학술논문

Application of a Hybrid Neural Fuzzy Inference System to Forecast Solar Intensity
Document Type
Conference
Source
2016 27th International Workshop on Database and Expert Systems Applications (DEXA) DEXA Database and Expert Systems Applications (DEXA), 2016 27th International Workshop on. :161-165 Sep, 2016
Subject
Computing and Processing
Forecasting
Support vector machines
Artificial neural networks
Fuzzy logic
Training
Inference algorithms
Artificial Neural Networks; Hybrid Neural Fuzzy Inference System; Solar Forecasting; Support Vector Machines
Language
ISSN
2378-3915
Abstract
This paper presents a proposal for the use of the Hybrid Fuzzy Inference System algorithm (HyFIS) as solar intensity forecast mechanism. Fuzzy Inference Systems (FIS) are used to solve regression problems in various contexts. The HyFIS is a method based on FIS with the particular advantage of combining fuzzy concepts with Artificial Neural Networks (ANN), thus optimizing the learning process. This algorithm is part of several other FIS algorithms implemented in the Fuzzy Rule-Based Systems (FRBS) package of R. The ANN algorithms and Support Vector Machine (SVM), both widely used for solving regression problems, are also used in this study to allow the comparison of results. Results show that HyFIS presents higher performance when compared to the ANN and SVM, when applied to real data of Florianopolis, Brazil, which helps to reinforce the potential of this algorithm in solving the solar intensity forecasting problems.