Paper Info
Title
Fuzzy Forecasting Of Energy Production In Solar Photovoltaic Installations
Abstract
In this paper we describe a fuzzy rule-based classifier applied to forecasting of energy production in solar photovoltaic installations. After adapting the available numerical data to a dataset appropriate for classification, we propose a processing method to create an efficient rule base. The aim is to build an intelligent system able to forecast the class label of the energy production from a photovoltaic installation, given the values of some environmental parameters. Despite some already existing methods for forecasting problems, the main advantages of our approach are easier interpretability and versatility, as we deal with class labels. Moreover we propose a way to extract an ad hoc training dataset, in order to perform an effective training even when we deal with non optimal data (e. g., non-uniformly sampled data, missing samples, etc.). With a fuzzy forecasting system, in place of a traditional one, even the non-expert user of a photovoltaic system may be able to make decisions more easily. The results obtained show a correct classification percentage of almost 93%.
Year
DOI
Venue
2012
10.1109/FUZZ-IEEE.2012.6251161
2012 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
Keywords
Field
DocType
forecasting, fuzzy rule-based classifiers, pattern recognition, PRTools, solar photovoltaic energy
Data mining,Interpretability,Fuzzy reasoning,Computer science,Fuzzy logic,Knowledge-based systems,Fuzzy set,Artificial intelligence,Classifier (linguistics),Photovoltaic system,Machine learning,Fuzzy rule
Conference
ISSN
Citations 
PageRank 
1098-7584
1
0.38
References 
Authors
7
2
Name
Order
Citations
PageRank
Eleonora D'Andrea1516.19
Beatrice Lazzerini271545.56