Next Article in Journal
Development of a Software Tool for Seismic Probabilistic Safety Assessment Quantification with a Sufficiently Large Number of Bins for Enhanced Accuracy
Previous Article in Journal
An Immersed Boundary Method Based Improved Divergence-Free-Condition Compensated Coupled Framework for Solving the Flow–Particle Interactions
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Energies
Volume 14
Issue 6
10.3390/en14061676
energies-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic

by
Grzegorz Dec
1,
Grzegorz Drałus
2,
Damian Mazur
2,* and
Bogdan Kwiatkowski
2
1
Department of Computer and Control Engineering, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszów, Poland
2
Department of Electrical and Computer Engineering Fundamentals, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszów, Poland
*
Author to whom correspondence should be addressed.
Energies 2021, 14(6), 1676; https://doi.org/10.3390/en14061676
Submission received: 21 February 2021 / Revised: 14 March 2021 / Accepted: 15 March 2021 / Published: 17 March 2021
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
Browse Figures
Versions Notes

Abstract

This paper contains studies of daily energy production forecasting methods for photovoltaic solar panels (PV panel) by using mathematical methods and fuzzy logic models. Mathematical models are based on analytic equations that bind PV panel power with temperature and solar radiation. In models based on fuzzy logic, we use Adaptive-network-based Fuzzy Inference Systems (ANFIS) and the zero-order Takagi-Sugeno model (TS) with specially selected linear and non-linear membership functions. The use of mentioned membership functions causes that the TS system is equivalent to a polynomial and its properties can be compared to other analytical models of PV panels found in the literature. The developed models are based on data from a real system. The accuracy of developed prognostic models is compared, and a prototype software implementing the best-performing models is presented. The software is written for a generic programmable logic controller (PLC) compliant to the IEC 61131-3 standard.
Keywords: forecasting; solar PV system; fuzzy rules; ANFIS; mathematical models forecasting; solar PV system; fuzzy rules; ANFIS; mathematical models

Share and Cite

MDPI and ACS Style

Dec, G.; Drałus, G.; Mazur, D.; Kwiatkowski, B. Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic. Energies 2021, 14, 1676. https://doi.org/10.3390/en14061676

AMA Style

Dec G, Drałus G, Mazur D, Kwiatkowski B. Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic. Energies. 2021; 14(6):1676. https://doi.org/10.3390/en14061676

Chicago/Turabian Style

Dec, Grzegorz, Grzegorz Drałus, Damian Mazur, and Bogdan Kwiatkowski. 2021. "Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic" Energies 14, no. 6: 1676. https://doi.org/10.3390/en14061676

APA Style

Dec, G., Drałus, G., Mazur, D., & Kwiatkowski, B. (2021). Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic. Energies, 14(6), 1676. https://doi.org/10.3390/en14061676

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop