基于潜变量的热力管网热负荷预测方法
A latent variable approach to heat load prediction in thermal grids
摘要：
本文提出了一种区域能源系统热负荷预测的新方法。该方法使用标称模型预测的室外温度依赖的空间热负荷，和数据驱动的潜变量模型预测的时间依赖的余热负荷。余热负荷主要来自于空间供暖和通风的时间依赖性操作以及生活热水生产。由此产生的模型递归更新的超参数自由实现的基础上，在一个简约的模型，允许高的计算性能的结果。该方法被应用到一个单一的多住宅建筑在瑞典吕勒亚，预测热负荷使用相对较少的模型参数和容易获得的测量。使用人工神经网络的预测结果进行比较，表明该方法实现了更好的预测精度的验证情况。此外，所提出的方法通过使用可解释的物理模型表现出可解释的行为。
英文摘要：
In this paper a new method for heat load prediction in district energy systems is proposed. The method uses a nominal model for the prediction of the outdoor temperature dependent space heating load, and a data driven latent variable model to predict the time dependent residual heat load. The residual heat load arises mainly from time dependent operation of space heating and ventilation, and domestic hot water production. The resulting model is recursively updated on the basis of a hyper-parameter free implementation that results in a parsimonious model allowing for high computational performance. The approach is applied to a single multi-dwelling building in Lulea, Sweden, predicting the heat load using a relatively small number of model parameters and easily obtained measurements. The results are compared with predictions using an artificial neural network, showing that the proposed method achieves better prediction accuracy for the validation case. Additionally, the proposed methods exhibits explainable behavior through the use of an interpretable physical model.
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。
