• 설비공학논문집
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• 제4권3호
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• pp.204-216
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• 1992

In most cases, EER(Energy Efficiency Ratio) is available to present energy efficiency of air-conditioners. But, EER is not adapt to measure energy efficiency at actual life environment because it is based on fixed temperature and humidity contditions. To overcome this disadvantage, there is need to introduce SEER(Seasonal Energy Efficiency Ratio) established at time varient temperature and humidity conditions. In this paper, SEER measurement method and conditions based on actual life environment of the country is introduced, and discussed SEER value about two air-conditioner type, that is, non inverter air-conditioner and inverter air-conditioner. As a result of, inverter air-conditioner was superior to non inverter air-conditioner at cooling seasonal energy efficiency.

• 응용통계연구
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• 제29권1호
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• pp.231-242
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• 2016

시계열 분석 소프트웨어로 국내에서도 많이 사용되는 X-13-ARIMA에서 제공하고 있는 계절이동평균필터($3{\times}3$, $3{\times}5$, $3{\times}9$, $3{\times}15$)가 외국과 다르게 불규칙한 변동이 많고 다양한 변동이 존재하는 한국의 경제 시계열에 적합한가라는 의문 속에서 새로운 계절이동평균필터들의 필요성이 제기되었다. 본 연구에서는 최근에 개발된 새로운 계절이동평균필터($3{\times}7$, $3{\times}11$)를 소개한다. 또한, 새롭게 작성된 계절이동평균필터를 국내의 경제 시계열에 적용하여 그 적합성과 안정성을 비교한 결과, 일부 시계열에서 새로운 계절이동평균필터들의 필요성이 발견되었다. 새로 개발된 계절이동평균필터를 활용하여 각 시계열에 맞는 적절한 계절조정방법을 사용하면 더욱 정확한 시계열분석을 할 수 있을 것이라 기대된다.

• Journal of the Korean Data and Information Science Society
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• 제20권1호
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• pp.117-124
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• 2009

시계열자료에 계절효과가 존재할 때 성공적인 수요예측을 위해 Winters 방법과 같은 다양한 통계적 방법이 존재지만 신상품과 같이 과거 매출자료가 충분하지 않을 경우 통계적 방법 적용에 한계가 존재한다. 본 연구논문은 신제품과 같이 과거 매출자료가 충분하지 않아 계절효과 등을 추정하기 어려울 때 누적자료를 활용한 통계적 예측방법을 제안한다. 제안된 통계적 방법은 회귀모형이론에 기초하고 있으며 이 방법의 유효성을 최근 화장품 매출자료를 이용하여 검증하였다.

• 한국태양에너지학회 논문집
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• 제27권2호
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• pp.11-17
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• 2007

This study evaluates the seasonal performances of the horizontal-type geothermal heat exchanger(HGHEX) installed into the foundation slabs of the complex building located at Seoul. The geothermal system is consisted with totally 31,860m long HGHEX, 16 GSHPs (Ground-source Heat Pump) and 8 circulation pumps. This system supplies cooling and heating to the lobby(F1) and the common spaces(BF1). The average heat exchange temperature differences are $2.7^{\circ}C\;and\;2.5^{\circ}C$ in the summer, $1.5^{\circ}C\;and\;0.5^{\circ}C$ in the winter for the F1 and BF1 respectively. From these results, approximately 400Gcal and 180Gcal of geothermal energy are assumed to have been used during the summer and winter seasons respectively. As a conclusion, the geothermal system is reviewed as a effective utility for heating and cooling at the point of seasonal performances.

• 한국멀티미디어학회논문지
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• 제25권1호
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• pp.82-91
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• 2022

In this paper, time series prediction method of photovoltaic power is introduced using seasonal autoregressive integrated moving average (SARIMA). In order to obtain the best fitting model by a time series method in the absence of an environmental sensor, this research was used data below 50% of cloud cover. Three samples were extracted by time intervals from the raw data. After that, the best fitting models were derived from mean absolute percentage error (MAPE) with the minimum akaike information criterion (AIC) or beysian information criterion (BIC). They are SARIMA (1,0,0)(0,2,2)14, SARIMA (1,0,0)(0,2,2)28, SARIMA (2,0,3)(1,2,2)55. Generally parameter of model derived from BIC was lower than AIC. SARIMA (2,0,3)(1,2,2)55, unlike other models, was drawn by AIC. And the performance of models obtained by SARIMA was compared. MAPE value was affected by the seasonal period of the sample. It is estimated that long seasonal period samples include atmosphere irregularity. Consequently using 1 hour or 30 minutes interval sample is able to be helpful for prediction accuracy improvement.

• International Journal of Industrial Entomology and Biomaterials
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• 제9권2호
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• pp.193-198
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• 2004

Seasonal effects of the newly evolved bivoltine hybrid namely CSR$_2$${\times}$SR$_{5}$, SR$_1$ ${\times}$SR$_4$ and control hybrid KA${\times}$NB$_4$D$_2$ along with their parents SR$_1$, SR$_4$, SR$_{5}$, CSR$_2$ KA and NB$_4$D$_2$ were evaluated during different seasons of the year to understand genotype and environment interactions. Data were collected on five economic importance namely, pupation rate, cocoon yield, cocoon weight, cocoon shell ratio and filament length of the lines, hybrids and the control breeds/hybrid in three different seasons i.e., Pre-Monsoon, Monsoon and post-monsoon and subjected to relevant statistical methods. Seasonal performance of CSR$_2$, SR$_1$, SR$_4$ and SR$_{5}$ revealed superiority over control breeds KA and NB$_4$D$_2$. Both the hybrids i.e., CSR$_2$${\times}$SR$_{5}$ and SR$_1$${\times}$SR$_4$ performed well under diversified environmental conditions of tropical climate in a year indicating overall stability. These hybrids revealed highly significant (P < 0.01) variations for majority of the traits studied over the control hybrid KA${\times}$NB$_4$D$_2$.$.

• 한국산림과학회지
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• 제100권4호
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• pp.650-663
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• 2011

본 연구는 국립수목원 이용객을 대상으로 계절별 서비스 질 만족도를 조사함으로써, IPA를 활용하여 수목원 운영시 고려해야 할 서비스항목을 도출하고 국립수목원 서비스의 질적 수준을 향상시킬 수 있는 방안을 제시하고자 실시하였다. 봄, 여름, 가을에 방문하는 수목원 이용객을 대상으로 5개 부문의 24개 서비스 질 항목에 대해 방문 전 중요도와 방문 후 만족도를 7점 리커트(likert)척도를 사용하여 설문조사하였다. 그 결과, 계절별 서비스 질에 대한 중요도와 만족도는 모든 계절에서 평균 5점 이상의 긍정적인 수준을 보였으며, 특히 여름에 서비스 질에 대한 중요도와 만족도가 모두 높게 나타났다. 또한 중요도-만족도분석을 통해, 여름의 가시성 부문 항목들이 중점개선영역에 주로 나타남에 따라, 서비스 부문 간에 중요도-만족도에서 계절별 차이가 있음을 확인하였다.

• 대기
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• 제33권5호
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• pp.531-548
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• 2023

We developed the Aviation Convective Index (ACI) for predicting deep convective area using the operational global Numerical Weather Prediction model of the Korea Meteorological Administration. Seasonally optimized ACI (ACI_SnOpt) was developed to consider seasonal variabilities on deep convections in Korea. Yearly optimized ACI (ACI_YrOpt) in Part 1 showed that seasonally averaged values of Area Under the ROC Curve (AUC) and True Skill Statistics (TSS) were decreased by 0.420% and 5.797%, respectively, due to the significant degradation in winter season. In Part 2, we developed new membership function (MF) and weight combination of input variables in the ACI algorithm, which were optimized in each season. Finally, the seasonally optimized ACI (ACI_SnOpt) showed better performance skills with the significant improvements in AUC and TSS by 0.983% and 25.641% respectively, compared with those from the ACI_YrOpt. To confirm the improvements in new algorithm, we also conducted two case studies in winter and spring with observed Convectively-Induced Turbulence (CIT) events from the aircraft data. In these cases, the ACI_SnOpt predicted a better spatial distribution and intensity of deep convection. Enhancements in the forecast fields from the ACI_YrOpt to ACI_SnOpt in the selected cases explained well the changes in overall performance skills of the probability of detection for both "yes" and "no" occurrences of deep convection during 1-yr period of the data. These results imply that the ACI forecast should be optimized seasonally to take into account the variabilities in the background conditions for deep convections in Korea.

• 전력전자학회논문지
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• 제27권4호
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• pp.316-324
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• 2022

This paper uses seasonal auto-regressive integrated moving average (S-ARIMA), which is efficient in seasonality between time-series models, to predict the degradation tendency for lithium-ion batteries and study a method for improving the predictive performance. The proposed method analyzes the degradation tendency and extracted factors through an electrical characteristic experiment of lithium-ion batteries, and verifies whether time-series data are suitable for the S-ARIMA model through several statistical analysis techniques. Finally, prediction of battery aging is performed through S-ARIMA, and performance of the model is verified through error comparison of predictions through mean absolute error.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.377-379
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• 2023

Concept drift detection on data stream is the major issue to maintain the performance of the machine learning model. Since the online stream is to be a function of time, the classical statistic methods are hard to apply. In particular case of seasonal time series, a novel window strategy with Fourier analysis however, gives a chance to adapt the classical methods on the series. We explore the KS-test for an adaptation of the periodic time series and show that this strategy handles a complicate time series as an ordinary tabular dataset. We verify that the detection with the strategy takes the second place in time delay and shows the best performance in false alarm rate and detection accuracy comparing to that of arbitrary window sizes.