• Journal of Digital Convergence
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• v.12 no.3
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• pp.211-218
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• 2014

Recently, wind energy is expanding to combination of computing to forecast of wind power generation as well as intelligent of wind powerturbine. Wind power is rise and fall depending on weather conditions and difficult to predict the output for efficient power production. Wind power is need to reliably linked technology in order to efficient power generation. In this paper, distributed power generation forecasts to enhance the predicted and actual power generation in order to minimize the difference between the power of distributed power short-term prediction model is designed. The proposed model for prediction of short-term combining the physical models and statistical models were produced in a physical model of the predicted value predicted by the lattice points within the branch prediction to extract the value of a physical model by applying the estimated value of a statistical model for estimating power generation final gas phase produces a predicted value. Also, the proposed model in real-time National Weather Service forecast for medium-term and real-time observations used as input data to perform the short-term prediction models.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.10
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• pp.87-97
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• 2020

In general, tires require various sensors and power supply devices, such as batteries, to obtain information such as pressure, temperature, acceleration, and the friction coefficient between the tire and the road in real time. However, these sensors have a size limitation because they are mounted on a tire, and their batteries have limited usability due to short replacement cycles, leading to additional replacement costs. Therefore, vibration energy harvesting technology, which converts the dynamic strain energy generated from the tire into electrical energy and then stores the energy in a power supply, is advantageous. In this study, the output voltage and power generated from piezoelectric elements are predicted through finite element analysis under static state and transient state conditions, taking into account the dynamic characteristics of tires. First, the tire and piezoelectric elements are created as a finite element model and then the natural frequency and mode shapes are identified through modal analysis. Next, in the static state, with the piezoelectric element attached to the inside of the tire, the voltage distribution at the contact surface between the tire and the road is examined. Lastly, in the transient state, with the tire rotating at the speeds of 30 km/h and 50 km/h, the output voltage and power characteristics of the piezoelectric elements attached to four locations inside the tire are evaluated.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.04a
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• pp.545-550
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• 1997

It is well known that Statistical Energy Analysis(SEA) is one of very attractive analytical methods to solve shipboard noise problems. With reasonable successes, many applications of SEA to shipboard noise prediction have been reported. However when one wishes to obtain theoretical predictions by using SEA in practical systems, he will find difficulty in modeling of source systems, that is, foundations where to place main engine, generator, compressor, and so on. Also, he will find that it is hard to determine the amount of power flow from machinery to structures. In this paper, SEA of a simple foundation model was carried out using the estimated amount of power flow from source; the estimated mobility method. The comparison between the estimated and measured results is presented. That comparison shows a method to get structure-borne noise power from the combination of machinery and foundation. This prediction method gave a good results for a air-compressor mounted on a model foundation. The method is expected to give a reasonable power output in practical problems.

• The Transactions of the Korean Institute of Power Electronics
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• v.21 no.6
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• pp.481-485
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• 2016

When analyzing economic feasibility for installing a PV generation plant at a certain location, the prediction of possible annual power production at the site using the target PV panels should be conducted on the basis of the local weather data provided by a local weather forecasting office. In addition, the prediction of PV generating power under certain weather conditions is useful for fault diagnosis and performance evaluation of PV generation plants during actual operation. This study analyzes PV cell characteristics according to a variety of weather conditions, including ambient temperature and irradiance level. From the analysis and simulation results, this work establishes a proper model that can predict the output characteristics of PV cells under changes in weather conditions.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.64-71
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• 2017

Considering the volatility, intermittent and random of photovoltaic (PV) generation systems, accurate forecasting of PV power output is important for the grid scheduling and energy management. In order to improve the accuracy of short-term power forecasting of PV systems, this paper proposes a prediction model based on environmental factors and support vector machine optimized by genetic algorithm (GA-SVM). In order to improve the prediction accuracy of this model, weather conditions are divided into three types, and the gray correlation coefficient algorithm is used to find out a similar day of the predicted day. To avoid parameters optimization into local optima, this paper uses genetic algorithm to optimize SVM parameters. Example verification shows that the prediction accuracy in three types of weather will remain at between 10% -15% and the short-term PV power forecasting model proposed is effective and promising.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.60 no.1
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• pp.87-99
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• 2024

This study deals with the application of an artificial neural network (ANN) model to predict power consumption for utilizing seawater source heat pumps of recirculating aquaculture system. An integrated dynamic simulation model was constructed using the TRNSYS program to obtain input and output data for the ANN model to predict the power consumption of the recirculating aquaculture system with a heat pump system. Data obtained from the TRNSYS program were analyzed using linear regression, and converted into optimal data necessary for the ANN model through normalization. To optimize the ANN-based power consumption prediction model, the hyper parameters of ANN were determined using the Bayesian optimization. ANN simulation results showed that ANN models with optimized hyper parameters exhibited acceptably high predictive accuracy conforming to ASHRAE standards.

• Transactions of the Korean hydrogen and new energy society
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• v.33 no.4
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• pp.419-429
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• 2022

Effects of operating variables on temperature profile and performance of 3 MWth chemical looping combustion system were estimated by mass and energy balance analysis based on configuration and dimension of the system determined by design tool. Air reactor gas velocity, fuel reactor gas velocity, solid circulation rate, and solid input percentage to fluidized bed heat exchanger were considered as representative operating variables. Overall heat output and oxygen concentration in the exhaust gas from the air reactor increased but temperature difference decreased as air reactor gas velocity increased. Overall heat output, required solid circulation rate, and temperature difference increased as fuel reactor gas velocity increased. However, overall heat output and temperature difference decreased as solid circulation rate increased. Temperature difference decreased as solid circulation rate through the fluidized bed heat exchanger increased. Effect of each variables on temperature profile and performance can be determined and these results will be helpful to determine operating range of each variable.

• Journal of Power Electronics
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• v.13 no.2
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• pp.197-205
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• 2013

In energy injection resonant AC-AC converters, due to the low frequency effect of the AC input envelope and the low energy injection losses requirement, the constant and steady control of the high frequency AC output envelope is still a problem that has not been solved very well. With the aid of system modeling, this paper analyzes the mechanism of the envelope pit on the resonant AC current. The computing methods for the critical damping point, the falling time and the bottom value of the envelope pit are presented as well. Furthermore, this paper concludes the stability precondition of the system AC output. Accordingly, an optimal control method for the AC output envelope is put forward based on the envelope prediction model. This control method can predict system responses dynamically under different series of control decisions. In addition, this control method can select best series of control decisions to make the AC output envelope stable and constant. Simulation and experimental results for a contactless power transfer system verify the control method.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.478-484
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• 2018

This study predicts solar radiation, solar radiation, and solar power generation using hourly weather data such as temperature, precipitation, wind direction, wind speed, humidity, cloudiness, sunshine and solar radiation. I/O pattern in supervised learning is the most important factor in prediction, but it must be determined by repeated experiments because humans have to decide. This study proposed four input and output patterns for solar and sunrise prediction. In addition, we predicted solar power generation using the predicted solar and solar radiation data and power generation data of Youngam solar power plant in Jeollanamdo. As a experiment result, the model 4 showed the best prediction results in the sunshine and solar radiation prediction, and the RMSE of sunshine was 1.5 times and the sunshine RMSE was 3 times less than that of model 1. As a experiment result of solar power generation prediction, the best prediction result was obtained for model 4 as well as sunshine and solar radiation, and the RMSE was reduced by 2.7 times less than that of model 1.

• Journal of Digital Convergence
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• v.12 no.3
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• pp.237-242
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• 2014

Recently, wind energy is expanding to combination of computing to forecast of wind power generation as well as intelligent of wind powerturbine. Wind power is rise and fall depending on weather conditions and difficult to predict the output for efficient power production. Wind power is need to reliably linked technology in order to efficient power generation. In this paper, distributed power generation forecasts to enhance the predicted and actual power generation in order to minimize the difference between the power of distributed power short-term prediction model is designed. The proposed model for prediction of short-term combining the physical models and statistical models were produced in a physical model of the predicted value predicted by the lattice points within the branch prediction to extract the value of a physical model by applying the estimated value of a statistical model for estimating power generation final gas phase produces a predicted value. Also, the proposed model in real-time National Weather Service forecast for medium-term and real-time observations used as input data to perform the short-term prediction models.