A latent variable approach to heat load prediction in thermal grids

Autor:	Simonsson, Johan, Atta, Khalid Tourkey, Zachariah, Dave, Birk, Wolfgang
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Paper submitted to 2020 European Control Conference, Saint Petersburg, Russia
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2002.05397 Zobrazit plný text záznamu View this record from Arxiv