• Journal of the Korean Solar Energy Society
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• v.33 no.3
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• pp.114-123
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• 2013

In these days, most of the office buildings are being required to save energy for maintenance. lighting system constitutes 20% to 30% of the total annual electrical energy consumption in office buildings. As an energy saving strategy for lighting system, dimming control system based on illuminance sensors came into use. But the system is accompanied with many illuminance sensors to control lighting and needs a lot of initial investment. In this study, the prediction equation for indoor daylighting illuminance distribution is proposed through the review for conventional research results and field measurements. The proposed equation was verified by the comparison between predicted results and field measurement results. The developed prediction equation for daylighting can be used to control the indoor illuminance level with the limited sensor when dimming control system is operated.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.21 no.2
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• pp.225-234
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• 2023

In the design of HLW repositories, it is important to confirm the performance and safety of buffer materials at high temperatures. Most existing models for predicting hydraulic conductivity of bentonite buffer materials have been derived using the results of tests conducted below 100℃. However, they cannot be applied to temperatures above 100℃. This study suggests a prediction model for the hydraulic conductivity of bentonite buffer materials, valid at temperatures between 100℃ and 125℃, based on different test results and values reported in literature. Among several factors, dry density and temperature were the most relevant to hydraulic conductivity and were used as important independent variables for the prediction model. The effect of temperature, which positively correlates with hydraulic conductivity, was greater than that of dry density, which negatively correlates with hydraulic conductivity. Finally, to enhance the prediction accuracy, a new parameter reflecting the effect of dry density and temperature was proposed and included in the final prediction model. Compared to the existing model, the predicted result of the final suggested model was closer to the measured values.

• Journal of Wind Energy
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• v.15 no.2
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• pp.10-22
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• 2024

This research is a comprehensive analysis of wind power prediction sensitivity using a Long Short-Term Memory (LSTM) deep learning neural network model, accounting for the inherent uncertainties in wind speed estimation. Utilizing a year's worth of operational data from an operational wind farm, the study forecasts the power output of both individual wind turbines and the farm collectively. Predictions were made daily at intervals of 10 minutes and 1 hour over a span of three months. The model's forecast accuracy was evaluated by comparing the root mean square error (RMSE), normalized RMSE (NRMSE), and correlation coefficients with actual power output data. Moreover, the research investigated how inaccuracies in wind speed inputs affect the power prediction sensitivity of the model. By simulating wind speed errors within a normal distribution range of 1% to 15%, the study analyzed their influence on the accuracy of power predictions. This investigation provided insights into the required wind speed prediction error rate to achieve an 8% power prediction error threshold, meeting the incentive standards for forecasting systems in renewable energy generation.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.3
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• pp.375-381
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• 2012

Two experiments were conducted to establish a digestible energy (DE) content prediction model of rapeseed meal for growing-finishing pig based on rapeseed meal's chemical composition. In experiment 1, observed linear relationships between the determined DE content of 22 rapeseed meal calibration samples and proximate nutrients, gross energy (GE) and neutral detergent fiber (NDF) were used to develop the DE prediction model. In experiment 2, 4 samples of rapeseed meal selected at random from the primary rapeseed growing regions of China were used for testing the accuracy of DE prediction models. The results indicated that the DE was negatively correlated with NDF (r = -0.86) and acid detergent fiber (ADF) (r = -0.73) contents, and moderately correlated with gross energy (GE; r = 0.56) content in rapeseed meal calibration samples. In contrast, no significant correlations were found for crude protein, ether extract, crude fiber and ash contents. According to the regression analysis, NDF or both NDF and GE were found to be useful for the DE prediction models. Two prediction models: DE = 16.775-0.147${\times}$NDF ($R^2$ = 0.73) and DE = 11.848-0.131${\times}$NDF+0.231${\times}$GE ($R^2$ = 0.76) were obtained. The maximum absolute difference between the in vivo DE determinations and the predicted DE values was 0.62 MJ/kg and the relative difference was 5.21%. Therefore, it was concluded that, for growing-finishing pigs, these two prediction models could be used to predict the DE content of rapeseed meal with acceptable accuracy.

• Journal of Electrochemical Science and Technology
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• v.9 no.1
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• pp.51-59
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• 2018

Various Si-Fe-Al ternary alloys were prepared with the same amount of Si by the melt spinning technique. The feasibility of the capacity prediction approach based on the estimation of the active amount of Si using the phase diagram was practically examined and reported. These predictions were verified by the electrochemical test of fabricated coin cells and other characterization methods. The capacity prediction approach using the phase diagram might be a fundamental and efficient method to accelerate the practical application of Si-based alloys as the anode material for Li-ion batteries. The details on the prediction procedure were discussed.

• Journal of Advanced Navigation Technology
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• v.21 no.4
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• pp.365-370
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• 2017

This study analyze correlation between weekdays data and special days data of different power demand patterns, and builds a separate data set, and suggests ways to reduce power demand prediction error by using deep learning network suitable for each data set. In addition, we propose a method to improve the prediction rate by adding the environmental elements and the separating element to the meteorological element, which is a basic power demand prediction elements. The entire data predicted power demand using LSTM which is suitable for learning time series data, and the special day data predicted power demand using DNN. The experiment result show that the prediction rate is improved by adding prediction elements other than meteorological elements. The average RMSE of the entire dataset was 0.2597 for LSTM and 0.5474 for DNN, indicating that the LSTM showed a good prediction rate. The average RMSE of the special day data set was 0.2201 for DNN, indicating that the DNN had better prediction than LSTM. The MAPE of the LSTM of the whole data set was 2.74% and the MAPE of the special day was 3.07 %.

• Computers and Concrete
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• v.12 no.4
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• pp.377-392
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• 2013

This paper investigates prediction models estimating the hydration properties of concrete, such as the compressive strength, the splitting tensile strength, the elastic modulus,and the autogenous shrinkage. A prediction model is suggested on the basis of an equation that is formulated to predict the compressive strength. Based on the assumption that the apparent activation energy is a characteristic property of concrete, a prediction model for the compressive strength is applied to hydration-related properties. The hydration properties predicted by the model are compared with experimental results, and it is concluded that the prediction model properly estimates the splitting tensile strength, elastic modulus, and autogenous shrinkage as well as the compressive strength of concrete.

• Solar Energy
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• v.20 no.2
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• pp.67-73
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• 2000

This study represents the methodology of performance prediction for small hydro power(SHP) sites. Nine tunnel type SHP sites with diversion dam were selected and the performance characteristics were analyzed by using a developed model. Also, primary design specifications such as design flowrate, plant capacity, and operational rate were suggested and feasibility for tunnel-type SHP sites were estimated. It was found that the design flowrate was most important parameter to exploit SHP plant and the methodology developed in this study was useful tool to analyze the performance of SHP sites.

• Korean Journal of Computational Design and Engineering
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• v.21 no.1
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• pp.69-77
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• 2016

The purpose of this study is to estimate the energy expenditure of working in a clean room for manufacturing embedded needles by ergonomic programs. Embedding needle is one of medical devices and it should be manufactured in a clean room. 3D static strength prediction program was used to analyze the slow movements during embedding needle manufacturing in a clean room. Also the energy expenditure prediction program was used to estimate energy expenditure rates for materials handling tasks to help assure worker safety and health in clean room. The energy expenditures of the tasks were calculated using prediction equations derived from empirical data. The energy expenditure rate of 3.09 kcal/min in a clean room didn't exceed the 3.5 kcal/min action limit guideline for an average 8-hour day set by the National Institute for Occupational Safety and Health (NIOSH). Energy consumption was calculated on the same working conditions as EEPP program, using an average body weight of female 20 years old to 59 years who would be the candidates of the real workers.

• Journal of the Korean Solar Energy Society
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• v.35 no.3
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• pp.1-8
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• 2015

The MERRA reanalysis data provided online by NASA was applied to predict the monthly energy productions of Donghai Bridge Offshore wind farms in China. WindPRO and WindSim that are commercial software for wind farm design and energy prediction were used. For topography and roughness map, the contour line data from SRTM combined with roughness information were made and used. Predictions were made for 11 months from July, 2010 to May, 2011, and the results were compared with the actual electricity energy production presented in the CDM(Clean Development Mechanism)monitoring report of the wind farm. The results from the prediction programs were close to the actual electricity energy productions and the errors were within 4%.