Active learning concerning sampling cost for enhancing AI-enabled building energy system modeling

IF 13 Q1 ENERGY & FUELS Advances in Applied Energy Pub Date : 2024-09-02 DOI:10.1016/j.adapen.2024.100189
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Abstract

Machine learning is widely recognized as a promising data-driven modeling technique for the model-based control and optimization of building energy systems. However, the generalizability of data-driven models often faces significant challenges, as the available training data from building operations usually only covers a limited range of working conditions. Active learning can proactively test unseen and informative working conditions to enrich the training set by adding new data samples, leading to improved generalization performance of data-driven models. A novel distance and information density-based sample strategy is developed that accounts for the real-time status of building operation and outdoor environment. Based on Mahalanobis distance, this strategy determines the sampling value of an unlabeled sample (unseen working condition) by assessing its similarity to both the training samples and other unlabeled samples. As collecting sufficiently representative samples can be difficult, costly, and time-consuming, a distance-based sampling cost metric is proposed to compare the efficiency of different sampling methods, considering the detrimental effects of the actively sampling process on the normal operation of building energy systems. This paper presents a comprehensive and in-depth comparison of five active learning methods, including one incorporating the distance-based sampling strategy, by conducting data experiments on the data collected from the cooling towers of a real high-rise building. The results show that active learning can effectively identify informative data samples and improve the generalization performance of data-driven models. The research outcomes are valuable for enhancing AI-enabled data-driven modeling of building energy systems with substantial decreases in costs on data sampling.

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关于采样成本的主动学习,以提高人工智能建筑能源系统建模能力
机器学习被广泛认为是一种有前途的数据驱动建模技术,可用于基于模型的建筑能源系统控制和优化。然而,数据驱动模型的普适性往往面临重大挑战,因为来自建筑运行的可用训练数据通常只涵盖有限的工作条件范围。主动学习可以主动测试未见过的、信息量大的工作条件,通过添加新的数据样本来丰富训练集,从而提高数据驱动模型的泛化性能。本研究开发了一种基于距离和信息密度的新型样本策略,该策略考虑了建筑物运行和室外环境的实时状态。基于马哈拉诺比斯距离,该策略通过评估未标注样本(未见工作状态)与训练样本和其他未标注样本的相似度来确定其采样值。考虑到主动采样过程对建筑能源系统正常运行的不利影响,本文提出了一种基于距离的采样成本指标,用于比较不同采样方法的效率。本文通过对实际高层建筑冷却塔采集的数据进行数据实验,对五种主动学习方法进行了全面深入的比较,其中包括一种结合了基于距离的采样策略的方法。结果表明,主动学习能有效识别信息数据样本,提高数据驱动模型的泛化性能。这些研究成果对于提高人工智能数据驱动的建筑能源系统建模具有重要价值,同时还能大幅降低数据采样成本。
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来源期刊
Advances in Applied Energy
Advances in Applied Energy Energy-General Energy
CiteScore
23.90
自引率
0.00%
发文量
36
审稿时长
21 days
期刊最新文献
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