• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.5
• /
• pp.79-85
• /
• 2019

In this paper, we propose a short-term power forecasting method by applying Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network to Internet of Things (IoT) power meter. We analyze performance based on real power consumption data of households. Mean absolute error (MAE), mean absolute percentage error (MAPE), mean percentage error (MPE), mean squared error (MSE), and root mean squared error (RMSE) are used as performance evaluation indexes. The experimental results show that the GRU-based model improves the performance by 4.52% in the MAPE and 5.59% in the MPE compared to the LSTM-based model.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.8
• /
• pp.2793-2800
• /
• 2010

In this paper, we propose a quadratic (nonlinear) regression model that forecasts daily demands of electric power in summer. For cost-effective production (and/or procurement) of electric power, forecasting demands of electric power with accuracy is important, especially in summer when temperature is high. In the literature, temperature and daily demands of preceding days are typically employed to construct forecasting models. While, we consider another factor, day of the week, together with temperature and daily demands of preceding days. For validating the proposed model, we demonstrate the forecasting accuracy in terms of MAPE(Mean Absolute Percentage Error) and MPE(Maximum Percentage Error) using field data from KEPCO(Korea Electric Power Corporation) in comparison with two forecasting models in the literature. When compared with the two benchmarks, the proposed forecasting model performs far better providing MAPE and MPE not exceeding 3.08% and 8.99%, respectively, in summer from 2005 to 2009.

• International Journal of Highway Engineering
• /
• v.15 no.6
• /
• pp.79-91
• /
• 2013

PURPOSES : The methods of measuring the sound from the noise source are Pass-by method and NCPX (Noble Close Proximity) method. These measuring methods were used to determine the linkage of TAPL (Total Acoustic Pressure Level) and SPL (Sound Pressure Level) in terms of frequencies. METHODS : The frequency analysis methods are DFT (Discrete Fourier Transform) and FFT (Fast Fourier Transform), CPB (Constant Percentage Bandwidth). The CPB analysis was used in this study, based on the 1/3 octave band option configured for the frequency analysis. Furthermore, the regression analysis was used at the condition related to the sound attenuation effect. The MPE (Mean Percentage Error) and RMSE (Root Mean Squared Error) were utilized for calculating the error. RESULTS : From the results of the CPB frequency analysis, the predicted SPL along the frequency has 99.1% maximum precision with the measured SPL, resulting in roughly 1 dB(A) error. The TAPL results have precision by 99.37% with the measured TAPL. The predicted TAPL results at this study by using the SPL prediction model along the frequency have the maximum precision of 98.37% with the vehicle velocity. CONCLUSIONS : The Predicted SPL model along the frequency and the TAPL result by using the predicted SPL model have a high level of accuracy through this study. But the vehicle velocity-TAPL prediction model from the previous study by using the log regression analysis cannot be consistent with the TAPL result by using the predicted SPL model.

• Korean Journal of Agricultural Science
• /
• v.38 no.1
• /
• pp.129-143
• /
• 2011

In this study, to select the incoming solar radiation equation which is most suitable for the estimation of Penman evaporation, 12 incoming solar radiation equations were selected. The Penman evaporation rates were estimated using 12 selected incoming solar radiation equations, and the estimated Penman evaporation rates were compared with measured pan evaporation rates. The monthly average daily meteorological data measured from 17 meteorological stations (춘천, 강능, 서울, 인천, 수원, 서산, 청주, 대전, 추풍령, 포항, 대구, 전주, 광주, 부산, 목포, 제주, 진주) were used for this study. To evaluate the reliability of estimated evaporation rates, mean absolute bias error(MABE), root mean square error(RMSE), mean percentage error(MPE) and Nash-Sutcliffe equation were applied. The study results indicate that to estimate pan evaporation using Penman evaporation equation, incoming solar radiation equation using meteorological data such as precipitation, minimum air temperature, sunshine duration, possible duration of sunshine, and extraterrestrial radiation are most suitable for 11 study stations out of 17 study stations.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.7
• /
• pp.884-889
• /
• 2021

The number of people infected with Covid-19 in Korea seemed to be gradually decreasing thanks to various efforts such as social distancing and vaccines. However, just as the number of infected people increased after a particular incident on February 20, 2020, the number of infected people has been increasing rapidly since December 2020 by approximately 500 per day. Therefore, the future Covid-19 is predicted through the Prophet algorithm using Kaggle's dataset, and the explanatory power for this prediction is added through the coefficient of determination, mean absolute error, mean percent error, mean square difference, and mean square deviation through Scikit-learn. Moreover, in the absence of a specific incident rapidly increasing the cases of Covid-19, the proposed method predicts the number of infected people in Korea and emphasizes the importance of implementing epidemic prevention and quarantine rules for future diseases.

• International Journal of Highway Engineering
• /
• v.15 no.3
• /
• pp.53-63
• /
• 2013

PURPOSES : This study is to evaluate moisture susceptibility of asphalt mixtures by using non-destructive impact wave and to determine durability so as to decrease the gap between before and after freezing in the future. METHODS : Using non-destructive impact wave, this study is to determine the dynamic modulus of asphalt specimen. Furthermore, the results obtained from two experiment accelerometers are used for the dynamic modulus determination. The dynamic moduli of specimens are compared with those of the freezing-thawing specimens. RESULTS : Test results showed that the dynamic modulus before freezing and thawing environment loads at each temperature dropped about 3.7% after the environmental loads. Furthermore, correlation analysis indicates that transition of dynamic modulus at each point is about 89.59%. CONCLUSIONS: Evaluation of asphalt mixtures using non-destructive impact wave has excellent repeatability and simple equipment for the test. Consequently, the method in the study will be useful for evaluating the characteristics of a various asphalt mixtures.

• International Journal of Highway Engineering
• /
• v.18 no.6
• /
• pp.123-130
• /
• 2016

OBJECTIVES : The objective of this research is to determine the moisture resistance of the freeze-thaw process occurring in low-noise porous pavement using either hydrated-lime or anti-freezing agent. Various additives were applied to low-noise porous asphalt, which is actively paved in South Korea, to overcome its disadvantages. Moreover, the optimum contents of hydrated-lime and anti-freezing agent and behavior properties of low-noise porous asphalt layer are determined using dynamic moduli via the freeze-thaw test. METHODS : The low-noise porous asphalt mixtures were made using gyratory compacters to investigate its properties with either hydrated-lime or anti-freezing agent. To determine the dynamic moduli of each mixture, impact resonance test was conducted. The applied standard for the freeze-thaw test of asphalt mixture is ASTM D 6857. The freeze-thaw and impact resonance tests were performed twice at each stage. The behavior properties were defined using finite element method, which was performed using the dynamic modulus data obtained from the freeze-thaw test and resonance frequencies obtained from non-destructive impact test. RESULTS : The results show that the coherence and strength of the low-noise porous asphalt mixture decreased continuously with the increase in the temperature of the mixture. The dynamic modulus of the normal low-noise porous asphalt mixture dramatically decreased after one cycle of freezing and thawing stages, which is more than that of other mixtures containing additives. The damage rate was higher when the freeze-thaw test was repeated. CONCLUSIONS : From the root mean squared error (RMSE) and mean percentage error (MPE) analyses, the addition rates of 1.5% hydrated-lime and 0.5% anti-freezing agent resulted in the strongest mixture having the highest moisture resistance compared to other specimens with each additive in 1 cycle freeze-thaw test. Moreover, the freeze-thaw resistance significantly improved when a hydrated-lime content of 0.5% was applied for the two cycles of the freeze-thaw test. Hence, the optimum contents of both hydrated-lime and anti-freezing agent are 0.5%.