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).

scholarly journals Optimal Control Strategy for Variable Air Volume Air-Conditioning Systems Using Genetic Algorithms

2019 ◽  
Vol 11 (18) ◽  
pp. 5122 ◽  
Author(s):  
Nam-Chul Seong ◽  
Jee-Heon Kim ◽  
Wonchang Choi
Keyword(s):  
Optimal Control ◽  
Genetic Algorithms ◽  
Control Strategy ◽  
Air Conditioning ◽  
Static Pressure ◽  
Hvac System ◽  
Total Energy Consumption ◽  
Optimal Control Strategy ◽  
Variable Air Volume ◽  
Air Volume

This study is aimed at developing a real-time optimal control strategy for variable air volume (VAV) air-conditioning in a heating, ventilation, and air-conditioning (HVAC) system using genetic algorithms and a simulated large-scale office building. The two selected control variables are the settings for the supply air temperature and the duct static pressure to provide optimal control for the VAV air-conditioning system. Genetic algorithms were employed to calculate the optimal control settings for each control variable. The proposed optimal control conditions were evaluated according to the total energy consumption of the HVAC system based on its component parts (fan, chiller, and cold-water pump). The results confirm that the supply air temperature and duct static pressure change according to the cooling load of the simulated building. Using the proposed optimal control variables, the total energy consumption of the building was reduced up to 5.72% compared to under ‘normal’ settings and conditions.

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 Determining the Modelling Input Parameters for Heating, Ventilation, and Air Conditioning Systems in New Zealand Commercial Buildings

2021 ◽  
Author(s):  
◽  
Anthony Gates
Keyword(s):  
Energy Consumption ◽  
New Zealand ◽  
Air Conditioning ◽  
Energy Performance ◽  
Hvac Systems ◽  
Commercial Buildings ◽  
Hvac System ◽  
Input Parameters ◽  
Parameter Values ◽  
The Ideal

<p>Template energy calculation models that have been produced by the Building Energy End-use Study (BEES) team are used to quickly and reliably model commercial buildings and calculate their energy performance. The template models contain standardised equipment, lighting, and occupancy loads; cooling and heating requirements are calculated using an ideal loads air system. Using seven buildings, Cory et al. 2011a have demonstrated that the template models have the potential to closely match the monthly energy performance of detailed (individually purpose built) models and the real buildings. Three of these models were within the ±5% acceptable tolerance to be considered calibrated. The four template models that were not within the acceptable tolerance have been identified to have complex Heating, Ventilation, and Air Conditioning (HVAC) systems that the ideal loads air systems could not replicate. Because HVAC systems consume one of the largest proportions of energy in commercial buildings, this has a significant impact on the reliability of the template models. To address this issue, a set of detailed HVAC systems were needed to replace the ideal loads air systems. Due to HVAC system parameters not being collected by the BEES team and the lack of published modelling input parameters available, it is unknown what values are reasonable to use in the models. This study used a Delphi survey to collect real building information of the commonly installed HVAC systems in New Zealand commercial buildings. The survey formed a consensus between HVAC engineers that determined what the most commonly installed systems are and their associated performance values. The outcome of the survey was a documented set of system types and modelling input parameters that are representative of New Zealand HVAC systems. The responses of the survey were used to produce a set of HVAC system templates that replace the ideal loads air systems. The HVAC template models updated the software default parameter values with values that are representative of commonly installed systems in New Zealand. The importance of the updated input values was illustrated through a comparison of the calculated monthly energy consumption. The resulting difference in energy consumption using the updated parameter values is typically <5% monthly; at worst it is 75% for Variable Air Volume (VAV) system in the Wellington climate during June.</p>

scholarly journals Determining the Modelling Input Parameters for Heating, Ventilation, and Air Conditioning Systems in New Zealand Commercial Buildings

2021 ◽  
Author(s):  
◽  
Anthony Gates
Keyword(s):  
Energy Consumption ◽  
New Zealand ◽  
Air Conditioning ◽  
Energy Performance ◽  
Hvac Systems ◽  
Commercial Buildings ◽  
Hvac System ◽  
Input Parameters ◽  
Parameter Values ◽  
The Ideal

<p>Template energy calculation models that have been produced by the Building Energy End-use Study (BEES) team are used to quickly and reliably model commercial buildings and calculate their energy performance. The template models contain standardised equipment, lighting, and occupancy loads; cooling and heating requirements are calculated using an ideal loads air system. Using seven buildings, Cory et al. 2011a have demonstrated that the template models have the potential to closely match the monthly energy performance of detailed (individually purpose built) models and the real buildings. Three of these models were within the ±5% acceptable tolerance to be considered calibrated. The four template models that were not within the acceptable tolerance have been identified to have complex Heating, Ventilation, and Air Conditioning (HVAC) systems that the ideal loads air systems could not replicate. Because HVAC systems consume one of the largest proportions of energy in commercial buildings, this has a significant impact on the reliability of the template models. To address this issue, a set of detailed HVAC systems were needed to replace the ideal loads air systems. Due to HVAC system parameters not being collected by the BEES team and the lack of published modelling input parameters available, it is unknown what values are reasonable to use in the models. This study used a Delphi survey to collect real building information of the commonly installed HVAC systems in New Zealand commercial buildings. The survey formed a consensus between HVAC engineers that determined what the most commonly installed systems are and their associated performance values. The outcome of the survey was a documented set of system types and modelling input parameters that are representative of New Zealand HVAC systems. The responses of the survey were used to produce a set of HVAC system templates that replace the ideal loads air systems. The HVAC template models updated the software default parameter values with values that are representative of commonly installed systems in New Zealand. The importance of the updated input values was illustrated through a comparison of the calculated monthly energy consumption. The resulting difference in energy consumption using the updated parameter values is typically <5% monthly; at worst it is 75% for Variable Air Volume (VAV) system in the Wellington climate during June.</p>

Perancangan Pengkondisian Udara pada Ruangan Steril Rumah Sakit Mitra Medika dengan Standard Cleanroom Menggunakan Instalasi Ducting

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Fremmy Raymond Agustinus
Keyword(s):  
Air Conditioning ◽  
Static Pressure ◽  
Filter Medium ◽  
Positive Pressure ◽  
Design Tools ◽  
Microsoft Excel ◽  
Cooling Unit ◽  
X 24 ◽  
Air Passage ◽  
Booster Fan

Desain penyejuk udara juga dapat diterapkan di bidang kesehatan, dengan standar Cleanroom dapat diperoleh suhu, kelembaban, kenyamanan dan kebersihan yang dibutuhkan untuk ruang steril (ruang bedah). Perancangan pendingin udara dalam hal ini dilakukan dengan menentukan beban pendinginan yang diperlukan untuk ruang steril (ruang bedah), kemudian menentukan ukuran ducting, jalur ducting, dan jumlah penggunaan ducting. Desain ini menggabungkan unit split saluran yang dimodifikasi, kipas booster, filter pra, filter medium, dan filter HEPA dengan menggunakan saluran aluminium preinsulated sebagai saluran udara. Desain dilakukan dengan menggunakan perangkat lunak AutoCAD 2012, Design Tools Duct Sizer, dan Microsoft Excel. Dari hasil perhitungan dan desain didapatkan kebutuhan kapasitas 3 ruang bedah yaitu ducted ducted 100.000 BTUH sebanyak 3 unit, booster fan 3.3 - 4 Di WG sebanyak 3 unit, pre filter 24 "x 24" x 2 "6 set, filter menengah 610 x 610 x 290 mm 6 set, dan filter HEPA 1220 x 610 x 70 mm 12. Untuk ruang steril, tekanan statis yang dihasilkan oleh unit pendingin harus lebih besar daripada tekanan statis yang dihasilkan dari unit yang ada. di ruang semi steril. Dengan kata lain, ruang steril harus memiliki tekanan positif terhadap ruang semi steril. Hal ini dimaksudkan agar udara di ruang semi steril tidak masuk ke ruang steril ketika pintu antar ruangan dibuka. Desain dan perhitungan ruang bedah, suhu nyata yang diperoleh adalah 23 ° C ± 2 ° C dan kelembaban relatif yang diperoleh adalah 60% ± 2%.   Air conditioning design can also be applied in the health field, with cleanroom standard can be obtained temperature, humidity, comfort and hygiene needed for sterile room (surgical room). The design of air conditioning in this case is done by determining the cooling load required for the sterile room (surgical room), then determining the ducting size, ducting path, and the amount of ducting usage. This design combines modified ducted split unit, booster fan, pre filter, medium filter, and HEPA filter by using preinsulated aluminum duct as an air passage. The design is done by using AutoCAD 2012 software, Design Tools Duct Sizer, and Microsoft Excel. From the calculation and design result obtained the capacity requirement of 3 surgical room that is split ducted 100.000 BTUH as many as 3 units, booster fan 3.3 - 4 In WG as many as 3 units, pre filter 24"x 24" x 2" 6 sets, medium filter 610 x 610 x 290 mm 6 sets, and HEPA filter 1220 x 610 x 70 mm 12 sets. For the sterile room, the static pressure generated by the cooling unit shall be larger than the static pressure generated from the unit present in the semi sterile room. In other words, the sterile room must have positive pressure to the semi sterile room. It is intended that the air in the semi sterile room does not enter into the sterile room when the door between room opened. In this surgical room design and calculation, real temperature obtained is 23 °C ± 2 °C and the relative moisture obtained is 60% ± 2%.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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