Solar Power Generation Prediction Using Machine Learning and Study of Power Generation Using Solar Tracking Panels

被引：2

作者：

Ki, Jun Hong ^{[1
]}

Baek, Seung-jun ^{[1
]}

So, Jae-young ^{[1
]}

Eom, Han-gyeol ^{[1
]}

Shin, Jeong-Heon ^{[1
]}

机构：

[1] Hongik Univ, Dept Mech Syst & Design Engn, Seoul, South Korea

来源：

TRANSACTIONS OF THE KOREAN SOCIETY OF MECHANICAL ENGINEERS B | 2023年 / 47卷 / 01期

关键词：

Machine Learning; Recurrent Neural Network; Pearson Correlation; Ensemble; Photovoltaics; Field Experiments; Sun Tracking System; Computation of Sun Position;

D O I：

10.3795/KSME-B.2023.47.1.055

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

As the importance of renewable energy is growing worldwide, Republic of Korea aims to increase the proportion of renewable energy generation to 30.2% by 2030. Solar power plays an important role, accounting for more than 80% of domestic renewable energy generation. However, since the amount of solar power generation varies greatly depending on weather factors, it is necessary to respond to the instability of solar power generation and increase the generation amount in order to secure stable energy. Therefore, in this paper, we learn a machine learning model based on weather data to predict the amount of solar power generation, calculate the solar altitude and azimuth that change according to seasons and time, and increase the amount of solar power generation by controlling the solar power panel. As a result of the experiment, the power generation was predicted to be within 0.208 mean square error, and it was confirmed that the difference in power generation before and after sun tracking was 23.4%.

引用

页码：55 / 62

页数：8

共 50 条

[1] ENHANCEMENT OF SOLAR PANELS' POWER GENERATION BY THE USAGE OF SOLAR TRACKING
Plesz, Balazs
Sagi, Peter
Timar-Horvath, Veronika
PROCEEDINGS OF ECOPOLE, 2009, 2009, 3 (01): : 15 - 22
[2] Solar-Cast: Solar Power Generation Prediction from Weather Forecasts using Machine Learning
Singhal, Rishi
Singhal, Poonam
Gupta, Shailender
2022 IEEE 10TH POWER INDIA INTERNATIONAL CONFERENCE, PIICON, 2022,
[3] Solar Power Generation Prediction Using a Lightweight Machine Learning Model for Edge Computing
Minh-Hoang Nguyen
Van-Nhan Nguyen
Trong-Minh Hoang
Chalee Vorakulpipat
Nam-Hoang Nguyen
INTELLIGENCE OF THINGS: TECHNOLOGIES AND APPLICATIONS, ICIT 2024, VOL 2, 2025, 230 : 134 - 145
[4] The Prediction of Power in Solar Panel using Machine Learning
Garg, Umang
Chohan, Deepak Kumar
Dobhal, Dinesh C.
2021 INTERNATIONAL CONFERENCE ON COMPUTATIONAL PERFORMANCE EVALUATION (COMPE-2021), 2021, : 354 - 358
[5] Evaluation of Photovoltaic Power Generation by Using Deep Learning in Solar Panels Installed in Buildings
Wei, Chih-Chiang
ENERGIES, 2019, 12 (18)
[6] Analysis and Modeling of Photovoltaic Arrays for Sustaining Power Generation in Geostationary Satellite Solar Panels using Machine Learning
Oz, Ibrahim
Bulut, Mehmet
JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI, 2025,
[7] Solar Panel Tracking and Power Generation Using Automatic system
Pushpavalli, M.
Sivagami, P.
Abirami, P.
Sindhuja, S.
Amzadkhan, Pathan
2018 IEEE 4TH INTERNATIONAL SYMPOSIUM IN ROBOTICS AND MANUFACTURING AUTOMATION (ROMA), 2018,
[8] Solar power generation prediction based on deep Learning
Chang, Rui
Bai, Lei
Hsu, Ching-Hsien
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS, 2021, 47
[9] Forecasting hourly electricity generation by a solar power plant using machine learning algorithms
Morgoeva, A. D.
Morgoev, I. D.
Klyuev, R. V.
Kochkovskaya, S. S.
BULLETIN OF THE TOMSK POLYTECHNIC UNIVERSITY-GEO ASSETS ENGINEERING, 2023, 334 (12): : 7 - 19
[10] Solar power generation prediction based on deep Learning
Chang, Rui
Bai, Lei
Hsu, Ching-Hsien
Sustainable Energy Technologies and Assessments, 2021, 47

← 1 2 3 4 5 →