Energy Consumption and Carbon Emission Prediction of District Heating System in Residential Communities Based on SSA-LSTM Model
住宅コミュニティにおける地域暖房システムのエネルギー消費と炭素排出予測 - SSA-LSTMモデルに基づく (AI 翻訳)
Bingwen Zhao, Luchan Xu, 郑振海, Yanqi Wu, Tiancheng Yuan
🤖 gxceed AI 要約
日本語
本研究は、SSA-LSTMハイブリッドモデルを用いて地域暖房システムの熱負荷と電力負荷を高精度に予測し、炭素会計と3つの政策シナリオの下で炭素ピーク時期と排出削減ポテンシャルを評価した。その結果、中程度および理想的な改修シナリオでのみ2023-2024暖房期に炭素ピークを達成し、2031-2032年までに累積削減量138.19トンおよび254.2トンを見込む。家庭用熱量計が全体削減の28%を占める。
English
This study employs an SSA-LSTM hybrid model to accurately predict heat and power loads of a district heating system, combined with carbon accounting and three policy scenarios to evaluate carbon peak timing and emission reduction potential. Results show that only moderate and ideal renovation scenarios achieve carbon peaks in the 2023-2024 heating period, with cumulative reductions of 138.19 t and 254.2 t by 2031-2032; household heat meters account for 28% of total reductions. The framework provides quantitative support for community heating operation, renovation evaluation, and carbon quota management.
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
日本でも地域熱供給システムは広く導入されており、本モデルはSSBJや有報におけるGHG排出量算定の精緻化や、省エネ改修の効果検証に応用可能。政策シナリオ分析の枠組みは日本の自治体のカーボンニュートラル計画策定に参考となる。
In the global GX context
The SSA-LSTM framework offers a replicable methodology for district heating carbon prediction relevant to global TCFD/ISSB-aligned disclosure and transition finance. The policy scenario analysis supports carbon peak planning under varying renovation levels, which can inform national decarbonization pathways and energy efficiency investments.
👥 読者別の含意
🔬研究者:Validates SSA-LSTM superior performance over LSTM and BP for heating/cooling load prediction, offering a benchmark for ML-based carbon accounting.
🏢実務担当者:Provides a quantitative tool for district heating operators to optimize renovation plans and carbon quota management based on scenario outcomes.
🏛政策担当者:Demonstrates how policy scenario analysis (baseline, moderate, ideal) can determine carbon peak timing and guide subsidy targeting for district heating retrofits.
📄 Abstract（原文）
Against global dual-carbon targets, urban residential central heating dominates building energy use and carbon emissions. Conventional LSTM forecasting requires manual hyperparameter adjustment and easily falls into local optima; micro-community carbon prediction also lacks accurate energy models and policy-based multi-scenario analysis for targeted low-carbon renovation. This study adopts the 2018–2023 hourly heating data of a community in H Province. It builds a preprocessing workflow with boxplot-Isolation Forest anomaly detection and MissForest filling, then constructs an SSA-LSTM hybrid model optimized by Sparrow Search Algorithm to predict heat and power loads precisely. Combined with carbon accounting and three policy scenarios, it evaluates carbon peak timing and emission reduction potential of heating renovations. Results show that SSA-LSTM attains 2.48% MAPE for heat and 3.20% for power, surpassing LSTM and BP. Only moderate and ideal renovation scenarios realize carbon peaks in the 2023–2024 heating period, with cumulative cuts of 138.19 t and 254.2 t by 2031–2032; household heat meters deliver 28% of total reductions. The framework offers quantitative support for community heating operation, renovation evaluation and carbon quota management.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/s26154782first seen 2026-07-30 05:45:05
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