基于Transformer-xLSTM架构的公共建筑多联机系统冷负荷预测模型
Cooling load prediction model for multi-split systems in public buildings based on Transformer-xLSTM architecture
摘要：
随着多联机系统在办公建筑等领域的不断应用,其在新能源不断融入能源结构的背景下如何有效消纳可再生能源成为一个重要议题。本研究以日本大阪的一个案例建筑的实测数据为基础,开发了一个逐时负荷预测模型,该模型结合了Transformer和xLSTM的优势,能够有效预测建筑冷负荷。研究结果表明,模型的均方根误差变异系数(RMSE-CV)仅为24.66%,显示出良好的预测性能。这一成果不仅为该建筑提供了灵活调控的基础,也为多联机系统在可再生能源消纳和需求响应方面的应用提供了有力支持。
Abstract：
With the continuous application of multi-split systems in office buildings and other sectors, how to effectively utilize renewable energy under the background of its increasing integration into the energy structure has become a significant issue. This study, based on empirical data from a case building in Osaka, Japan, develops an hourly load prediction model that leverages the strengths of Transformer and xLSTM to effectively predict building cooling loads. The results show that the model achieves a coefficient of variation from root mean square error (RMSE-CV) of only 24.66%, demonstrating excellent predictive performance. This achievement not only provides a foundation for flexible regulation of the building, but also offers strong support for the application of multi-split systems in renewable energy integration and demand response.
Keywords:public building; multi-split system; empirical study; load prediction; deep learning; renewable energy