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

被引:14
作者
Thilker, Christian Ankerstjerne [1 ]
Bacher, Peder [1 ]
Cali, Davide [1 ]
Madsen, Henrik [1 ]
机构
[1] Tech Univ Denmark, Dept Appl Math & Comp Sci, Asmussens Alle,Bldg 303B, DK-2800 Lyngby, Denmark
关键词
Time series analysis; Non-linear models; District heating; Smart energy systems; ARTIFICIAL NEURAL-NETWORK; INDOOR TEMPERATURE; SOLAR-RADIATION; HEAT DYNAMICS; B-SPLINES; ARX MODEL; PREDICTION; BUILDINGS; LOAD;
D O I
10.1016/j.egyai.2022.100165
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 degrees C and 0.6 degrees 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.
引用
收藏
页数:10
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