• Journal of Advanced Marine Engineering and Technology
• /
• v.35 no.3
• /
• pp.335-340
• /
• 2011

Building energy saving is one of the most important issues in these days. Control algorithm for energy saving should be designed properly to reduce power consumption in building. Recently, building energy system consists of hybrid energy system coupling with RE (Renewable Energy) source. In this paper, an optimum control algorithm for building energy saving is applied to BEMS (Building Energy Management System) by using an outdoor air temperature prediction strategy. BEMS coupling with renewable energy can control HVAC (Heating, Ventilating and Air-Conditioning) system effectively. In order to verify the effectiveness of building energy saving, BEMS was tested for several months at a laboratorial chamber with an air conditioner, fan and heater. To this end BEMS embedded control algorithm has been tested successfully.

• KIEAE Journal
• /
• v.16 no.3
• /
• pp.89-94
• /
• 2016

Purpose: This study aimed at developing an artificial neural network (ANN) model to predict the optimal start moment of the setback temperature during the normal occupied period of a building. Method: For achieving this objective, three major steps were conducted: the development of an initial ANN model, optimization of the initial model, and performance tests of the optimized model. The development and performance testing of the ANN model were conducted through numerical simulation methods using transient systems simulation (TRNSYS) and matrix laboratory (MATLAB) software. Result: The results analysis in the development and test processes revealed that the indoor temperature, outdoor temperature, and temperature difference from the setback temperature presented strong relationship with the optimal start moment of the setback temperature; thus, these variables were used as input neurons in the ANN model. The optimal values for the number of hidden layers, number of hidden neurons, learning rate, and moment were found to be 4, 9, 0.6, and 0.9, respectively, and these values were applied to the optimized ANN model. The optimized model proved its prediction accuracy with the very storing statistical correlation between the predicted values from the ANN model and the simulated values in the TRNSYS model. Thus, the optimized model showed its potential to be applied in the control algorithm.

• Journal of the Architectural Institute of Korea Structure & Construction
• /
• v.34 no.5
• /
• pp.51-58
• /
• 2018

The purpose of this study was to analyze the effect of seasonal change in characteristics of hygiene activity on domestic hot water energy consumption. With 16 residents of 4 households, the data about frequency of hygiene activity and water temperature was collected from February to August, 2017. The results of collected data discovered that the frequency of hygiene activity was higher especially in summer, whereas the consumption of warm water they used was higher in winter. The seasonal change in characteristics of hygiene activity was analyzed to be changed and strongly influenced by outdoor temperature. The influence of characteristics of hygiene activity on hot water consumption was analyzed. There was 13% of difference between consumption that was calculated taking characteristics of hygiene activity into account and consumption that was not. Therefore, this study suggested hygiene activity schedule, hot water profile and hot water consumption pattern, which can be utilized for improving simulation as well.

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

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.347-350
• /
• 2018

In order for an IoT system to automatically make the house temperature pleasant for the user, the system needs to predict the optimal start-up time of air-conditioner or heater to get to the temperature that the user has set. Predicting the optimal start-up time is important because it prevents extra fee from the unnecessary operation of the air-conditioner and heater. This paper introduces an ANN(Artificial Neural Network) and an IoT system that predicts the cooling and heating time in households using air-conditioner and heater. Many variables such as house structure, house size, and external weather condition affect the cooling and heating. Out of the many variables, measurable variables such as house temperature, house humidity, outdoor temperature, outdoor humidity, wind speed, wind direction, and wind chill was used to create training data for constructing the model. After constructing the ANN model, an IoT system that uses the model was developed. The IoT system comprises of a main system powered by Raspberry Pi 3 and a mobile application powered by Android. The mobile's GPS sensor and an developed feature used to predict user's return.

• Journal of Korean Association for Spatial Structures
• /
• v.23 no.3
• /
• pp.95-103
• /
• 2023

With the increasing number of aging buildings across Korea, emerging maintenance technologies have surged. One such technology is the non-contact detection of concrete cracks via thermal images. This study aims to develop a technique that can accurately predict the depth of a crack by analyzing the temperature difference between the crack part and the normal part in the thermal image of the concrete. The research obtained temperature data through thermal imaging experiments and constructed a big data set including outdoor variables such as air temperature, illumination, and humidity that can influence temperature differences. Based on the collected data, the team designed an algorithm for learning and predicting the crack depth using machine learning. Initially, standardized crack specimens were used in experiments, and the big data was updated by specimens similar to actual cracks. Finally, a crack depth prediction technology was implemented using five regression analysis algorithms for approximately 24,000 data points. To confirm the practicality of the development technique, crack simulators with various shapes were added to the study.

• Journal of the Korean Solar Energy Society
• /
• v.32 no.3
• /
• pp.42-49
• /
• 2012

신경망방법은 공학, 경영 그리고 정보기술과 같이 다양한 분양에서 널리 사용되어지고 있다. 신경망방법은 기본적으로 예측, 제어, 식별과 같은 기능을 가지고 있는데, 본 논문에서는 신경망방법을 이용하여 C사의 모델 T의 히트펌프 전기부하를 예측하였다. 부하예측은 시스템을 더욱 효율적이고, 적절하게 만들기 위해 필요하다. 본 논문에서 사용된 히트펌프는 지열원 히트 펌프 시스템이다. 이 지열 히트 펌프의 부하는 사전에 미리 예측되어진 외기온도 및 건물 열부하에 따라 측정 학습된 전력 소비량으로 겨울에는 난방, 여름에는 냉방에 대한 전력 부하를 예측할 수 있다. 이 신경망방법은 신경망 학습 순서를 통해 부하 예측을 위해 히트펌프의 성능데이터를 필요로 한다. 이 부하 예측 인공지능망 방법으로 외기 온도별 건물 통합형 지열 히트 펌프 부하가 예측되어질 수 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.10a
• /
• pp.893-895
• /
• 2016

Data center has a lot of management efforts for the facility, energy, and efficient usage monitoring. Data center power management is important to make the data center have reliable service and cost-effective business. In this paper, IoT based energy measurements monitoring which gives support to energy consumption analysis including indoor, outdoor temperature condition. This converged information for energy analysis gives various aspects of energy consumption effects. With IoT big data, energy machine learning system can give the relation of energy components and measurements, it is the key information of the quick energy analysis in the just one month data trend for the prediction and estimation.

• Journal of the Institute of Convergence Signal Processing
• /
• v.14 no.2
• /
• pp.99-104
• /
• 2013

Replacement of sensor nodes for monitoring a wide range area such as mountains and forests needs a lot of time and cost. Using new and renewable energy around them can maximize the lifetime of wireless sensor networks, in which solar energy is infinite energy source that is available in 365 days. To design these sensor networks, solar energy model is essential and to estimate and analyze the overall photovoltaic energy. Using this, we can figure out important data such as the size and performance of solar panel needed. However, existing researches for solar energy harvesting consider parts of many factors to influence the quantity of solar energy gathered. In this paper, we suggest advanced solar energy harvesting model considering angular loss (solar cell panel), overheat loss (solar cell), rechargeable battery heat and cooling for each monthly properties. From our experimental results according to outdoor temperature, panel angle and the surface temperature of solar panel, we show these impact factors are correctly configured.

• Journal of Conservation Science
• /
• v.33 no.6
• /
• pp.467-483
• /
• 2017

In this study, the degradation properties by temperature stress of $Araldite^{(R)}$ rapid-curing epoxy resin used for inorganic cultural heritages, was identified. The tensile and tensile shear strength of durability decreased for 12,624 hours at temperatures of $40{\sim}60^{\circ}C$. In terms of stability of external stress and temperature, the slow-curing epoxy was superior to the rapid-curing epoxy, and cultural heritage conservation plans should therefore consider the strength and stress properties of restoration materials. Color differences increased for 12,624 hours at temperatures of $40{\sim}60^{\circ}C$, and glossiness decreased. Both color and gloss stability were weak, which necessitates the improvement of optical properties. Thermal properties (weight loss, decomposition temperature, and glass transition temperature) of adhesives are linked to mechanical properties. Interfacial properties of the adherend and water vapor transmission rates of adhesives are linked to performance variation. For porous media (ceramics, brick, and stone), isothermal and isohumid environments are important. For outdoor artifacts on display in museums, changes in physical properties by exposure to varying environmental conditions need to be minimized. These results can be used as baseline data in the study of the degradation velocity and lifetime prediction of rapid-curing epoxy resin for the restoration of cultural heritages.