Identification of non-linear autoregressive models with exogenous inputs for room air temperature modelling

Autor: Christian Ankerstjerne Thilker, Peder Bacher, Davide Cali, Henrik Madsen
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Energy and AI, Vol 9, Iss , Pp 100165- (2022)
Druh dokumentu: article
ISSN: 2666-5468
DOI: 10.1016/j.egyai.2022.100165
Popis: This paper proposes non-linear autoregressive models with exogenous inputs to model the air temperature in each room of a Danish school building connected to the local district heating network. To obtain satisfactory models, the authors find it necessary to estimate the solar radiation effect as a function of the time of the day using a B-spline basis expansion. Furthermore, this paper proposes a method for estimating the valve position of the radiator thermostats in each room using modified Hermite polynomials to ensure monotonicity of the estimated curve. The non-linearities require a modification in the estimation procedure: Some parameters are estimated in an outer optimisation, while the usual regression parameters are estimated in an inner optimisation. The models are able to simulate the temperature 24 h ahead with a root-mean-square-error of the predictions between 0.25 °C and 0.6 °C. The models seem to capture the solar radiation gain in a way aligned with expectations. The estimated thermostatic valve functions also seem to capture the important variations of the individual room heat inputs.
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