To know in advance the value of solar radiation is an advantage in order to obtain solar energy. This paper proposes the design and implementation of solar radiation modelling for the estimation of the solar energy generation, based on different series of data collected from meteorological stations in Gran Canaria and Tenerife (Canary Islands, Spain), helping to generate green energy from sun by the estimation of solar radiation. Artificial Neural Network multilayer perceptron, were the classification method used to obtain the forecast. The study of solar radiation prediction achieves a mean average error of 0.04 kilowatts hour per square meter.
Temperature control and its prediction has turned into a research challenge for the knowledge of the planet and its effects on different human activities and this will assure, in conjunction with energy efficiency, a sustainable development reducing CO 2 emissions and fuel consumption. This work tries to offer a practical solution to temperature forecast and control, which has been traditionally carried out by specialized institutes. For the accomplishment of temperature estimation, a score fusion block based on Artificial Neural Networks was used. The dataset is composed by data from a meteorological station, using 20,000 temperature values and 10,000 samples of several meteorological parameters. Thus, the complexity of the traditional forecasting models is resolved. As a result, a practical system has been obtained, reaching a mean squared error of 0.136 • C for short period of time prediction and 5 • C for large period of time prediction.
Accurate meteorological forecasting has great importance in different fields. This works introduces a system to obtain precise predictions, which uses regression functions, and collected data using the meteorological stations from the Gran Canaria and South Tenerife airports. The dataset offers information about different phenomena as temperature, wind speed, solar radiation, pressure, moisture, cloudiness, rainfall and meteors. A preprocessing stage has been applied before prediction stage to adapt the collected data. A support vector machine, regression tree, and fit linear model are applied as regression functions. Results has been measured by the mean square error. These results reached an accuracy of 0.07 °C for temperature, 0.56 km/h for wind speed, 7.45 tenths of kJ/m2 for solar radiation and 0.11 mm for precipitation. It shows the robustness of the multiparameter meteorological forecast approach.
This paper proposes a emotion detector, applied for facial images, based on the analysis of facial segmentation. The parameterizations have been developed on spatial and transform domains, and the classification has been done by Support Vector Machines. A public database has been used in experiments, The Radboud Faces Database (RAFD), with eight possible emotions: anger, disgust, fear, happiness, sadness, surprise, neutral and contempt. Our best approach has been reached with decision fusion, using transform domains, reaching an accurate up to 96.62%.
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