XGBoost "is all you need": the case of forecasting transmitted heat energy in District Heating Systems
XGBoost「だけで十分」：地域熱供給システムにおける伝熱エネルギー予測の事例 (AI 翻訳)
Milan Zdravković
🤖 gxceed AI 要約
日本語
地域熱供給システム（DHS）における伝熱エネルギー予測について、XGBoostとLSTMを比較した研究。実データを用いた実験で、XGBoostがLSTMを上回る性能を示し、計算コストと環境負荷の低減に寄与することを明らかにした。データ不足区間でのLSTMの誤差が大きいことが差の要因とされる。
English
This study compares XGBoost and LSTM for forecasting transmitted heat energy in District Heating Systems (DHS). Using real-world data, XGBoost consistently outperforms LSTM, with lower computational cost and carbon footprint. The performance gap is attributed to LSTM's larger errors in data-sparse intervals.
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
日本の地域熱供給システムは省エネルギーと脱炭素の観点から注目されており、効率的な需要予測は運用最適化に寄与する。本研究成果は、日本の熱供給事業者にとってコスト削減とCO2排出削減の両立に資する可能性がある。
In the global GX context
District heating is a key component of urban energy efficiency and decarbonization globally. This paper provides evidence that simpler ML models can achieve high accuracy with lower computational and environmental costs, relevant for energy system operators and sustainability reporting.
👥 読者別の含意
🔬研究者:Highlights the value of comparing ML methods for energy forecasting, with implications for computational efficiency and carbon footprint.
🏢実務担当者:Offers a practical approach for DHS operators to reduce costs and emissions through efficient forecasting.
📄 Abstract（原文）
This paper presents a comparative study of two distinct approaches, XGBoost and Long-Short Term Memory (LSTM), for forecasting transmitted heat energy in District Heating Systems (DHS). The objective is to explore scenarios in which conventional ML algorithms demonstrate better performance over deep learning networks in time series forecasting and the associated benefits in terms of computational cost and environmental impact. The study focuses on a real-world DHS dataset. Through experimentation and analysis, it is demonstrated that XGBoost consistently outperforms LSTM in this specific forecasting task. The difference is explained by the error distribution illustrating that LSTM makes more significant errors in the intervals of less data availability. The reduced computational demands of conventional ML approaches not only result in cost savings but also minimize the carbon footprint associated with data analysis tasks in energy systems.
🔗 Provenance — このレコードを発見したソース
- arXiv https://arxiv.org/abs/2608.11446first seen 2026-08-13 04:12:56
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