• Title/Summary/Keyword: Solar Power Generation

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Comparison of Solar Power Generation Forecasting Performance in Daejeon and Busan Based on Preprocessing Methods and Artificial Intelligence Techniques: Using Meteorological Observation and Forecast Data (전처리 방법과 인공지능 모델 차이에 따른 대전과 부산의 태양광 발전량 예측성능 비교: 기상관측자료와 예보자료를 이용하여)

  • Chae-Yeon Shim;Gyeong-Min Baek;Hyun-Su Park;Jong-Yeon Park
    • Atmosphere
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    • v.34 no.2
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    • pp.177-185
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    • 2024
  • As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

A Study on Electric Capacity and CO2 by the Roof Top PV System of the Industrial Building in Korea (한국 산업용 건물지붕 적용 PV에 의한 발전량 및 CO2 분석연구)

  • Kim, Ji-Su;Lee, Eung-Jik;Hwang, Jung-Ha
    • Journal of the Korean Solar Energy Society
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    • v.30 no.6
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    • pp.131-136
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    • 2010
  • The purpose of this study is to provide foundational data for expansion of solar generation in building application, a clean energy, by introducing applicability of solar power generation system on roofs of industrial buildings and computing expected amounts of power and carbon dioxides reduction. As methodologies of this study, after reviewing 120,000 domestic factories to verify the BIPV feasibility for industrial building sthrough theoretical considerations of solar generation system, we calculated BIPV application methods and subsequent expected power generation quantity and carbon dioxide reductions through roof type analysis. we analyzed four cases of expected power generation amounts of solar batteries according to application methods, and when considering that the main type of roofs are slant roofs according to the investigation result about roof forms of domestic industrial complexes, we believe that the module angle of a slant roof around $17^{\circ}$(case3) is most suitable for the application. Finally, we came up with 517,944[TOE] as the corresponding petroleum tonnage based on this computed expected power generation amount and the amount of 1,214,836[$tCO_2$] carbon dioxide reductions by calculating them by energy sources.

Solar Power Generation Forecast Model Using Seasonal ARIMA (SARIMA 모형을 이용한 태양광 발전량 예보 모형 구축)

  • Lee, Dong-Hyun;Jung, Ahyun;Kim, Jin-Young;Kim, Chang Ki;Kim, Hyun-Goo;Lee, Yung-Seop
    • Journal of the Korean Solar Energy Society
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    • v.39 no.3
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    • pp.59-66
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    • 2019
  • New and renewable energy forecasts are key technology to reduce the annual operating cost of new and renewable facilities, and accuracy of forecasts is paramount. In this study, we intend to build a model for the prediction of short-term solar power generation for 1 hour to 3 hours. To this end, this study applied two time series technique, ARIMA model without considering seasonality and SARIMA model with considering seasonality, comparing which technique has better predictive accuracy. Comparing predicted errors by MAE measures of solar power generation for 1 hour to 3 hours at four locations, the solar power forecast model using ARIMA was better in terms of predictive accuracy than the solar power forecast model using SARIMA. On the other hand, a comparison of predicted error by RMSE measures resulted in a solar power forecast model using SARIMA being better in terms of predictive accuracy than a solar power forecast model using ARIMA.

Module liver optimization interval that consider generating power (출력을 고려한 모듈 간 최적화 간격)

  • Choi, Dae-Won;Choi, Hong-Kyoo;Lee, Guen-Moo;Shim, Yong-Sik;Choi, Young-Jun;Chang, Min-Kee;Kim, Tae-Hoon
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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    • 2009.05a
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    • pp.53-58
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    • 2009
  • Solar photovoltaic power generation system is judging that by the most suitable new refreshing energy in real condition of our country forward continuously interest for solar photovoltaic power generation system and diffusion me enlarged. Output decline problem is item to consider necessarily and should be verified in continuous interest for solar photovoltaic power generation system are diffusion. Present plan that minimize output decline calculating module liver optimum interval that consider recitation of a poem to reduce output decline by module liver shade by incidence angle consideration and this that occupy most parts among factor because do output of solar photovoltaic power generation system well.

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Contactless Power Supply for DC Power Service in Hybrid Home Generation System (직류수용가 서비스를 위한 무접점 전원장치)

  • Kang, J.W.;Song, H.K.;Kim, J.H.;Kim, E.S.;Kim, Y.H.
    • Proceedings of the KIPE Conference
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    • 2007.07a
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    • pp.104-107
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    • 2007
  • Among the alternative energy sources, the solar energy is recognized as an important energy source and its application is increasing. Especially in future, the hybrid solar energy generation system with battery will be widely used as an independent distributed power generation system. In this paper, a solar power hybrid home generation system using a contact-less power supply (CPS) that can transfer an electric power without any mechanical contact by using magnetic coupling instead of the power transfer by directly supplying the DC power to the home electric system is proposed. The proposed system consists of a ZVS boost converter, a half bridge LLC resonant converter and contact-less transformer.

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Analysis of Maximum Power Generation of Photovoltaic Module Depending on Constituent Materials and Incident Light Characteristics (구성 재료와 방사조도 특성에 따른 태양전지모듈의 최대출력 분석)

  • Kang, Gi-Hwan;Kim, Kyung-Soo;Park, Chi-Hong;Yu, Gwon-Jong;Ahn, Hyung-Keun;Han, Deuk-Young
    • Journal of the Korean Solar Energy Society
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    • v.27 no.3
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    • pp.1-6
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    • 2007
  • In this study, we analyze the maximum power generation of photovoltaic(PV) module depending on constituent materials and incidence angle dependence of light. To verify characteristics of constituent materials, we made photovoltaic modules with 4 kinds of solar cells and textured glass according to fabrication method. To find the degree of the maximum power generation dependence on intensity of light, Solar Simulator is applied by changing angle of module and light intensity. Through this experiment, to obtain maximum power generation from limited PV modules, it is needed to fully understand constituent materials, fabrication method and dependence of incident light characteristics.

Non-linear Regression Model Between Solar Irradiation and PV Power Generation by Using Gompertz Curve (Gompertz 곡선을 이용한 비선형 일사량-태양광 발전량 회귀 모델)

  • Kim, Boyoung;Alba, Vilanova Cortezon;Kim, Chang Ki;Kang, Yong-Heack;Yun, Chang-Yeol;Kim, Hyung-Goo
    • Journal of the Korean Solar Energy Society
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    • v.39 no.6
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    • pp.113-125
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    • 2019
  • With the opening of the small power brokerage business market in December 2018, the small power trading market has started in Korea. Operators must submit the day-ahead estimates of power output and receive incentives based on its accuracy. Therefore, the accuracy of power generation forecasts is directly affects profits of the operators. The forecasting process for power generation can be divided into two procedure. The first is to forecast solar irradiation and the second is to transform forecasted solar irradiation into power generation. There are two methods for transformation. One is to simulate with physical model, and another is to use regression model. In this study, we found the best-fit regression model by analyzing hourly data of PV output and solar irradiation data during three years for 242 PV plants in Korea. The best model was not a linear model, but a sigmoidal model and specifically a Gompertz model. The combined linear regression and Gompertz curve was proposed because a the curve has non-zero y-intercept. As the result, R2 and RMSE between observed data and the curve was significantly reduced.

Design of Sun Tracker System for Solar Power Generation (태양광 발전을 위한 태양추적시스템 설계)

  • An, Jun-Sik;Heo, Nam-Euk;Kim, Il-Hwan
    • Proceedings of the KIEE Conference
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    • 2006.10c
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    • pp.330-332
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    • 2006
  • In this paper, sun tracking system using Sun position sensor is proposed, the sun tracking system designed as which raises the efficiency of solar power generation. It design the structure being simple and it develops the system which is economical efficiency. It develops the hazard technique such as location tracking method of the sun which uses the sensor and to use the motor solar cell module movement. The Sun tracking system makes the drive in order to do with one axis and to use the sensor and to know in order to put out, the location of the sun and it makes. To make the solar location tracking sensor where the structure is simple it used two solar cells.

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Thermal Test of High-Temperature Solar Concentrating System for Hybrid Power Generation (복합발전용 고온 집광시스템의 집열 특성 분석)

  • Kim, Jin-Soo;Lee, Sang-Nam;Kang, Yong-Heack;Yun, Hwan-Ki;Yun, Chang-Kyun;Kim, Jong-Kyu
    • 한국신재생에너지학회:학술대회논문집
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    • 2006.06a
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    • pp.580-583
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    • 2006
  • A small-scale solar concentrating system was developed and demonstrated for supplying process heat required in solar thermo chemical reaction. The concentration system consists of a heliostat equipped with a solar tracking device and a dish concentrator. From the initial thermal test of the concentrating system it was found that the system works very well with around 500-600 concentration ratio capable of supplying about 3kW therml energy to the reactor. Once the concentration system was turned on, the reactor temperature rapidly increased over $1,000^{\circ}C$ and could be maintained high enough for solar chemical reaction.

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Mid- and Short-term Power Generation Forecasting using Hybrid Model (하이브리드 모델을 이용하여 중단기 태양발전량 예측)

  • Nam-Rye Son
    • Journal of the Korean Society of Industry Convergence
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    • v.26 no.4_2
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    • pp.715-724
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    • 2023
  • Solar energy forecasting is essential for (1) power system planning, management, and operation, requiring accurate predictions. It is crucial for (2) ensuring a continuous and sustainable power supply to customers and (3) optimizing the operation and control of renewable energy systems and the electricity market. Recently, research has been focusing on developing solar energy forecasting models that can provide daily plans for power usage and production and be verified in the electricity market. In these prediction models, various data, including solar energy generation and climate data, are chosen to be utilized in the forecasting process. The most commonly used climate data (such as temperature, relative humidity, precipitation, solar radiation, and wind speed) significantly influence the fluctuations in solar energy generation based on weather conditions. Therefore, this paper proposes a hybrid forecasting model by combining the strengths of the Prophet model and the GRU model, which exhibits excellent predictive performance. The forecasting periods for solar energy generation are tested in short-term (2 days, 7 days) and medium-term (15 days, 30 days) scenarios. The experimental results demonstrate that the proposed approach outperforms the conventional Prophet model by more than twice in terms of Root Mean Square Error (RMSE) and surpasses the modified GRU model by more than 1.5 times, showcasing superior performance.