Wind and solar resource assessment and prediction using Artificial Neural Network and semi-empirical model: case study of the Colombian Caribbean region

Oscar Churio Silvera; Marley Vanegas Chamorro; Guillermo Valencia Ochoa

doi:10.1016/j.heliyon.2021.e07959

Wind and solar resource assessment and prediction using Artificial Neural Network and semi-empirical model: case study of the Colombian Caribbean region

Heliyon. 2021 Sep 8;7(9):e07959. doi: 10.1016/j.heliyon.2021.e07959. eCollection 2021 Sep.

Authors

Oscar Churio Silvera¹, Marley Vanegas Chamorro¹, Guillermo Valencia Ochoa¹

Affiliation

¹ Universidad del Atlántico, Facultad de Ingeniería, Carrera 30 Número 8 - 49, Puerto Colombia, Área Metropolitana de Barranquilla, Colombia.

Abstract

This work is focused on the importance of developing and promoting the use of wind and solar energy resources in the Colombian Caribbean coast. This region has a considerable interest for the development of solar technology due to the available climatic characteristics. Therefore, a detailed solarimetric analysis has been carried out in the department of San Andrés, Providencia and Santa Catalina, located in the Colombian Caribbean region, using a semi-empirical radiation model, based on the Bird & Hulstrom model, and the parameterizations of the Mächler & Iqbal model, which allowed obtaining an average total irradiation value of 6.5 kWh/m²day. In addition, a statistical analysis of the wind resource was carried out based on meteorological data, which yielded an average multiannual wind speed of 3.4 m/s, and a maximum wind speed of 15.2 m/s during the month of October. The meteorological input data used for this analysis were provided by the Colombian Institute of Hydrology, Meteorology and Environmental Studies (IDEAM), in order to perform initial calculations and obtain a climatic profile of the areas with clear, medium and cloudy atmospheres throughout the year. Regarding the comparative study, the analysis was complemented with a prediction of solar radiation using Artificial Neural Networks (ANN), where irradiance could be predicted with a fairly good agreement, which was validated with a Root Mean Square Error (RMSE) of 0.87 using the temperature, the relative humidity, the pressure and the wind speed as the input data.

Keywords: Artificial Neural Network; Back propagation; Bird & Hulstrom model; Renewable energy sources; Solar radiation; Wind power; Wind speed.