Estimación de temperatura y humedad relativa en Venezuela mediante redes neuronales

Abstract

The aim of this paper is to present estimations about climatic conditions in Venezuela by using a neural network model. These conditions include values of dry-bulb temperature, relative humidity based on a yearly average. These values are calculated using the altitude and the geographical localization. The estimates were carried out by using an annual average, from 1989 to1998, in various locations taken from weather stations that belong to the Fuerza Aerea Venezolana. This information is commonly used for design, sizing, distribution, installation, and marketing air-conditioning equipment; as well as for other energy-related processes in residential, agricultural, commercial and industrial applications.