A Novel Application of Naive Bayes Classifier in Photovoltaic Energy Prediction

被引：37

作者：

Bayindir, Ramazan ^{[1
]}

Yesilbudak, Mehmet ^{[2
]}

Colak, Medine ^{[1
]}

Genc, Naci ^{[3
]}

机构：

[1] Gazi Univ, Fac Technol, Dept Elect & Elect Engn, TR-06500 Ankara, Turkey

[2] Nevsehir Haci Bektas Veli Univ, Dept Elect & Elect Engn, Fac Engn & Architecture, TR-50300 Nevsehir, Turkey

[3] Yuzuncu Yil Univ, Dept Elect & Elect Engn, Fac Engn & Architecture, TR-65080 Van, Turkey

来源：

2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) | 2017年

关键词：

PV system; solar energy; Naive Bayes; prediction; GLOBAL SOLAR-RADIATION; MODEL; ALGORITHMS;

D O I：

10.1109/ICMLA.2017.0-108

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Solar energy is one of the most affordable and clean renewable energy source in the world. Hence, the solar energy prediction is an inevitable requirement in order to get the maximum solar energy during the day time and to increase the efficiency of solar energy systems. For this purpose, this paper predicts the daily total energy generation of an installed photovoltaic system using the Naive Bayes classifier. In the prediction process, one-year historical dataset including daily average temperature, daily total sunshine duration, daily total global solar radiation and daily total photovoltaic energy generation parameters are used as the categorical-valued attributes. By means of the Naive Bayes application, the sensitivity and the accuracy measures are improved for the photovoltaic energy prediction and the effects of other solar attributes on the photovoltaic energy generation are evaluated.

引用

页码：523 / 527

页数：5

共 21 条

[1]

Abedin Z, 2017, 2017 INTERNATIONAL CONFERENCE ON ELECTRICAL, COMPUTER AND COMMUNICATION ENGINEERING (ECCE), P692, DOI 10.1109/ECACE.2017.7912993

[2]

[Anonymous], INT J INFORM TECHNOL

[3]

[Anonymous], 2016, WORLD EN OUTL

[4] Prediction of daily and mean monthly global solar radiation using support vector machine in an arid climate [J].

Belaid, S. ;

Mellit, A. .

ENERGY CONVERSION AND MANAGEMENT, 2016, 118 :105-118

[5]

Bhattacharyya S., 2015, HDB RES ADV HYBRID I, P492

[6] Design of an Intelligent Decision Making System for a Travelling Wave Ultrasonic Motor [J].

Colak, Ilhami ;

Bayindir, Ramazan ;

Kahraman, H. Tolga ;

Yesilbudak, Mehmet .

EIGHTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, PROCEEDINGS, 2009, :669-672

[7] Next Day Power Forecast Model Using Smart Hybrid Energy Monitoring System and Meteorological Data [J].

Creayla, Christine May C. ;

Garcia, Felan Carlo C. ;

Macabebe, Erees Queen B. .

2016 IEEE INTERNATIONAL SYMPOSIUM ON ROBOTICS AND INTELLIGENT SENSORS (IRIS 2016), 2017, 105 :256-263

[8]

Enerji Enstitusu, GUN EN

[9] Performance assessment of different day-of-the-year-based models for estimating global solar radiation - Case study: Egypt [J].

Hassan, Gasser E. ;

Youssef, M. Elsayed ;

Ali, Mohamed A. ;

Mohamed, Zahraa E. ;

Shehata, Ali I. .

JOURNAL OF ATMOSPHERIC AND SOLAR-TERRESTRIAL PHYSICS, 2016, 149 :69-80

[10] Potential of four different machine-learning algorithms in modeling daily global solar radiation [J].

Hassan, Muhammed A. ;

Khalil, A. ;

Kaseb, S. ;

Kassem, M. A. .

RENEWABLE ENERGY, 2017, 111 :52-62

← 1 2 3 →