scholarly journals Development of a cooling load prediction model for air-conditioning system control of office buildings

2019 ◽  
Vol 14 (1) ◽  
pp. 70-75 ◽  
Author(s):  
Chengliang Fan ◽  
Yundan Liao ◽  
Yunfei Ding
Keyword(s):  
Prediction Model ◽  
Air Conditioning ◽  
Office Buildings ◽  
System Control ◽  
Cooling Load ◽  
Load Prediction ◽  
Air Conditioning System

Air Conditioning Load Prediction of an Office Building based on Long Short Term Memory Neural Network

2021 ◽  
Vol 14 ◽  
Author(s):  
Mengxiang Zhuang ◽  
Qixin Zhu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Air Conditioning ◽  
Short Term Memory ◽  
Load Prediction ◽  
Short Term ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Long Short Term Memory ◽  
Grey Relational

Background: Energy conservation has always been a major issue in our country, and the air conditioning energy consumption of buildings accounts for the majority of the energy consumption of buildings. If the building load can be predicted and the air conditioning equipment can respond in advance, it can not only save energy, but also extend the life of the equipment. Introduction: The Neural network proposed in this paper can deeply analyze the load characteristics through three gate structures, which is helpful to improve the prediction accuracy. Combined with grey relational degree method, the prediction speed can be accelerated. Method: This paper introduces a grey relational degree method to analyze the factors related to air conditioning load and selects the best ones. A Long Short Term Memory Neural Network (LSTMNN) prediction model was established. In this paper, grey relational analysis and LSTMNN are combined to predict the air conditioning load of an office building, and the predicted results are compared with the real values. Results: Compared with Back Propagation Neural Network (BPNN) prediction model and Support Vector Machine (SVM) prediction model, the simulation results show that this method has better effect on air conditioning load prediction. Conclusion: Grey relational degree analysis can extract the main factors from the numerous data, which is more convenient and quicker without repeated trial and error. LSTMNN prediction model not only considers the relation of air conditioning load on time series, but also considers the nonlinear relation between load and other factors. This model has higher prediction accuracy, shorter prediction time and great application potential.

EXERGY ANALYSIS OF THE PERFORMANCE OF A VARIABLE REFRIGERANT FLOW (VRF) AIR CONDITIONING SYSTEM

2011 ◽  
Vol 19 (01) ◽  
pp. 57-68 ◽  
Author(s):  
MIGUEL PADILLA
Keyword(s):  
System Performance ◽  
Air Conditioning ◽  
Exergy Analysis ◽  
Control Volume ◽  
Hvac Systems ◽  
Coefficient Of Performance ◽  
Cooling Load ◽  
Air Conditioning System ◽  
Operational Conditions ◽  
Highly Sensitive

Commercial multiple evaporators variable refrigerant flow (VRF) HVAC systems present many advantages such as being energy saving and the capability of adjusting refrigerant mass flow rate according to the change of high rises occurrence. This paper deals with an experimental control volume exergy analysis in a VRF air conditioning system. The experimental results show that the brunt of the total exergy destroyed in the whole system occurs in the outdoor unit, where the exergy destroyed in the condenser is more important. The values of coefficient of performance (COP) obtained for the tests increase as the system reaches operational conditions imposed in every indoor unit zone. The VRF system analyzed is highly sensitive to the action of the constant speed compressor. The use of an inverter compressor improves the system performance by adjusting the power consumption according to the cooling load in the evaporators.

Review and Evaluation of Using R-C Thermal Modeling of Cooling Load Prediction for HVAC System Control Purpose

2012 ◽  
Author(s):  
Oluwaseyi T. Ogunsola ◽  
Li Song
Keyword(s):  
Thermal Modeling ◽  
Model Performance ◽  
Hvac Systems ◽  
Measured Data ◽  
System Control ◽  
Cooling Load ◽  
Hvac System ◽  
Monitoring And Control ◽  
Load Prediction ◽  
Total Energy Consumption

Heating and cooling loads which are compensated by heating, ventilation, and air-conditioning (HVAC) systems, are the main reason for energy uses in buildings. Energy utilized by HVAC system accounts for two-thirds of a building’s total energy consumption. Excessive energy is consumed when HVAC systems fail to operate as intended. This is often due to several factors such as inappropriate monitoring and control strategy, lack of understanding of the dynamics of thermal loads, and system complexity. Amidst several models, estimation of cooling load using Resistance Capacitance (RC) models have proved to provide more robust and accurate estimates of the building load based on measured data but the use of this method is not without challenges. This study aims to highlight common challenges associated with implementation of the RC method for thermal modeling of cooling load. Past and current research have handled some of the challenges by introducing simplifying assumptions which if not adequately selected can lead to significant deviation between model performance and measured data. Without proper understanding of the challenges, engineers may not be able to place a high degree of confidence in load calculation methods and the computer implementations that they use.

scholarly journals ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ КОНДИЦІЮВАННЯ ЗОВНІШНЬОГО ПОВІТРЯ СИСТЕМИ КОМБІНОВАНОГО ТИПУ

2019 ◽  
pp. 9-14
Author(s):  
Євген Іванович Трушляков ◽  
Андрій Миколайович Радченко ◽  
Микола Іванович Радченко ◽  
Сергій Анатолійович Кантор ◽  
Веніамін Сергійович Ткаченко
Keyword(s):  
Heat Load ◽  
Air Conditioning ◽  
Rational Design ◽  
Cooling Capacity ◽  
Climatic Conditions ◽  
Energetic Efficiency ◽  
Cooling Load ◽  
Air Conditioner ◽  
Outdoor Air ◽  
Air Conditioning System

One of the most attractive reserves of enhancing the energetic efficiency of air conditioning systems is to provide the operation of refrigeration compressors in nominal or close to nominal modes by choosing rational design cooling loads (cooling capacities) and their distribution according to a cooling load behaviour within the overall design (installed) cooling load band to match current changeable climatic conditions and provide close to maximum annual cooling capacity generation according to cooling duties. The direction of increasing the efficiency of outdoor air conditioning in combined central-local type systems by rationally distributing the heat load - cooling capacity of the central air conditioner into zones of variable heat load in accordance with current climatic conditions and its relatively stable value, i.e. cooling capacity required for further air cooling at the entrance to the indoor recirculation air conditioning system is justified. By comparing the values of the excessive production of cold and its deficit within every 3 days for a rational design heat load of the air conditioning system (cooling capacity of the installed refrigeration machine), which provides close to maximum annual production of cold, and the corresponding values of the excess and deficit of cooling capacity in accordance with current climatic conditions during July substantiated the feasibility of accumulating the excess of cooling capacity of a central air conditioner at low current loads and its use for covering cooling deficit at elevated heat loads through pre-cooling the outdoor air. It is developed a scheme of a combined central-local air conditioning system, which includes the subsystems for the outdoor air conditioning in a central air conditioner and the local indoor recirculated air conditioning.

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