International audienceShort Term Load Forecasting (STLF) is essential for planning the day-to-day operation of an electric power system. As this forecasting leads to increased security operation's conditions and economic cost savings, numerous techniques have been used to improve the STLF. We propose in this paper the comparison of two nonlinear regression techniques namely Gaussian Process (GP) regression models and Neural Network (NN) models. While the Bayesian approach to NN modelling offers significant advantages over the classical NN learning methods, it will be shown that the use of GP regression models will improve the performances of the forecasting. The proposed techniques are applied to real load data
The aim of this work is to simulate the pollutants transport in buildings. Focusing mainly on the presence of CO2, firstly we resolve the airflow equations for two typical validation cases, the Rao case and the IEA case. These numerical results are compared to the most known software and they are used to evaluate of the evolution of CO2 concentration in the different rooms. In order to obtain the different parameters and filters of the proposed model we use a statistical method based on Bayesian inference. The final comparison of results is coherent but a complementary experimental procedure is necessary to calibrate and refine the model.
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