Prediction of outdoor air temperature using neural networks: Application in 4 European cities

• Development of neural network for outdoor air temperature prediction.
• The neural network is trained using acquired measurements.
• The performance of the neural network is evaluated by statistical tools.

The aim of this paper is to present the development and evaluation of neural network based identification algorithms for the prediction of outdoor air temperature using acquired data from four European cities (Ancona – Italy, Chania – Greece, Granada – Spain and Mollet – Spain). Different neural network topologies (feed forward, cascade and elman) have been tested to identify the most suitable for each city. The efficiency of the prediction is validated by comparing predicted and measured outdoor air temperature. Furthermore, statistical tools such as R2, and root mean square error (rmse) are used to evaluate the annual performance of the neural network. The comparison of measured and predicted outdoor air temperature (R2 > 0.9, rmse <2 °C) confirms the accurate training of the neural network for all four European cities. All work has been contacted using Matlab's environment.