Abstract
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.
Highlights
The information about the climatic parameters like mean hourly values of relative humidity, ambient temperature and wind velocity are useful in the thermal analysis of building, heating and cooling load calculations to decide the correct sizing of an airconditioning system for thermal comfort and in the performance evaluation and optimum design of many solar energy system [1]
The knowledge of variation in ambient temperature has a considerable value in predicting the solar radiation [3,4,5], hourly energy consumption and cooling load estimation in buildings [6,7] and room air temperature prediction [8]
The Artificial Neural Network (ANN) model was trained with the mean hourly temperature values of the years 2002-2004, and the trained model was tested for the mean hourly temperature values of the year 2005 by using software developed in Matlab
Summary
The information about the climatic parameters like mean hourly values of relative humidity, ambient temperature and wind velocity are useful in the thermal analysis of building, heating and cooling load calculations to decide the correct sizing of an airconditioning system for thermal comfort and in the performance evaluation and optimum design of many solar energy system [1]. An ANN model was developed in order to use to estimate the hourly ambient temperature in Denizli, Turkey. The output of the network was the hourly mean temperature at the date and time specified by the inputs. The hourly mean temperature values one and two hours earlier than the specified date and time was used in the input layer
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