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

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.

Highlights

The global concern is currently focused on strategies to mitigate climate change and its effects, which are framed by the way countries generate, transport and consume their energy resources, since the energy sector produces a large amount of greenhouse gas emissions [1]
Worldwide awareness is currently focused on strategies to mitigate climate change and its effects, which are framed by the way in which countries generate, transport and consume their energy resources, since the energy sector produces a large amount of greenhouse gas emissions [2], as energy demand is increasing by approximately 5% per year in developing countries, such as Colombia [3]
The prediction of solar radiation is validated by means of an Artificial Neural Network (ANN) in order to validate the behavior of the data and to determine the Mean Square Error (RMSE), using variables such as the temperature, the relative humidity, the pressure and the wind speed as input data

Summary

Introduction

The global concern is currently focused on strategies to mitigate climate change and its effects, which are framed by the way countries generate, transport and consume their energy resources, since the energy sector produces a large amount of greenhouse gas emissions [1]. The Colombian Caribbean region is currently considered one of the largest sources of wind and solar potential at nationwide level, due to the progress that this region has made in recent decades in terms of the implementation of renewable energy sources [11] considering the growing importance that solar and wind energy have been acquiring in the national and global energy matrix [8, 12] This has awakened the interest of different sectors focused on developing projects that allow the country to take full advantage of its energy resources, making the Colombian Caribbean an attractive sector for local and foreign investors [13]

Methods

Results

Conclusion