The Estimate for the Green House Heating Demand Using Artifical Neural Networks

15746

In this study by taking into account the latitude, longitude, height, months and mean temperature data of the city and districts of Mersin, the heating need for unit base and surface zone is determined. In the model of artificial neural nets the heating need for the green house which is longitude, latitude, height and means temperature data is used as entry layer and the need for heater need is used as exit layer. Of the data belonging to Mersin Province and 7 districts; 6 district education data, Mersin Centrum and Tarsus district are used as test data in artificial neural net model. The data has been tested by using Levenberg-Marquardt algoritm and an estimate (R2 ) of value from an average of %99 has been found. The average of quadric error square root value is 0.0498 in average and is 0.0018 for education data. The mean absolute error for test data is 0.0478 and 0.0014 for education data. In conclusion, this study focused on the successful estimate of green house heater need by using the model of artificial neural nets.

235

Green house , heater need , artificial neural networks

Journal Of Polytechnic

241

Journal Of Polytechnic