Neural Network Model with Time Series for the Prediction of the Electric Field in the East Lima Zone, Peru

Multiple regression models are very relevant to predict values using predictor variables. The objective of this study was to predict the global solar radiation in the year 2019 in the area of East Lima, Peru. Three continuous quantitative predictor variables were analyzed: temperature, humidity, wind speed and the response variable was global solar radiation, resulting in a model with excellent significance p<0.001 that shows the prediction is effective. The multiple linear regression method was used, finding an average global radiation of 175 W/m2 and predictor variables with average temperature of 19.2 °C, humidity 23.9% and wind speed 1.77 m/s, with the highest temperature in summer recorded at 24.6°C, the highest humidity of 51.2% in autumn, the highest wind speed in summer at 2.63 m/s and the highest maximum global solar radiation in spring with 183 W/m2.

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Neural Network Model with Time Series for the Prediction of the Electric Field in the East Lima Zone, Peru

Cited by 2 publications

References 12 publications

Machine Learning with Meteorological Variables for the Prediction of the Electric Field in East Lima, Peru

Machine Learning with Meteorological Variables for the Prediction of the Electric Field in East Lima, Peru

Multiple Linear Regression Model of Environmental Variables, Predictors of Global Solar Radiation in the Area of East Lima, Peru

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