• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.9
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• pp.836-844
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• 2001

For the radiant floor heating system, the possibility of suboptimal prediction control was investigated by computer simulation and experiment. For this study, TRANSYS program was used and an experimental facility consisting of two rooms (3$\times$4.4$\times$2.8m) was built. The facility enabled simultaneous comparison of two different control strategies which implemented in a separate room. Results showed that outdoor air temperature prediction control was superior to the conventional outdoor air temperature compensation control for radiant floor heating system. However, more research for fine prediction of outside air temperature was required in the future.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.27 no.3
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• pp.137-145
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• 2015

In this study, the actual energy consumption of the secondary side of District Heating System (DHS) with different hot water supply temperature control methods are compared. Three methods are Set-point Control, Outdoor Temperature Reset Control and Outdoor Temperature Prediction Control. While Outdoor Temperature Reset Control has been widely used for energy savings of the secondary side of the system, the results show that Outdoor Temperature Prediction Control method saves more energy. In general, Outdoor Temperature Prediction Control method lowers the supply temperature of hot water, and it reduces standby losses and increases overall heat transfer value of heated spaces due to more flow into the space. During actual energy consumption monitoring, Outdoor Temperature Prediction Control method saves about 7.1% in comparison to Outdoor Temperature Reset Control method and about 15.7% in comparison to Set-point Control method. Also, it is found that at when partial load condition, such as daytime, the fluctuation of hot water supply temperature with Set-point Control is more severe than Outdoor Temperature Prediction Control. Therefore, it proves that Outdoor Temperature Prediction Control is more stable even at the partial load conditions.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.12 no.4
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• pp.8-14
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• 2016

In this study, the effects of prediction and reset control of outdoor air temperature on energy consumption for central heating system are researched by using TRNSYS program package, and the control performances with the suggested methods of prediction and reset control of outdoor air temperature are compared with the existing ones. As a result, the value of coefficient of determination $R^2$ for the predicted outdoor temperatures is improved and the suggested control method shows maximum 21.8% energy saving in comparison with existing control ones.

• 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.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.8
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• pp.682-691
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• 2001

Most of radiant floor heating systems are operated in the intermittent heating mode in Korea. The application possibility of predictive suboptimal control for Koran residential house was investigated by computer simulation and experiment. For this study, TRNSYS program was used and an experimental facility consisting of tow rooms ($3\times4.4\times2.8 m$) identical in construction was built. The facility enabled simultaneous comparison of two different control method. And real multi residential hose was investigated. Results showed that outdoor air temperature prediction control was superior to the conventional control for radiant floor heating system operated in the intermittent heating mode. New control system resulted in good thermal environment and les energy consumption.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.12 no.6
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• pp.609-615
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• 2000

The energy conservation is one of the most important issues in recent years. Especially, the energy conservation through improved control strategies is one of the most highly possible area to be implemented in the near future. The energy conservation of the ice storage system can be accomplished through the improved control strategies. A real time building load prediction algorithm was developed. The expected highest and the lowest outdoor temperature of the next day were used to estimate the next day outdoor temperature profile. The measured dry bulb temperature and the measured building load were used to estimate system parameters by using the on-line weighted recursive least square method. The estimated hourly outdoor temperatures and the estimated hourly system parameters were used to predict the next day hourly building loads. In order to see the effectiveness of the building load prediction algorithm, two different types of building models were selected and analysed. The simulation results show less than 1% in error for the prediction of the next day building loads. Therefore, this algorithm may successfully be used for the development of improved control algorithms of the ice storage system.

• KIEAE Journal
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• v.17 no.5
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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.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.28 no.2
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• pp.69-74
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• 2016

Most hot water heating valves for apartments are constant-flow types, which limit the flow rate through an individual household for even distribution of heating water to other households. The constant-flow type is implemented by an on-off control. As a result, heating water is supplied intermittently and hence, indoor air temperature also fluctuates. Returning water temperature is also high, which reduces energy efficiency. To implement continuous feedback control, the indoor temperature dynamics was simulated to fit a measured temperature history by a state-of-the-art physical model. From the model, it was found that the most important disturbance is outdoor temperature and its effect on indoor temperature lasts about an hour. To cope with the slow response and the significant disturbance, a prediction control with proportional feedback is proposed. The control was found to be successful in implementing continuous heating water flow and improved indoor temperature control.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.1300-1305
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• 2009

The energy conservation in buildings affects environmental preservation as well as economic benefits, and creates the comfortable indoor environment set for the inhabitants. Especially, apartment buildings show ever-increasing energy consumption with large-sized and high-class tendency, thus energy saving counterplans are needed. The present study is to develop an optical control algorithm by using heating load curve according to the outdoor temperature change. Heating load analysis should be performed before the present method can be applied. Dynamic heating load simulations are performed by resistance-capacitance method. Results show that heating load decrease linearly according to the increase of outdoor temperature.

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