Estimation of Hourly Mean Ambient Temperatures with Artificial Neural Networks

In this study, the artificial neural networks have been used for the estimation of hourly ambient temperature in Denizli, Turkey. The model was trained and tested with four years (2002-2005) of hourly mean temperature values. The hourly temperature values for the years 2002-2004 were used in training phase, the values for the year 2005 were used to test the model. The architecture of the ANN model was the multi-layer feedforward architecture and has three layers. Inputs of the network were month, day, hour, and two hourly mean temperatures at the previous hours, and the output was the mean temperature at the hour specified in the input. In the model, Levenberg-Marquardt learning algorithm which is a variant of backpropagation was used. With the software developed in Matlab, an ANN was constructed, trained, and tested for a different number of neurons in its hidden layer. The best result was obtained for 27 neurons, where R2, RMSE and MAPE values were found to be 0.99999, 0.92024 and 0.20900% for training, and 0.9999, 0.91301 and 0.20907% for test. The results show that the artificial neural network is powerful an alternate method in temperature estimations.