 33バス系統での検証で、顧客レベルの位置特定精度98%を達成。
English
This study proposes a privacy-preserving framework to detect, classify, and localize unregistered behind-the-meter low-carbon technologies (PV, EVs, heat pumps) in low-voltage networks. Using GDPR-compliant measurements, it combines LSTM autoencoders, statistical rule-based classifiers, and a digital twin. Validation on the IEEE 33-bus system achieves 98% customer-level localization accuracy.
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、再エネ導入拡大に伴う配電網の観測性向上が課題。本手法は、個人情報保護法に配慮しつつ系統運用者が低炭素技術の導入状況を把握する手段を提供し、カーボンニュートラル実現に向けた系統運用の高度化に貢献する。
In the global GX context
Globally, this addresses the challenge of integrating distributed energy resources while respecting privacy regulations like GDPR. It offers a scalable approach for distribution system operators to enhance observability, supporting the energy transition and aligning with climate disclosure requirements for grid resilience.
👥 読者別の含意
🔬研究者:Provides a novel privacy-preserving ML framework for detecting and localizing BTM LCTs, advancing methods in grid observability and AI for energy.
🏢実務担当者:Offers a GDPR-compliant tool for utilities to improve network visibility without accessing individual customer data, aiding in grid planning and DER integration.
🏛政策担当者:Demonstrates how privacy regulations can be reconciled with grid modernization needs, informing policy on data access and DER integration.
📄 Abstract（原文）
The increasing adoption of behind-the-meter low carbon technologies (BTM LCTs), including photovoltaic (PV) systems, electric vehicles (EVs), and heat pumps (HPs) is transforming low-voltage distribution networks (LVDNs), necessitating real-time update and improved observability of the network. However, many BTM LCT installations remain unregistered, while privacy regulations limit access to individual customer demand data, reducing network observability. This paper proposes a privacy-preserving framework for detecting, classifying and localising newly installed BTM LCTs using UK General Data Protection Regulation (GDPR) compliant measurements, namely branch-level aggregated current and individual customer voltage measurements. The proposed framework comprises three stages: (i) BTM LCT event detection using a residual input-based multivariate Long Short-Term Memory Autoencoder (LSTM-AE); (ii) BTM LCT type classification using a statistical rule-based (SRB) classifier; and (iii) customer-level BTM LCT localisation and rating classification using a distribution network digital twin (DNDT). The framework is validated using 15-minute resolution smart meter data from the Fluvius’ dataset and simulations on the IEEE 33-bus distribution system. The residual input-based LSTM-AE achieves an F1-score of 0.873, outperforming the raw-input model with an F1-score of 0.741. The SRB classifier successfully identifies the tested BTM LCT types, while the DNDT-trained hybrid one-dimensional convolution neural network (1D-CNN) achieves 98% customer-level localisation accuracy under the evaluated network conditions. Furthermore, the proposed detection approach achieves a 100% detection success rate for the higher-rated BTM LCT cases (10 kW PV and 7.2 kW EV charger) across aggregation levels of 5-25 customers.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.22541/authorea.15008272/v1first seen 2026-09-06 05:09:47
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