• Journal of the Korean Society for Railway
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• v.20 no.4
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• pp.474-481
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• 2017

When designing photovoltaic power plants in Korea, the prediction of photovoltaic power generation at the design phase is carried out using PVSyst, PVWatts (Overseas power generation prediction software), and overseas weather data even if the test site is a domestic site. In this paper, for a comparative study to predict power generation using weather information, domestic photovoltaic power plants in two regions were selected as target sites. PVsyst, which is a commercial power generation forecasting program, was used to compare the accuracy between the predicted value of power generation (obtained using overseas weather information (Meteonorm 7.1, NASA-SSE)) and the predicted value of power generation obtained by the Korea Meteorological Administration (KMA). In addition, we have studied ways to improve the prediction of power generation through comparative analysis of meteorological data. Finally, we proposed a revised solar power generation prediction model that considers climatic factors by considering the actual generation amount.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.3
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• pp.182-187
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• 2018

In this study, 20.8% of a p-type Si bifacial solar cell was used to develop a photovoltaic (PV) module to obtain the maximum power under a limited installation area. The transparent back sheet material was replaced during fabrication with a white one, which is opaque in commercial products. This is very beneficial for the generation of more electricity, owing to the additional power generation via absorption of light from the rear side. A new model is suggested herein to predict the power of the bifacial PV module by considering the backside reflections from the roof and/or environment. This model considers not only the frontside reflection, but also the nonuniformity of the backside light sources. Theoretical predictions were compared to experimental data to prove the validity of this model, the error range for which ranged from 0.32% to 8.49%. Especially, under $700W/m^2$, the error rate was as low as 2.25%. This work could provide theoretical and experimental bases for application to a distributed and microgrid network.

• Current Photovoltaic Research
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• v.5 no.1
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• pp.33-37
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• 2017

Today, photovoltaic power generation mostly uses Si crystalline solar cell modules. The most vulnerable part of the Si solar cell module is that the power generation decreases due to the temperature rise. But, it is widely used because of low installation cost. In the solar market, where Si crystalline solar cell modules are widely used. The CPV (Concentrated Photovoltaic) module appeared in the solar market. The CPV module reduces the manufacturing cost of the solar cell by using non-Si in the solar cell. Also, there is an advantage that a rise in temperature does not cause a drop in power generation. But this requires high technology to install and has a disadvantage that the initial installation cost is expensive compared to normal Si solar cell module. So that we built a testbed to see these characteristics. The testbed was used to measure the amount of power generation in a long-term outdoor environment and compared with the general Si solar cell module.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.3
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• pp.393-398
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• 2015

City or Community-scale Energy Management System(CEMS) is used to reduce the total energy consumed in the city by arranging the energy resources efficiently at the planning stage and controlling them economically at the operating stage. Of the operational functions of the CEMS, generation forecasting of renewable energy resources is an essential feature for the effective supply scheduling. This is because it can develop daily operating schedules of controllable generators in the city (e.g. diesel turbine, micro-gas turbine, ESS, CHP and so on) in order to minimize the inflow of the external power supply system, considering the amount of power generated by the uncontrollable renewable energy resources. This paper is written to introduce numerical models for photo-voltaic power generation prediction based on the weather forecasting information. Unlike the conventional methods using the average radiation or average utilization rate, the proposed models are developed for CEMS applications using the realtime weather forecast information provided by the National Weather Service.

• Design & Manufacturing
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• v.15 no.3
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• pp.50-55
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• 2021

Solar power generation, which is one of the methods of using solar energy, has a high possibility of practical implementation compared to other renewable energy power generation, and it has the characteristic that it can generate as much power as needed in necessary places. In addition, maintenance is easy, unmanned operation is possible, and power management can be performed more efficiently if operated in a hybrid method with existing electric energy. Therefore, in this study, numerical analysis using a computer program was performed to analyze the efficient operation and performance improvement of solar energy of the rotating condensing type solar LED street lamp. As a result, the two-axis tracking type could obtain 15.23 % more electricity per year than the fixed type, and additional auxiliary power generation was required for the fixed type by 19 % per year than the tracking type. As a result of computational fluid dynamics(CFD) simulation for PV module surface temperature prediction, the The surface temperature of the Photovoltaics(PV) module incident surface was predicted to be about 10℃ higher than that of the fixed type.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.4
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• pp.837-844
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• 2018

In order to increase the generation of renewable energy, it is necessary to increase or decrease the generation amount of existing generators. The generators that respond rapidly to increase / decrease the generation amount generally have high generation cost. Therefore, Cost effectiveness is affected. In this paper, we propose a PV remote control system with big data to minimize the uncertainty of solar power generation prediction.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.353-360
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• 2016

Recently, solar photovoltaic(PV) power generation which generates electrical power from solar panels composed of multiple solar cells, showed the most prominent growth in the renewable energy sector worldwide. However, in spite of increased demand and need for a photovoltaic power generation, it is difficult to early detect defects of solar panels and equipments due to wide and irregular distribution of power generation. In this paper, we choose an optimal machine learning algorithm for estimating the generation amount of solar power by considering several panel information and climate information and develop a defect detection system by using the chosen algorithm generation. Also we apply the algorithm to a domestic solar photovoltaic power plant as a case study.

• Journal of Bio-Environment Control
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• v.26 no.4
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• pp.258-267
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• 2017

The price competitiveness of photovoltaic system (PV system) has risen recently due to the growth of industries, however, it is rarely applied to the greenhouse compared to other renewable energy. In order to evaluate the application of PV system in the greenhouse, power generation and optimal installation area of PV panels should be analyzed. For this purpose, the prediction of the heating and cooling loads of the greenhouse is necessary at first. Therefore, periodic and maximum energy loads of a multi-span greenhouse were estimated using Building Energy Simulation(BES) and optimal installation area of PV panels was derived in this study. 5 parameter equivalent circuit model was applied to analyzed power generation of PV system under different installation angle and the optimal installation condition of the PV system was derived. As a result of the energy simulation, the average cooling load and heating load of the greenhouse were 627,516MJ and 1,652,050MJ respectively when the ventilation rate was $60AE{\cdot}hr^{-1}$. The highest electric power production of the PV system was generated when the installation angle was set to $30^{\circ}$. Also, adjustable PV system produced about 6% more electric power than the fixed PV system. Optimal installation area of the PV panels was derived with consideration of the estimated energy loads. As a result, optimal installation area of PV panels for fixed PV system and adjustable PV system were $521m^2$ and $494m^2$ respectively.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.561-569
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• 2019

Solar photovoltaic can provide electrical energy with only radiation, and its use is expanding rapidly as a new energy source. This study predicts the short and long-term PV power generation using actual converter output data of photovoltaic system. The prediction algorithm uses multiple linear regression, support vector machine (SVM), and deep learning such as deep neural network (DNN) and long short-term memory (LSTM). In addition, three models are used according to the input and output structure of the weather element. Long-term forecasts are made monthly, seasonally and annually, and short-term forecasts are made for 7 days. As a result, the deep learning network is better in prediction accuracy than multiple linear regression and SVM. In addition, LSTM, which is a better model for time series prediction than DNN, is somewhat superior in terms of prediction accuracy. The experiment results according to the input and output structure appear Model 2 has less error than Model 1, and Model 3 has less error than Model 2.

• Journal of the Korean Solar Energy Society
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• v.33 no.6
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• pp.62-69
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• 2013

In this paper, Korea Institute of Energy Research, building integrated renewable energy monitoring system that utilizes solar power generation forecast data forecast model is proposed. Renewable energy integration of real-time monitoring system based on monitoring data were building a database and the database of the weather conditions and to study the correlation structure was tailoring. The weather forecast cloud cover data, generation data, and solar radiation data, a data mining and time series analysis using the method developed models to forecast solar power. The development of solar power in order to forecast model of weather forecast data it is important to secure. To this end, in three hours, including a three-day forecast today Meteorological data were used from the KMA(korea Meteorological Administration) site offers. In order to verify the accuracy of the predicted solar circle for each prediction and the actual environment can be applied to generation and were analyzed.