scholarly journals An Automobile Heating, Ventilating and Air Conditioning (HVAC) System with a Neural Network for Controlling the Thermal Sensations Felt by a Passenger.

1997 ◽  
Vol 40 (3) ◽  
pp. 469-477 ◽  
Author(s):  
Matsuei UEDA ◽  
Yousuke TANIGUCHI ◽  
Akihiko ASANO ◽  
Miyo MOCHIZUKI ◽  
Tohru IKEGAMI ◽  
...  
Keyword(s):  
Neural Network ◽  
Air Conditioning ◽  
Hvac System

scholarly journals Sensorless Air Flow Control in an HVAC System through Deep Learning

2019 ◽  
Vol 9 (16) ◽  
pp. 3293 ◽  
Author(s):  
Junseo Son ◽  
Hyogon Kim
Keyword(s):  
Neural Network ◽  
Air Conditioning ◽  
Deep Neural Network ◽  
Static Pressure ◽  
Operating Cost ◽  
Appropriate Technology ◽  
Hvac System ◽  
Additional Costs ◽  
Air Flow Control ◽  
The Ideal

Sensor-based intelligence is essential in future smart buildings, but the benefits of increasing the number of sensors come at a cost. First, purchasing the sensors themselves can incur non-negligible costs. Second, since the sensors need to be physically connected and integrated into the heating, ventilation, and air conditioning (HVAC) system, the complexity and the operating cost of the system are increased. Third, sensors require maintenance at additional costs. Therefore, we need to pursue the appropriate technology (AT) in terms of the number of sensors used. In the ideal scenario, we can remove excessive sensors and yet achieve the intelligence that is required to operate the HVAC system. In this paper, we propose a method to replace the static pressure sensor that is essential for the operation of the HVAC system through the deep neural network (DNN).

A Novel Forecast Method for Air-Conditioning Load of Public Building Considering Accumulated Temperature Effect

2015 ◽  
Vol 9 (1) ◽  
pp. 363-367
Author(s):  
Qingshan Xu ◽  
Xufang Wang ◽  
Chenxing Yang ◽  
Hong Zhu ◽  
Qingguo Yan
Keyword(s):  
Neural Network ◽  
Temperature Effect ◽  
Air Conditioning ◽  
Forecast Accuracy ◽  
Power Network ◽  
Forecast Method ◽  
Elman Neural Network ◽  
Public Building ◽  
Effective Accumulated Temperature ◽  
Accumulated Temperature

It has great significance to estimate the schedulable capacity of air-conditioning load of public building for participating the power network regulation by forecasting the air-conditioning load accurately. A novel forecast method considering the accumulated temperature effect is proposed in this paper based on Elman neural network. Firstly, the starting and ending date for forecast considering the accumulated temperature effect are determined by providing the five day sliding average thermometer algorithm which is usually adopted in aerology research. Then, the effective accumulated temperature of each day is calculated. Finally, take the effective accumulated temperature, temperature and humidity into consideration, the air-conditioning load of public building in the forecast day is acquired by Elman neural network. Simulated results show that the higher forecast accuracy can be achieved by considering the accumulated temperature effect.

scholarly journals A Multi-Timescale Bilinear Model for Optimization and Control of HVAC Systems with Consistency

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 400 ◽  
Author(s):  
Zelin Nie ◽  
Feng Gao ◽  
Chao-Bo Yan
Keyword(s):  
Optimization Model ◽  
Air Conditioning ◽  
State Of The Art ◽  
Hvac Systems ◽  
Practical Significance ◽  
Hvac System ◽  
Bilinear Model ◽  
Optimization And Control ◽  
And Control ◽  
Slow Timescale

Reducing the energy consumption of the heating, ventilation, and air conditioning (HVAC) systems while ensuring users’ comfort is of both academic and practical significance. However, the-state-of-the-art of the optimization model of the HVAC system is that either the thermal dynamic model is simplified as a linear model, or the optimization model of the HVAC system is single-timescale, which leads to heavy computation burden. To balance the practicality and the overhead of computation, in this paper, a multi-timescale bilinear model of HVAC systems is proposed. To guarantee the consistency of models in different timescales, the fast timescale model is built first with a bilinear form, and then the slow timescale model is induced from the fast one, specifically, with a bilinear-like form. After a simplified replacement made for the bilinear-like part, this problem can be solved by a convexification method. Extensive numerical experiments have been conducted to validate the effectiveness of this model.

scholarly journals Thermal comfort control via air conditioning system using fuzzy neural network feedback controller

2020 ◽  
Vol 19 (2) ◽  
pp. 586
Author(s):  
Somaye A. Mohamadi ◽  
Abdulraheem J. Ahmed
Keyword(s):  
Neural Network ◽  
Thermal Comfort ◽  
Environmental Conditions ◽  
Air Conditioning ◽  
Fuzzy Controller ◽  
Environmental Parameters ◽  
Thermal Conditions ◽  
Real Time Control ◽  
Air Conditioning System ◽  
Air Conditioning Systems

<span>Despite their complexity and uncertainty, air conditioning systems should provide the optimal thermal conditions in a building. These controller systems should be adaptable to changes in environmental parameters. In most air conditioning systems, today, there are On/Off controllers or PID in more advanced types, which, due to different environmental conditions, are not optimal and cannot provide the optimal environmental conditions. Controlling thermal comfort of an air conditioning system requires estimation of thermal comfort index. In this study, fuzzy controller was used to provide thermal comfort in an air conditioning system, and neural network was used to estimate thermal comfort in the feedback path of the controller. Fuzzy controller has a good response given the non-linear features of air conditioning systems. In addition, the neural network makes it possible to use thermal comfort feedback in a real-time control.</span>

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.

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