L. Hontoria

Generation of hourly irradiation synthetic series using the neural network multilayer perceptron

In this work, a methodology based on the neural network model called multilayer perceptron (MLP) to solve a typical problem in solar energy is presented. This methodology consists of the generation of synthetic series of hourly solar irradiation. The model presented is based on the capacity of the MLP for finding relations between variables for which interrelation is unknown explicitly. The information available can be included progressively at the series generator at different stages. A comparative study with other solar irradiation synthetic generation methods has been done in order to demonstrate the validity of the one proposed.

Recurrent Neural Supervised Models for Generating Solar Radiation Synthetic Series

In this paper, a neural network method for generating solar radiation synthetic series is proposed and evaluated. In solar energy application fields such as photovoltaic systems and solar heating systems, the need of long sequences of solar irradiation data is fundamental. Nevertheless those series are not frequently available: in many locations the records are incomplete or difficult to manage, whereas in other places there are no records at all. Hence, many authors have proposed different methods to generate synthetic series of irradiation trying to preserve some statistical properties of the recorded ones. The neural procedure shown here represents a simple alternative way to address this problem. A comparative study of the neural-based synthetic series and series generated by other methods has been carried out with the objective of demonstrating the universality and generalisation capabilities of this new approach. The results show the good performance of this irradiation series generation method.

Two New Applications of Artificial Neural Networks: Estimation of Instantaneous Performance Ratio and of the Energy Produced by PV Generators

The growth of photovoltaic (PV) for electricity generation is one of the highest in the field of the renewable energies and this tendency is expected to continue in the next years. As an obvious consequence, an increasing number of new PV components and devices, mainly arrays and inverters, are coming into the PV market. The need for PV arrays and inverters to be characterized has then become a more and more important aspect. Due to the variable nature of the operating conditions in PV systems, the complete characterization of these elements is quite a difficult issue.

A neural network approach for generating solar irradiation artificial series

In this paper a relevant problem in the photovoltaic solar energy field is considered: the generation of artificial series of hourly solar irradiation. The proposed methodology artificially generates series following the average tendency of the hourly radiation series k t in a given place. This is obtained by making use of a set of historical values of this series in such place (for training purposes) as well as the daily clarity index K T of the year to be generated. This information is employed for the supervised training of a proposed neural network model. The neural model employs a well known paradigm, called Multilayer Perceptron (MLP), in a feedback architecture. The generation method is based on the MLP ability to extract, from a sufficiently general training set, the existing relationships between variables whose interdependence is unknown a priori. This way, the presented design methodology can implicitly include all the available information. Simulation results show the good performance of the irradiation series generator, and the general applicability of this methodology in the estimation of highly complex temporal series.

Calculation of direct normal irradiation from global horizontal irradiation

Concentrator Photovoltaic (CPV) systems only work with the Direct Normal Irradiance (DNI), so a knowledge of DNI data is required for the design and evaluation of these kinds of systems. DNI is not always measured at ground meteorological stations due to equipment costs. In recent years, several spatial databases that estimate DNI from satellite data have been developed. These databases are a very useful tool for CPV applications. However, the databases present uncertainty and provide different values of DNI. This lack of DNI data and the uncertainty of available data contrast with the availability of reliable global horizontal irradiation data, which is easy to find or measure. In this paper, a simple procedure for estimating DNI from global horizontal irradiation is presented. It does not try to improve the existing methods, but meets the basic requirements for the analysis of CPV systems. The method can be easily implemented in a spreadsheet or in computer programs in renewable energy and its accuracy ...

Artificial neural networks for the generation of direct normal solar annual irradiance synthetic series

The use of concentrators implies that CPV systems only work with the Direct Normal Irradiance (DNI). So it is necessary to know DNI data in order to estimate the energy that will be produced by the system, perform economic analysis, supervise plant operation, etc. However, DNI Typical Meteorological Year datasets are expensive and rarely available due to the cost and sophistication of measurement devices and data processing requirements. Particularly, there is a lack of data on the Sunbelt countries, which are more favorable for the use of CPV. In this work, an artificial neural network is used for the generation of DNI hourly time series for some Spanish locations. The model was trained and tested with different locations and different years data. Although several authors have proposed different methods for the generation of solar radiation synthetic series, these methods are for global radiation and flat panel, nevertheless, we calculate them for direct normal solar radiation and used for CPV systems. ...

Virtual laboratory for the training and learning of the subject solar resource: OrientSol 2.0

Nowadays there are a lot of problems concerning the use of energy among society, so a greater support to the renewable energies must be present. Some professors from the University of Jaen, Spain, have wide experience in the field of didactic resources for renewable energies subjects teaching. The use of solar energy in order to obtain electricity is called solar energy photovoltaic. This transformation is possible due to the photovoltaic effect. To design a photovoltaic system in any location it is essential to know the exact amount of solar resource available in the area. For this purpose, collecting data on solar radiation becomes crucial. Currently, there exist databases where we can find information on solar radiation but only for horizontal surfaces (known as global solar irradiance on horizontal surfaces). After this, by applying really complex mathematical equations and algorithms, it is possible to obtain solar radiation data for non-horizontal surfaces. In the present work a virtual laboratory which we developed by us is explained. The virtual laboratory “OrientSol 2.0” is an application developed with Matlab© which allows the users (students) to easily obtain the solar radiation on a non-horizontal surface (variations on tilt and orientation). Also, in this work we present all the experience acquired in some years at the University of Jaen when using this virtual laboratory by students from the following courses: Degree in Electronic Industrial Engineering and Master in Renewable Energy.