XGBoost比LSTM更准且省电，适合热力系统预测
XGBoost "is all you need": the case of forecasting transmitted heat energy in District Heating Systems
- 用XGBoost替代LSTM做供热系统能量预测
- XGBoost误差更小，尤其在数据少时表现更好
- 计算量更低，环保又省钱，适合实际部署
本文对比了XGBoost与长短期记忆网络（LSTM）在区域供热系统（DHS）中传输热能预测的表现。基于真实世界DHS数据集的实验表明，XGBoost在此任务中持续优于LSTM。分析显示，LSTM在数据稀缺时段会产生更大误差。传统机器学习方法显著降低计算需求，不仅节省成本，也减少了能源系统数据分析相关的碳足迹。
原文摘要 · Abstract (English)
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.
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