  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.
