支持向量机在建筑能耗伪动态预测中的应用
Support Vector Machine in Prediction of Building Energy Demand Using Pseudo Dynamic Approach
摘要：
建筑物的能耗预测是近年来人们关注的一个主要问题，为了改善建筑物的能源管理，人们做了很多努力。特别是，在建筑物中的能源消耗的预测是必不可少的能源运营商建立一个最佳的运营策略，它可以集成到建筑物的能源管理系统（BEMS）。提出了一种基于支持向量机的建筑能耗预测模型。数据驱动的模型，例如支持向量机，对训练数据的选择非常敏感。为此，采用基于动态时间规整的相关日数据选取方法对SVM模型进行训练。此外，为了包含建筑物的热惯性，应用伪动态模型，因为它考虑了能量消耗效应和占用剖面的过渡信息。将相关天数数据的选取和整体训练数据模型应用于法国南特矿业学院办公楼的实例研究。结果表明，基于相关数据选择方法的支持向量机能较好地预测建筑能耗，且预测精度高于全数据训练。此外，与整个数据训练（周末约31小时，工作日约116小时）相比，相关数据选择方法在计算上更便宜（约8分钟训练时间），并且还揭示了在线系统的现实控制实现。
英文摘要：
Building's energy consumption prediction is a major concern in the recent years and many efforts have been achieved in order to improve the energy management of buildings. In particular, the prediction of energy consumption in building is essential for the energy operator to build an optimal operating strategy, which could be integrated to building's energy management system (BEMS). This paper proposes a prediction model for building energy consumption using support vector machine (SVM). Data-driven model, for instance, SVM is very sensitive to the selection of training data. Thus the relevant days data selection method based on Dynamic Time Warping is used to train SVM model. In addition, to encompass thermal inertia of building, pseudo dynamic model is applied since it takes into account information of transition of energy consumption effects and occupancy profile. Relevant days data selection and whole training data model is applied to the case studies of Ecole des Mines de Nantes, France Office building. The results showed that support vector machine based on relevant data selection method is able to predict the energy consumption of building with a high accuracy in compare to whole data training. In addition, relevant data selection method is computationally cheaper (around 8 minute training time) in contrast to whole data training (around 31 hour for weekend and 116 hour for working days) and reveals realistic control implementation for online system as well.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
