• Journal of the Architectural Institute of Korea Structure & Construction
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• v.36 no.3
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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.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.36 no.8
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• pp.803-809
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• 2012

It is necessary to predict the heating load in order to determine the optimal scheduling control of district heating systems. Heating loads are affected by many complex parameters, and therefore, it is necessary to develop an efficient, flexible, and easy to use prediction method for the heating load. In this study, simple specifications included in a building design document and the estimated temperature and humidity are used to predict the heating load on the next day. To validate the performance of the proposed method, heating load data measured from a benchmark district heating system are compared with the predicted results. The predicted outdoor temperature and humidity show a variation trend that agrees with the measured data. The predicted heating loads show good agreement with the measured hourly, daily, and monthly loads. During the heating period, the monthly load error was estimated to be 4.68%.

• The Plant Pathology Journal
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• v.38 no.4
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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.

• Journal of Environmental Science International
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• v.9 no.6
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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.

• Journal of Environmental Science International
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• v.21 no.1
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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.

• Journal of the Korean Institute of Gas
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• v.16 no.1
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• pp.33-38
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• 2012

Analysis on gas accidents by types occurred has been made to prevent the recurrence of accidents, through analysis of past history of gas accident occurring environment. The number of gas accidents has been decreasing, but still accidents are occurring steadily. Gas-using environment and gas accidents are estimated to be closely connected since gas-using types are changing by time period, weather, etc. in terms of accident contents. As a result of analysing gas accidents by 7 meteorological elements, such as the mean temperature, the highest temperature, the lowest temperature, relative humidity, the amount of clouds, precipitation and wind velocity, it has been found out that gas accidents are influenced by temperature or relative humidity, and accident occurs more frequently when the sky is clean and wind velocity is slow. Possibility of gas accidents can be provided in real time, using the proposed model made to predict gas accidents in connection with the weather forecast service. Possibility and number of gas accidents will be checked real time by connecting to the business system of Korea Gas Safety Corp., and it is considered that it would be positively used for preventing gas accidents.

• International conference on construction engineering and project management
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• 2022.06a
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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.

• Journal of Korea Multimedia Society
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• v.19 no.8
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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.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.2
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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.

• Journal of Biosystems Engineering
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• v.15 no.1
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• pp.44-51
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• 1990

Four models in current use for cereal grain drying, equilibrium model, Morey model, partial differential equation model and simplified partial differential equation model, were modified to be suitable for natural air drying of barley. The predicted by the four models and experimental results were compared. Three models except equilibrium model predicted moisture comtent and grain temperature very well. But equilibrium model overpredicted moisture content and grain temperature of bottom layer. The degree of prediction of the four models for relative humidities of exhaust air didn't differ much from one another and equally the four models predicted relative humidity statisfatorily. Morey model took much shorter computing time than any other models. Therefore, considering the degree of prediction and computing time Morey model was the most suitable for natural air drying of barley.