Using vanilla recurrent neural network model and k-nearest neighbor algorithm for predict production and consumption electric

Production and consumption electric prediction under the influence of temperature, interesting and challenging research topic Developed countries' economies are measured according to their power economy. Currently, the temperature is considered to be an illustrious scientist field because in many result it gives different. The temperature with its huge and dynamic information sources is considered as a suitable environment for data mining and business researchers. In this paper, we applied k-nearest neighbor algorithm and Vanilla RNN Model Suggested Method approach in order to predict Production and consumption electric prediction to assist investors, management, decision makers, and users in making correct and informed investments decisions. According to the results, the Vanilla RNN Model Suggested Method is robust with small error ratio; consequently the results were rational and also reasonable. In addition, depending on the Production and consumption electric data; the prediction results were close and almost parallel to actual Production and consumption electric prediction data.