A latent variable approach to heat load prediction in thermal grids
This is an incremental improvement for thermal grid management, offering more accurate and explainable predictions.
The paper tackles heat load prediction in district energy systems by combining a nominal physical model with a data-driven latent variable model for residuals, achieving better prediction accuracy than an artificial neural network in a validation case on a building in Lulea, Sweden.
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.