• KIEAE Journal
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• 제17권5호
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• pp.69-76
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• 2017

Purpose: This study aimed at developing an Artificial Neural Network (ANN) model for predicting the amount of cooling energy consumption of the variable refrigerant flow (VRF) cooling system by the different set-points of the control variables, such as supply air temperature of air handling unit (AHU), condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. Applying the predicted results for the different set-points, the control algorithm, which embedded the ANN model, will determine the most energy efficient control strategy. Method: The ANN model was developed and tested its prediction accuracy by using matrix laboratory (MATLAB) and its neural network toolbox. The field data sets were collected for the model training and performance evaluation. For completing the prediction model, three major steps were conducted - i) initial model development including input variable selection, ii) model optimization, and iii) performance evaluation. Result: Eight meaningful input variables were selected in the initial model development such as outdoor temperature, outdoor humidity, indoor temperature, cooling load of the previous cycle, supply air temperature of AHU, condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. The initial model was optimized to have 2 hidden layers with 15 hidden neurons each, 0.3 learning rate, and 0.3 momentum. The optimized model proved its prediction accuracy with stable prediction results.

• 대한건축학회논문집:구조계
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• 제36권3호
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• pp.169-176
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• 2020

The authors developed a nodal network model that simulates the flow of moist air and the thermal behavior of a target area. The nodal network model was enhanced using a parameter estimation technique based on the measured temperature, humidity, and schedule data. However, the nodal model is not good enough for predicting humidity of the target space, having 55.6% of CVRMSE. It is because re-evaporation effect could not be modeled due to uncertain factors in the field measurement. Hence, a data-driven model was introduced using an artificial neural network (ANN). It was found that the data-driven model is suitable for predicting the condensation compared to the nodal model satisfying ASHRAE Guideline with 3.36% of CVRMSE for temprature, relative humidity, and surface temperature on average. The model will be embedded in automated devices for real-time predictive control, to minimize the risk of surface condensation at dressroom in an apartment housing.

• 대한기계학회논문집B
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• 제36권8호
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• pp.803-809
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• 2012

지역난방 시스템의 최적 스케쥴 제어를 위해서는 난방부하 예측이 필요하다. 공동주택의 난방부하는 복잡한 변수들의 영향을 받기 때문에 손쉬운 난방부하 예측을 위해 사용하기 쉬우며 효용성 있는 예측방법의 개발이 필요하다. 본 연구에서는 익일의 시간별 난방부하를 예측하기 위해 단순화된 외기조건 예측방법과 부하 예측방법을 제안하였다. 난방부하 예측을 위해 건물설계서에서 쉽게 얻을 수 있는 간단한 사양과 예측된 온습도가 사용되었다. 제안된 방법의 타당성을 검증하기 위해 지역난방 시스템으로부터 시간별로 실측된 난방부하와 예측된 결과를 비교하였다. 예측된 외기조건은 실측된 값과 비교하여 변화양상이 잘 일치하였다. 예측된 난방부하와 측정된 난방부하를 비교한 결과, 시간별, 일별, 월별 모두 예측과 실측이 비교적 잘 일치하였으며, 난방기간 동안 월별 부하의 평균 오차는 약 4.68%로 비교적 작은 값을 가졌다.

• The Plant Pathology Journal
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• 제38권4호
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• 한국환경과학회지
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• 제9권6호
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• pp.469-474
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• 2000

The traffic accidents in large cities such as Pusan metropolitan city have been increased every year due to increasing of vehicles numbers as well as the gravitation of the population. In addition to the carelessness of drivers, many meteorological factors have a great influence on the traffic accidents. Especially, the number of traffic accidents is governed by precipitation, visibility, cloud amounts temperature, etc. In this study, we have analyzed various data of meteorological factors from 1992 to 1997 and determined the standardized values for contributing to each traffic accident. Using the relationship between meteorological factors(visibility, precipitation, relative humidity and cloud amounts) and the total automobile mishaps, and experimental prediction formula for their traffic accident rates was seasonally obtained at Pusan city in 1997. Therefore, these prediction formulas at each meteorological factor may by used to predict the seasonal traffic accident numbers and contributed to estimate the variation of its value according to the weather condition it Pusan city.

• 한국환경과학회지
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• 제21권1호
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• pp.69-81
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• 2012

This study forecasts changes in thermal environment and microclimate change per new building construction and assignment of green space in urban area using Computational Fluid Dynamics(CFD) simulation. The analysis studies temperature, humidity and wind speed changes in 4 different given conditions that each reflects; 1) new building construction; 2) no new building construction; 3) green spaces; and 4) no green spaces. Daily average wind speed change is studied to be; Case 2(2.3 m/s) > Case 3. The result of daily average temperate change are; Case 3($26.5^{\circ}C$) > Case 4($24.6^{\circ}C$) > Case 2($23.9^{\circ}C$). This result depicts average of $2.5^{\circ}C$ temperature rise post new building construction, and decrease of approximately $1.8^{\circ}C$ when green space is provided. Daily average absolute humidity change is analysed to be; Case 3(15.8 g/kg') > Case 4(14.1 g/kg') > Case 2(13.5 g/kg'). This also reveals that when no green spaces is provided, 2.3 g/kg' of humidity change occurs, and when green space is provided, 0.6 g/kg change occurnd 4(1.8 m/s), which leads to a conclusion that daily average wind velocity is reduced by 0.5 m/s per new building construction in a building complex.

• 한국가스학회지
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• 제16권1호
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• pp.33-38
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• 2012

본 연구에서는 국내 가스사고의 발생 환경을 분석하여 가스사고의 재발을 방지하고자 가스 사고를 유형별로 분석하였다. 가스사고는 지속적으로 발생하고 있고, 사고의 내용에서도 시기별, 날씨 등에 따라 가스사용 형태가 변하고 있어서 가스의 사용환경과 가스사고는 밀접한 관계가 있는 것으로 나타났다. 가스사고를 평균기온, 최고기온, 최저기온, 상대습도, 운량, 강수량 및 풍속의 7가지 기상요소별로 분석해 본 결과, 기온과 상대습도 등에 따라 영향을 받고 있은 것으로 나타났으며, 맑은 날, 풍속은 낮을 때 가스사고 발생빈도가 많았다. 가스사고 예측을 위하여 제시된 모델식을 활용하여 기상청의 일기예보 시스템과 연계하여 가스사고 발생 가능성을 실시간으로 제공하고, 회사의 업무시스템과 연계시켜 실시간으로 확인이 가능하도록 하여 가스사고 예방활동에 적극 활용할 수 있을 것으로 사료된다.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.119-127
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• 2022

Most of the construction works are conducted outdoors, so the construction workers are affected by weather conditions such as temperature, humidity, and wind velocity which can be evaluated the thermal comfort as environmental factors. In our previous researches, it was found that construction accidents are usually occurred in the discomfort ranges. The safety management, therefore, should be planned in consideration of the thermal comfort and measured by a specialized simulation tool. However, it is very complex, time-consuming, and difficult to model. To address this issue, this study is aimed to develop a framework of a prediction model for improving the prediction accuracy about outdoor thermal comfort considering environmental factors using machine learning algorithms with hyperparameter tuning. This study is done in four steps: i) Establishment of database, ii) Selection of variables to develop prediction model, iii) Development of prediction model; iv) Conducting of hyperparameter tuning. The tree type algorithm is used to develop the prediction model. The results of this study are as follows. First, considering three variables related to environmental factor, the prediction accuracy was 85.74%. Second, the prediction accuracy was 86.55% when considering four environmental factors. Third, after conducting hyperparameter tuning, the prediction accuracy was increased up to 87.28%. This study has several contributions. First, using this prediction model, the thermal comfort can be calculated easily and quickly. Second, using this prediction model, the safety management can be utilized to manage the construction accident considering weather conditions.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1530-1537
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• 2016

Recently, solar power generation shows the significant growth in the renewable energy field. Using the short-term prediction, it is possible to control the electric power demand and the power generation plan of the auxiliary device. However, a short-term prediction can be used when you know the weather forecast. If it is not possible to use the weather forecast information because of disconnection of network at the island and the mountains or for security reasons, the accuracy of prediction is not good. Therefore, in this paper, we proposed a system capable of short-term prediction of solar power generation amount by using only the weather information that has been collected by oneself. We used temperature, humidity and insolation as weather information. We have applied a moving average to each information because they had a characteristic of time series. It was composed of min, max and average of each information, differences of mutual information and gradient of it. An artificial neural network, SVM and RBF Network model was used for the prediction algorithm and they were combined by Ensemble method. The results of this suggest that using a moving average during pre-processing and ensemble prediction models will maximize prediction accuracy.

• 설비공학논문집
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• 제29권2호
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• pp.43-50
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• 2017

The purpose of this study is to develop a control algorithm for outdoor air cooling based on the prediction of cooling load, and to evaluate the building energy saving using outdoor air cooling. Outdoor air conditions such as temperature, humidity, and solar insolation are predicted using forecasted information provided by the meteorological agency, and the building cooling load is predicted from the obtained outdoor air conditions and building characteristics. The air flow rate induced by outdoor air is determined by considering the predicted cooling loads. To evaluate the energy saving, the benchmark building is modeled and simulated using the TRNSYS program. Energy saving by outdoor air cooling using load prediction is found to be around 10% of the total cooling coil load in all locations of Korea. As the allowable minimum indoor temperature is decreased, the total energy saving is increased and approaches close to that of the conventional enthalpy control.