• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1874-1885
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• 2018

The customer side operation is getting more complex in a smart grid environment because of the adoption of renewable resources. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. The PV system is one of the common renewable energy resources in customer side. Its output depends on weather and physical characteristics of the PV system. Thus, weather information is essential to predict the amount of PV system output. However, weather forecast usually does not include enough solar irradiation information. In this study, a PV system power output prediction model (PPM) under limited weather information is proposed. In the proposed model, meteorological radiation model (MRM) is used to improve cloud cover radiation model (CRM) to consider the seasonal effect of the target region. The results of the proposed model are compared to the result of the conventional CRM prediction method on the PV generation obtained from a field test site. With the PPM, root mean square error (RMSE), and mean absolute error (MAE) are improved by 23.43% and 33.76%, respectively, compared to CRM for all days; while in clear days, they are improved by 53.36% and 62.90%, respectively.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.12
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• pp.10-19
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• 2008

Daylight responsive dimming systems are energy saving systems using available daylight. It is not popular to be adopted in buildings because of blocked daylight by shading systems. Therefore, both daylight responsive dimming systems and automated roller shading systems should be integrated. In this research, prediction process of yearly lighting energy savings is developed for application integrated systems in buildings, and then the process is applied to an example building. The predicted data which are yearly lighting energy savings using developed process in a building are useful as a part of feasibility study for determination of application of such integrated systems.

• 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 Hydrogen and New Energy
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• v.35 no.4
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• pp.437-450
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• 2024

Sustainable shipping depends on eco-friendly energy solutions. This paper reviews methods for predicting marine fuel cell performance, including empirical approaches, physical modeling, data-driven techniques, and hybrid methods. Accurate prediction models tailored to the marine environment's unique conditions are crucial for operational efficiency. By evaluating the strengths and weaknesses of each method, this study provides a comprehensive analysis of effective strategies for forecasting polymer electrolyte membrane fuel cell and solid oxide fuel cell performance in marine applications. These insights contribute to the advancement of eco-friendly shipping technologies and enhance fuel cell performance in challenging marine environments.

• 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.