Predicting weather using ANN with free open weather data in python Erik Boye

Predicting the weather is important for a lot of fields including agriculture, construction and hydro-power and flood management. Currently mechanistic meteorology predictions are generated using heavy computing based 3D Navier-Stokes models. Therefore, it is of interest to develop models that can predict weather conditions faster than traditional meteorological models. The field of machine learning has received much interest from the scientific community. Due to its applicability in a variety of fields, it is of interest to study if the use of artificial neural networks can be a good candidate for prediction of weather conditions. Machine learning methods benefit from large datasets. A python interface was developed to make it easier to obtain weather data from free sources, the python interface works well, but is more user-friendly when used with Met supplier compared with Netatmo supplier. Four separate models where trained to predict the temperature 1, 3, 6 and 12 hours ahead. In the first experiment, only temperature was used as input to the networks. This constitutes an auto-regressive neural network(ARNN). In the second experiment, precipitation data was introduced into the network, forming an autoregressive neural network with exogenous inputs (ARX-NN). The results show that the inclusion of precipitation had a negligible effect on accuracy for temperature prediction. Out of the four model types, 1-hour prediction has the best prediction results for both the ARNN and the ARX-NN