# An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions
## 2 Literature Review
Before the test, the data were normalized, and the measure of the accuracy of the models was the Mean Squared Error MSE. In this study, SVM performed better than MLP. A comparison of the results of air temperature forecasting using a MLP neural network model for prediction using the SVM model is the subject of many studies [39]. This shows that models with such structures are useful in forecasting weather variables. In the largest of the studies available in the literature, the most frequently predicted conditions were the mean, minimum, and maximum temperature. The research concerned a specific place in the world. [PERSON] and [PERSON], in their work from 2010, used an Artificial Neural Network with a backpropagation algorithm to predict the mean temperature [40], as did [PERSON] and others in a study conducted in 2015 [41]. Among the studies using Artificial Neural Networks for the prediction of the mean temperature, we paid attention to the work of an author from Poland, [PERSON] from 2005 [42]. Another study on temperature prediction using an Artificial Neural Network model is presented in the article [43]. By far, the largest number of studies described mean temperature predictions using ANNs, and one of the most frequently used network learning algorithms was the backpropagation algorithm [44; 45; 46; 47]. In the literature, there were additionally studies regarding forecasting the maximum temperature using ANNs [48; 49] as well as the minimum temperature [50]. This problem is discussed in an article regarding the prediction of the maximum and minimum temperature with linear regression [51]. A study comparing the effectiveness of the Support Vector Machine Regressor (SVR) and \(k\)-Nearest Neighbors in predicting the wind power used in renewable electricity production is presented in the article [52]. Wind speed is also the subject of research presented in the Polish study [53]. This study predicted wind speed as one determinant of the energy consumption in buildings. The tests were carried out on neural network models based on Multilayer Perceptron architecture (MLP), Generalized Regression Networks (GRNN), and networks with Radial Base Functions (RBFs). Another study with the aim of determining the annual mean wind speed by using a Multilayer Perceptron and backpropagation algorithm for training the network in foreign literature [54], showed the popular use of this network model for wind speed forecasting. Many types of neural network models can be used in forecasting weather conditions. For this purpose, networks with relatively low complexity, such as MLP, and networks with a more complex architecture, i.e., deep networks [55,56], Recurrence Neural Networks (RNN), Conditional Restricted Boltzmann Machine models (CRBM), and Convolutional Neural Networks (CNN) are suitable [57]. The application of a gated recurrent unit neural network (GRUNN) modification of RNNs to forecast wind power, which is one of the largest renewable energy sources, is described in a paper published in 2019 by [PERSON] and others [58]. LSTM network models for predicting the precipitation based on meteorological data from 2008 to 2018 in Jingdezhen City were deployed in the research described by [PERSON] and others in 2020. LSTM is a special kind of RNN, capable of learning long-term dependence. It was introduced by [PERSON] and [PERSON] [59]. RNNs are special ANNs that are connected in a feedback structure between units of an individual layer. They are called recurrent because they perform the same operation on all elements in the sequence. They make it possible to model data with time-series characteristics by complementing the limits of non-recurrent ANNs, which independently assume the relationship between inputs [60]. In this article, a study involving temperature prediction published by [PERSON] and others in 2020 is presented. The difference between LSTM and RNN is that it adds a \"processor\", which is called the cell state, to the algorithm to judge whether the information is useful or not.