Prediction Model of Photovoltaic Power in Solar Pumping Systems Based on Artificial Intelligence

Jorge Cervera-Gascó,Rafael González Perea,Miguel A Moreno,Jesús Montero

doi:10.3390/agronomy12030693

Jorge Cervera-Gascó, Rafael González Perea + Show 2 more

Open Access

https://doi.org/10.3390/agronomy12030693

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Abstract

Photovoltaic solar energy is becoming very important globally due the benefits of their use. Climate change is resulting in frequent climatic variations that have a direct effect on the energy production in photovoltaic installations, so their good management is essential. This can be a big problem, for example, in photovoltaic pumping systems where irrigated crops can be affected due to lack of water. In this work, a PREPOSOL (PREdiction of POwer in SOLar installations) model was developed in MATLAB® software, which allowed to predict the power generated in the photovoltaic installations up to 3 h in advance using Artificial Neural Networks (ANNs) in a Bayesian framework with Genetic Algorithms. Despite that the PREPOSOL model can be implemented for other activities with photovoltaic solar energy, in this case, it was applied to photovoltaic pumping systems. The results showed that the model estimated the generated power with a relative error (RE) and R2 of 8.10 and 0.9157, respectively. Moreover, a representative example concerning irrigation programming is presented, which allowed adequate management. The methodology was calibrated and validated in a high-power and complex photovoltaic pumping system in Albacete, Spain.

Highlights

In recent years, the consequences of climate change on the availability of resources such as fresh water seem to be evident [1]
In order to maximize the efficiency of this water use, in the last two decades, many irrigated areas have been subjected to modernization processes [3] where open channels have been replaced by pressurized networks [4]
The solar irradiance on the horizontal surface (W·m−2), measured by the pyranometer, shows the normal evolution according to the time of year, which is reflected on the power achieved

Summary

Introduction

The consequences of climate change on the availability of resources such as fresh water seem to be evident [1]. In order to maximize the efficiency of this water use, in the last two decades, many irrigated areas have been subjected to modernization processes [3] where open channels have been replaced by pressurized networks [4] These modernization processes has led to high energy consumption, making measures to optimize the use of this resource necessary [5], which together with the increase in the price of conventional electricity, can lead to the unfeasibility of some farms [6]; these are mainly in irrigation areas with underground resources, where the extraction cost alone can be up to 70% of the total energy cost [7]. Photovoltaic energy can be used in standalone or grid-connected systems to supply power for pumping stations in irrigated agriculture [17] This energy source has many environmental and economic benefits [18–20], their dynamic and highly weather-dependent nature makes its management a major challenge. Solar PV power forecasting is essential to the efficient management of these PV systems integrated in commercial irrigated farms

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