• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.5
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• pp.394-402
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• 2007

To estimate the cooling load for the following day, outdoor temperature and humidity are needed in hourly base. But the meteorological administration forecasts only maximum and minimum temperature. New methodology is proposed for predicting hourly outdoor temperature and humidity by using the forecasted maximum and minimum temperature. The correlations for normalized outdoor temperature and specific humidity has been derived from the weather data for five years from 2001 to 2005 at Seoul, Daejeon and Pusan. The correlations for normalized temperature are independent of date, while the correlations for specific humidity are linearly dependent on date. The predicted results show fairly good agreement with the measured data. The prediction program is also developed for hourly outdoor dry bulb temperature, specific humidity, dew point, relative humidity, enthalpy and specific volume.

• Journal of the Korean Society of Industry Convergence
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• v.13 no.3
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• pp.169-181
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• 2010

This paper presents a feasibility study of a fresh air load reduction system by using an underground double floor space. The fresh air is introduced into the double slab space and passes through the opening bored into the footing beam. The air is cooled by the heat exchange with the inside surface of the double slab space in summer, and heated in winter. This system not only reduces sensible heat load of the fresh air by heat exchange with earth but also reduces latent heat load of the fresh air by ad/de-sorption of underground double slab concrete. In this paper, we proposed a simplified presumption method for the prediction of cooling and heating performance in the system. In conclusion the proposed method has been verified by comparing with the calculated value of the numerical analysis model by using nonlinear two-dimension hygroscopic question.

• KIEAE Journal
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• v.17 no.4
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• pp.83-88
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• 2017

Purpose: The objective of this study is to develop a predictive model for calculating the amount of cooling load for the different setback temperatures during the setback period. An artificial neural network (ANN) is applied as a predictive model. The predictive model is designed to be employed in the control algorithm, in which the amount of cooling load for the different setback temperature is compared and works as a determinant for finding the most energy-efficient optimal setback temperature. Method: Three major steps were conducted for proposing the ANN-based predictive model - i) initial model development, ii) model optimization, and iii) performance evaluation. Result:The proposed model proved its prediction accuracy with the lower coefficient of variation of the root mean square errors (CVRMSEs) of the simulated results (Mi) and the predicted results (Si) under generally accepted levels. In conclusion, the ANN model presented its applicability to the thermal control algorithm for setting up the most energy-efficient setback temperature.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.14 no.5
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• pp.377-384
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• 2002

For economic evaluation of cooling plant equipments, it is necessary to simplify energy Prediction method, which should includes efficiency corrected by part-load ratio. This study proposed simplified method with regression equations of time-average partial loads and refrigerator capacity. DOE-2 Program was used to carry out a parametric study of twelve design variables. Five input variables were considered to be significant and were used in the regression equations. To test accuracy of simplified method, calculated results were compared with DOE-2 simulated results. Test result showes a good agreement with the simulation result with an error of 5.9∼7.6%. It is expected that this method can be used as an easy prediction tool for comparing energy use of different cooling plants during the early design stage.

• Proceedings of the SAREK Conference
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• 2006.06a
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• pp.789-794
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• 2006

The potential impacts of climate change on heating and cooling energy demand were investigated by means of transient building energy simulations and hourly weather data scenarios for Inchon. Future trends for the 21 st century was assessed based oil climate change scenarios with 7 global climate models(GCMs), We constructed hourly weather data from monthly temperatures and total incident solar radiation ($W/m^2$) and then simulated heating and cooling load by Trnsys 16 for Inchon. For 2004-2080, the selected scenarios made by IPCC foresaw a $3.7-5.8^{\circ}C$rise in mean annual air temperature. In 2004-2080, the annual cooling load for a apartment with internal heat gains increased by 75-165% while the heating load fell by 52-71%. Our analysis showed widely varying shifts in future energy demand depending on the season. Heating costs will significantly decrease whereas more expensive electrical energy will be needed of air conditioning during the summer.

• Journal of the Korean Solar Energy Society
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• v.21 no.4
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• pp.21-28
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• 2001

It is a time since sustainable architecture become a main issue of design concept in 21C. However, it is necessary to develope the tool estimating energy loads and uses in our architectural conditions for energy saving design. This study aims to develope the E-Load program to predict heat and cooling loads of houses. The program is developed by modified bin methods derived from ASHRAE TC 4.7. It consists of 4 divisions such as files, data inputs, energy load estimations and output options. The main processes of energy load estimations are based on ASHRAE fundamentals. The developed E-Load program is a easy and valid tool to predict heat and cooling loads of buildings.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.3
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• pp.113-124
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• 2012

Reliable estimation of energy load inside the greenhouse and the selection of cooling and heating facilities are very important preceding factors to save energy as well as initial and maintenance costs of operating a greenhouse. Recently, building energy simulation (BES) technique to simulate a model similar to the actual conditions through a variety of dynamic simulation methods, and predict and analyze the flow of energy is being actively introduced and developed. As a fundamental research to apply the BES technique which is mainly used for analysis of general buildings, to greenhouse, this research designed four types of naturally-ventilated greenhouses using one of commercial programs, TRNSYS, and then compared and analyzed their energy load properties, by applying meteorological data collected from six regions in Korea. When comparing the greenhouse load of each region depending on latitude and topographical characteristics through simulation, Chuncheon had nearly 9~49 % higher heating load per year than other regions, but its annual cooling load was the reverse to it. Except for Jeju, 1-2W type greenhouses in five regions showed about 17 % higher heating load than a widespan type greenhouse, and 1-2W type greenhouses in Chuncheon, Suwon, Cheongju, Daegu, Cheonju and Jeju had 23 %, 20 %, 17 %, 16 %, 18 % and 20 % higher cooling load respectively than a wide span-type one. Glasshouse and vinyl greenhouse showed 8~11 % and 10~12 % differences respectively in heating load, while 2~10 % and 7~10 % differences in cooling load respectively.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.22 no.9
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• pp.628-634
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• 2010

This paper presents a feasibility study of a fresh air load reduction system by using an underground double floor space. The fresh air is introduced into the double slab space and passes through the opening bored into the footing beam. The air is cooled by the heat exchange with the inside surface of the double slab space in summer, and heated in winter. This system not only reduces sensible heat load of the fresh air by heat exchange with earth but also reduces latent heat load of the fresh air by ad/de-sorption of underground double slab concrete. In this paper, we proposed a simplified presumption method for the prediction of cooling and heating performance in the system. In conclusion the proposed method has been verified by comparing with the calculated value of the numerical analysis model by using nonlinear two-dimension hygroscopic question.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.3
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• pp.205-212
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• 2008

This paper describes the application of an inverse building model to a calibrated forward building model using EnergyPlus program. Typically, inverse models are trained using measured data. However, in this study, an inverse building model was trained using data generated by an EnergyPlus model for an actual office building. The EnergyPlus model was calibrated using field data for the building. A training data set for a month of July was generated from the EnergyPlus model to train the inverse model. Cooling load prediction of the trained inverse model was tested using another data set from the EnergyPlus model for a month of August. Predicted cooling loads showed good agreement with cooling loads from the EnergyPlus model with root-mean square errors of 4.11%. In addition, different control strategies with dynamic cooling setpoint variation were simulated using the inverse model. Peak cooling loads and daily cooling loads were compared for the dynamic simulation.

• Smart Structures and Systems
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• v.29 no.5
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• pp.677-691
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• 2022

Water cycle algorithm (WCA) has been a very effective optimization technique for complex engineering problems. This study employs the WCA for simultaneous prediction of heating load (L_H) and cooling load (L_C) in residential buildings. This algorithm is responsible for optimally tuning a neural network (NN). Utilizing 614 records, the behavior of the L_H and L_C is explored and the captured knowledge is then used to predict for 154 unanalyzed building conditions. Since the WCA is a population-based algorithm, different numbers of the searching agents were tested to find the most optimum configuration. It was observed that the best solution is discovered by 500 agents. A comparison with five newly-developed benchmark optimizers, namely equilibrium optimizer (EO), multi-tracker optimization algorithm (MTOA), slime mould algorithm (SMA), multi-verse optimizer (MVO), and electromagnetic field optimization (EFO) revealed that the WCANN predicts the desired parameters with considerably larger accuracy. Obtained root mean square errors (1.4866, 2.1296, 2.8279, 2.5727, 2.5337, and 2.3029 for the L_H and 2.1767, 2.6459, 3.1821, 2.9732, 2.9616, and 2.6890 for the L_C) indicated that the most reliable prediction was presented by the proposed model. The EFONN, however, provided a more time-effective solution. Lastly, an explicit predictive formula was elicited from the WCANN.