Solar radiation: Cloudiness forecasting using a soft computing approach

Vassiliki H. Mantzari, Dimitrios H. Mantzaris

Abstract


Solar energy is one of the most important energy sources with increasing penetration into the power supply systems ofmany countries, due to the reduced environmental impact of its operation. One of the important factors of the efficiency ofphotovoltaic systems is the predictability of solar radiation, which depends on the clouds and the meteorologicalconditions, the occurrence of which is a non-linear process. Prediction of clouds’ amount that affects solar radiation usingArtificial Neural Networks (ANNs) is presented in this paper. To our knowledge, this approach is the first computationalintelligence method that refers to cloudiness forecasting. Input parameter selection is very critical in ANN design. In thisstudy, ten meteorological and temporal variables were selected in the implementation of the ANN structure. Theappropriate ANN architecture consists of a hidden layer with hyperbolic tangent sigmoid transfer function and an outputlayer with a saturating linear transfer function. The most important benefits of the proposed method are the cloudinessforecasting in the next half hour, the embedment of temporal parameters for cloudiness prediction and the highest quantityof data comparatively with other similar studies.


Full Text: PDF DOI: 10.5430/air.v2n1p69

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

Artificial Intelligence Research

ISSN 1927-6974 (Print)   ISSN 1927-6982 (Online)

Copyright © Sciedu Press 
To make sure that you can receive messages from us, please add the 'Sciedu.ca' domain to your e-mail 'safe list'. If you do not receive e-mail in your 'inbox', check your 'bulk mail' or 'junk mail' folders.