Next Article in Journal
Acknowledgement to Reviewers of Applied Sciences in 2018
Next Article in Special Issue
Optimal Energy Routing Design in Energy Internet with Multiple Energy Routing Centers Using Artificial Neural Network-Based Reinforcement Learning Method
Previous Article in Journal
Laboratory and Field Experiments on the Effect of Vinyl Acetate Polymer-Reinforced Soil
Previous Article in Special Issue
Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting
Article Menu
Issue 1 (January-1) cover image

Export Article

Open AccessArticle

Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications

1
Renewable Energy Department, University of Corsica, UMR CNRS 6134, Route des Sanguinaires, 20000 Ajaccio, France
2
Castelluccio Hospital, Radiotherapy Unit, BP 85, 20177 Ajaccio, France
3
Laboratory Physical and Mathematical Engineering for Energy, Environment and Building, University of Reunion Island, 15 Avenue René Cassin, BP, 97715 Saint-Denis CEDEX, France
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(1), 209; https://doi.org/10.3390/app9010209
Received: 4 December 2018 / Revised: 27 December 2018 / Accepted: 31 December 2018 / Published: 8 January 2019
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
  |  
PDF [5212 KB, uploaded 9 January 2019]
  |  

Abstract

In solar energy, the knowledge of solar radiation is very important for the integration of energy systems in building or electrical networks. Global horizontal irradiation (GHI) data are rarely measured over the world, thus an artificial neural network (ANN) model was built to calculate this data from more available ones. For the estimation of 5-min GHI, the normalized root mean square error (nRMSE) of the 6-inputs model is 19.35%. As solar collectors are often tilted, a second ANN model was developed to transform GHI into global tilted irradiation (GTI), a difficult task due to the anisotropy of scattering phenomena in the atmosphere. The GTI calculation from GHI was realized with an nRMSE around 8% for the optimal configuration. These two models estimate solar data at time, t, from other data measured at the same time, t. For an optimal management of energy, the development of forecasting tools is crucial because it allows anticipation of the production/consumption balance; thus, ANN models were developed to forecast hourly direct normal (DNI) and GHI irradiations for a time horizon from one hour (h+1) to six hours (h+6). The forecasting of hourly solar irradiation from h+1 to h+6 using ANN was realized with an nRMSE from 22.57% for h+1 to 34.85% for h+6 for GHI and from 38.23% for h+1 to 61.88% for h+6 for DNI. View Full-Text
Keywords: solar irradiation; estimation; forecasting; meteorological data; short time step solar irradiation; estimation; forecasting; meteorological data; short time step
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Notton, G.; Voyant, C.; Fouilloy, A.; Duchaud, J.L.; Nivet, M.L. Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications. Appl. Sci. 2019, 9, 209.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top