• International Journal of Air-Conditioning and Refrigeration
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• v.6
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• pp.1-13
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• 1998

The basic heat transfer process that occurs in a building can best be illustrated by an electrical circuit network. Present paper reports the dynamic simulation of annual energy consumption in an office building by the thermal resistance capacitance network method. Unsteady thermal behaviors and annual energy consumption in an office building were examined in detail by solving the simultaneous circuit equations of thermal network. The results are used to evaluate the accuracy of the modified BIN method for the energy consumption analysis of a large building. Present thermal resistance-capacitance method predicts annual energy consumption of an office building with the same accuracy as that of response factor method. However, the modified BIN method gives 15% lower annual heating load and 25% lower cooling load than those from the present method. Equipment annual energy consumptions for fan, boiler and chiller in the HVAC system are also calculated for various control systems as CAV, VAV, FCU+VAV and FCU+CAV. FCU+CAV system appears to consume minimum annual energy among them.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.3
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• pp.187-197
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• 2004

This paper presents a window-based load management system (LMS) developed as a decision-making tool in the competitive electricity market The developed LMS can help the users to monitor system load patterns, analyze their past energy consumption and schedule for the future energy consumption. The LMS can also provide the effective information on real-time energy/cost monitoring, consumed energy/cost analysis, demand schedule and cost-savings. Therefore. this LMS can be used to plan the optimal demand schedule and consumption strategy.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2014.05a
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• pp.292-293
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• 2014

Many project (such ESCO or BRP) are being implemented for existing building energy saving. Most of medium or large building that use many energy being implemented this project. In the case of large or medium building must implement obligatorily Energy auditing. Therefore, They can be recognize their building energy consumption situation. But, In the case of small building don't need obligatorily energy auditing. Therefore, They can't be recognize their building energy consumption situation. As a result, Small buildings are difficult to participate in energy efficiency retrofit. In this research, Building owners of buildings energy load recognition and energy efficiency retrofit possible participation was analysis though survey. Survey results, Most building owners don't know building energy load. But they have a good mind to retrofit building energy efficiency. As a result, If they have energy load information, they will be participate energy efficiency retrofit.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.27 no.11
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• pp.567-573
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• 2015

Commercial buildings account for significant portions of the total building energy in Korea, and thus, a variety of research on chiller operation has been carried out. However, most of the studies were carried out on the chiller itself, i.e., the part load ratio characteristics and the corresponding electricity energy consumption patterns were not analyzed in existing studies. In this study, the part load ratio and the operating characteristics of the vapor compression chiller were analyzed within an office building equipped with the conventional variable air volume system. As a result, significant portions of total operating hours, cooling load, and energy consumption turned out to be in the part load ratio range of 0 through 50%. Thus, energy consumption was significantly affected by the chiller COP at low part load conditions, indicating that chiller operation at the part load is an important factor in commercial buildings.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.9
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• pp.1658-1662
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• 2010

This paper presents an advanced energy saving algorithm in building. It is important to aggregate a various demand side resource which is surely controllable at the peak power time to reduce the energy cost. Previous demand side algorithm appropriate for building is based on peak power. In this paper, we develop the new energy saving algorithm to reduce the quantity of power consumption. The simulation results show that the proposed tem is very effective.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.633-636
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• 2022

The reduction of energy consumption at the base station (BS) has become more important recently. In this paper, we consider the adaptive muting of the antennas based on the predicted future traffic load to reduce the energy consumption where the number of active antennas is adaptively adjusted according to the predicted future traffic load. Given that traffic load is sequential data, three different RNN structures, namely long-short term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) are considered for the future traffic load prediction. Through the performance evaluation based on the actual traffic load collected from the Afghanistan telecom company, we confirm that the traffic load can be estimated accurately and the overall power consumption can also be reduced significantly using the antenna musing.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.19 no.2
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• pp.67-81
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• 1990

The objective of this is to develop a practical software package to calculate annual energy consumption of HVAC (Heating Ventilating, and Air Conditioning) System in a building. It can quickly estimate loads and energy consumption, and have a function of economic analysis through the estimation of operating cost. Techniques of save energy consumption used in a building are necessary from the stage of design process to operation. The single most significant task is on HVAC Systems. Their installation costs, and related operating costs have enormous influence upon initial and maintenance costs. HVAC designers and engineers now have a wide variety of software choices available, but only a few of them have been developed in this country and no source program has been disclosed. Neither load culculation nor estimation of energy consumption is systematically made by the domestic HVAC design firms. Even though computer improved over the years with a trend of large scale load calculation and system selection through simulaion, the utilization of software nowadays does not make good progress due to lack of working environment. Therefore, it is necessary to develop a practical software package with which load calculation can be made with ease and kind manner. This study concerns the development of a software package which makes it possible to design HVAC system and save energy consumption in operation. The algorithm used in this program is a Modified Bin Method widely known as a simplified energy analysis means.

• Journal of the Korean Solar Energy Society
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• v.30 no.6
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• pp.89-94
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• 2010

A large portion of the energy cost of a building is cooling and heating to maintain a comfortable indoor environment. Air conditioning is now one of the important parts in the building design, as increase in energy consumption and pollutant emission in energy conversion process. In this study, elements that affects the energy consumption of model building are identified and the perfomance analysis of the alternative a Low Energy Cooling Systems considering characteristics of model building and energy saving performance is analyzed. In this study, elements that affect the energy consumption of office building are identified and energy saving performance of the alternative air conditioning system is analyzed. As a result, applied to earn and suggest basic data for energy saving measures. In this study, EnergyPlus simulation program was used to evaluate the energy load when alternative Low Energy Cooling Systems are applied to the model building. The reliability of simulation program is verified by comparing actual energy load from operation data of building management office and predicted energy load using simulation program. For Low Energy Cooling System application which considers the purpose and characteristics of the building, reasonable and energy-saving air conditioning method obtained by analyzing energy consumption elements for each expected air conditioning methods is used to deduct result of this study.

• International journal of advanced smart convergence
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• v.4 no.1
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• pp.45-53
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• 2015

This paper is proposed mathematical load modelling based on system identification approach of energy consumption of residential air conditioning. Due to air conditioning is one of the significant equipment which consumes high energy and cause the peak load of power system especially in the summer time. The demand response is one of the solutions to decrease the load consumption and cutting peak load to avoid the reservation of power supply from power plant. In order to operate this solution, mathematical modelling of air conditioning which explains the behaviour is essential tool. The four type of linear model is selected for explanation the behaviour of this system. In order to obtain model, the experimental setup are performed by collecting input and output data every minute of 9,385 BTU/h air-conditioning split type with $25^{\circ}C$ thermostat setting of one sample house. The input data are composed of solar radiation ($W/m^2$) and ambient temperature ($^{\circ}C$). The output data are power and energy consumption of air conditioning. Both data are divided into two groups follow as training data and validation data for getting the exact model. The model is also verified with the other similar type of air condition by feed solar radiation and ambient temperature input data and compare the output energy consumption data. The best model in term of accuracy and model order is output error model with 70.78% accuracy and $17^{th}$ order. The model order reduction technique is used to reduce order of model to seven order for less complexity, then Kalman filtering technique is applied for remove white Gaussian noise for improve accuracy of model to be 72.66%. The obtained model can be also used for electrical load forecasting and designs the optimal size of renewable energy such photovoltaic system for supply the air conditioning.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.3
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• pp.90-96
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• 2004

The objective of this study was to develop a model for safe work load based on a physiological model of metabolic energy of manual material handling tasks. Fifteen male subjects voluntarily participated in this study. Lifting activities with four different weights, 0, 8, 16, 24kg, and four different working frequencies (2, 5, 8, 11 lifts/min) for a lifting range from floor to the knuckle height of 76cm were considered. Oxygen consumption rates and heart rates were measured during the performance of sixteen different lifting activities. Simplified predictive equations for estimating the oxygen consumption rate and the heart rate were developed. The oxygen consumption rate and the heart rate could be expressed as a function of task variables; frequency and the weight of the load, and a personal variable, body weight, and their interactions. The coefficients of determination ($r^2$) of the model were 0.9777 and 0.9784, respectively, for the oxygen consumption rate and the heart rate. The model of oxygen consumption rate was modified to estimate the work load for the given oxygen consumption rate. The overall absolute percent errors of the validation of this equation for work load with the original data set was 39.03%. The overall absolute percent errors were much larger than this for the two models based on the US population. The models for the oxygen consumption rate and for the work load developed in this study work better than the two models based on the US population. However, without considering the biomechanical approach, the developed model for the work load and the two US models are not recommended to estimate the work loads for low frequent lifting activities.