scholarly journals Artificial Intelligence Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 2. Prior Information Notice (PIN) Sensor Design and Simulation Results

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3440 ◽  
Author(s):  
Chin-Chi Cheng ◽  
Dasheng Lee
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Air Conditioning ◽  
Prior Information ◽  
Energy Savings ◽  
Theoretical Derivation ◽  
Consumption Data ◽  
Temperature And Humidity ◽  
Hvac Control ◽  
Comparative Results

The study continues the theoretical derivation from Part 1, and the experiment is carried out at a bus station equipped with six water-cooled chillers. Between 2012 and 2017, historical data collected from temperature and humidity sensors, as well as the energy consumption data, were used to build artificial intelligence (AI) assisted heating ventilation and air conditioning (HVAC) control models. The AI control system, in conjunction with a specifically designed prior information notice (PIN) sensor, was used to improve the prediction accuracy. This data collected between 2012 and 2016 was used for AI training and PIN sensor testing. During the hottest week of 2017 in Taiwan, the PIN sensor was used to conduct temperature and humidity data predictions. A model-based predictive control was developed to obtain air conditioning energy consumption data. The comparative results between the predictive and actual data showed that the temperature and humidity prediction accuracies were between 95.5 and 96.6%, respectively. Additionally, energy savings amounting to 39.8% were achieved compared to the theoretical estimates of 44.6%, a difference of less than 5%. These results show that the experimental model supports the theoretical estimations. In the future, a PIN sensor will be installed in a chiller to further verify the energy savings of the AI assisted HVAC control.

Analysis for Energy Savings Potential and Economics of Combined Air-Conditioning System with Temperature and Humidity Decoupled Treatment

2011 ◽  
Vol 280 ◽  
pp. 71-75
Author(s):  
Zhong Chao Zhao ◽  
Dong Hui Zhang ◽  
Yu Ping Chen
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Air Conditioning ◽  
Energy Savings ◽  
Operating Cost ◽  
Air Conditioning System ◽  
Operation Mechanism ◽  
Energy Saving Potential ◽  
Temperature And Humidity

In this paper, the operation mechanism of combined air-conditioning system with temperature and humidity decoupled treatment (CACSTHDT) was presented, and the energy saving potential and economics of CACSTHDT were primarily analyzed through compared with a traditional air-conditioning system. The results indicated that CACSTHDT could save up to 28.64% energy consumption in comparison with a traditional air-conditioning system. The operating cost in one summer only was 71.36% of that cost of traditional air-conditioning system.

scholarly journals Artificial Intelligence-Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 1. Problem Formulation and the Hypothesis

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1131 ◽  
Author(s):  
Chin-Chi Cheng ◽  
Dasheng Lee
Keyword(s):  
Artificial Intelligence ◽  
Air Conditioning ◽  
Energy Savings ◽  
Weather Forecasting ◽  
Average Energy ◽  
Weather Forecast ◽  
Hvac Systems ◽  
Prediction Errors ◽  
Hvac Control ◽  
Maximum Saving

In this study, information pertaining to the development of artificial intelligence (AI) technology for improving the performance of heating, ventilation, and air conditioning (HVAC) systems was collected. Among the 18 AI tools developed for HVAC control during the past 20 years, only three functions, including weather forecasting, optimization, and predictive controls, have become mainstream. Based on the presented data, the energy savings of HVAC systems that have AI functionality is less than those equipped with traditional energy management system (EMS) controlling techniques. This is because the existing sensors cannot meet the required demand for AI functionality. The errors of most of the existing sensors are less than 5%. However, most of the prediction errors of AI tools are larger than 7%, except for the weather forecast. The normalized Harris index (NHI) is able to evaluate the energy saving percentages and the maximum saving rations of different kinds of HVAC controls. Based on the NHI, the estimated average energy savings percentage and the maximum saving rations of AI-assisted HVAC control are 14.4% and 44.04%, respectively. Data regarding the hypothesis of AI forecasting or prediction tools having less accuracy forms Part 1 of this series of research.

scholarly journals AUDIT ENERGI PADA SEBUAH HOTEL

2012 ◽  
Vol 16 (3) ◽  
pp. 131
Author(s):  
Didik Ariwibowo
Keyword(s):  
Energy Consumption ◽  
Air Conditioning ◽  
Energy Savings ◽  
Electric Energy ◽  
Electrical Energy ◽  
Hot Water ◽  
Electrical Energy Consumption ◽  
Energy Audit ◽  
Electric Energy Consumption ◽  
Pumping System

Didik Ariwibowo, in this paper explain that energy audit activities conducted through several phases, namely: the initial audit, detailed audit, analysis of energy savings opportunities, and the proposed energy savings. Total energy consumed consists of electrical energy, fuel, and materials in this case is water. Electrical energy consumption data obtained from payment of electricity accounts for a year while consumption of fuel and water obtained from the payment of material procurement. From the calculation data, IKE hotels accounted for 420.867 kWh/m2.tahun, while the IKE standards for the hotel is 300 kWh/m2.tahun. Thus, IKE hotel included categorized wasteful in energy usage. The largest energy consumption on electric energy consumption. Largest electric energy consumption is on the air conditioning (AC-air conditioning) that is equal to 71.3%, and lighting and electrical equipment at 27.28%, and hot water supply system by 4.44%. Electrical energy consumption in AC looks very big. Ministry of Energy and Mineral Resources of the statutes, the profile of energy use by air conditioning at the hotel by 48.5%. With these considerations in the AC target for audit detail as the next phase of activity. The results of a detailed audit analysis to find an air conditioning system energy savings opportunities in pumping systems. Recommendations on these savings is the integration of automation on the pumping system and fan coil units (FCU). The principle of energy conservation in the pumping system is by installing variable speed drives (VSD) pump drive motor to adjust speed according to load on the FCU. Load variations FCU provide input on the VSD pumps to match. Adaptation is predicted pump can save electricity consumption up to 65.7%. Keywords: energy audit, IKE, AC

Thermal performance assessment of cool roofs on supermarkets through case analysis in 13 cities

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Juan Carlos Ríos-Fernández
Keyword(s):  
Energy Consumption ◽  
Air Conditioning ◽  
Energy Savings ◽  
Real Data ◽  
Research Approach ◽  
Environmental Implications ◽  
Content Type ◽  
Cool Roof ◽  
Increase Energy Efficiency ◽  
Cool Roofs

PurposeThis paper aims to study the use of cool roof technology to avoid unnecessary energy consumption in supermarkets. This will allow to reduce and even cancel the heat absorbed by the roofs, transferring it to the buildings and thus, creating more sustainable cities.Design/methodology/approachThirteen real supermarkets with cool roofs were analysed in Australia, Canada, the USA and Spain. An analysis of so many supermarkets located in different parts of the world with different climatic zones has allowed an inductive analysis, obtaining real data of energy consumption associated with the air conditioning installations for a year with and without implementing the cool roof technology.FindingsThe paper provides insights on how the use of cool roof managed to reduce the need for energy for heating, ventilating and air conditioning by between 3.5 and 38%. Additionally, this technology reduces the annual generation of carbon dioxide (CO2) emissions per square meter of supermarket up to 2.7 kgCO2/m2. It could be an economical technology to apply in new and old buildings with a period of average economic recovery of four years.Research limitations/implicationsBecause of the chosen research approach, the research results may be generalisable. Therefore, researchers are encouraged to test proposals in construction with other uses.Practical implicationsThe paper includes economic and environmental implications for the development of cool roof technology and smooths the way for its implementation to increase energy efficiency in commercial buildings.Originality/valueThis paper is an innovative contribution to the application of cool roof technology as a source of energy savings in commercial construction through the analysis of supermarkets located in different countries with different climate zones. This will help other researchers to advance in this field and facilitate the implementation of the technology.

scholarly journals Design of an adiabatic air conditioning package system

2022 ◽  
Vol 13 ◽  
pp. 8
Author(s):  
Sachin Sunil Mothiravally ◽  
Sachidananda Hassan Krishanmurthy
Keyword(s):  
Thermal Analysis ◽  
Energy Consumption ◽  
Return On Investment ◽  
Air Conditioning ◽  
Energy Savings ◽  
Coefficient Of Performance ◽  
Cooling Systems ◽  
Air Conditioning System ◽  
Adiabatic Cooling ◽  
Save Energy

Air conditioning plays a significant role to maintain a cool atmosphere in warm conditions, However, the power consumed by the machine is higher. The commercial prevailing cooling systems are required to operate ventilation and cooling systems in buildings and in turn consumes more power. These systems apart from consuming electricity it also adds to the CO2 emissions to our environment. These energy consumption and CO2 emissions can be decreased by the assistance of energy effective frameworks to the prevailing air conditioning system. The study was conducted on a package unit of 414.2 kW by measuring the relative humidity, dry bulb, and wet bulb temperature to investigate the effect of indirect evaporative cooling on the systems COP. Also, the modelling of the package unit was done using Creo software and the analysis was carried out using ANSYS considering the flow and thermal analysis for different components of the package units. From this analysis it can be observed that by implementing the adiabatic cooling in package unit it is possible to save energy consumption. From the results it can be concluded that energy efficiency was more and the return on investment is high. Also, coefficient of performance of this machine is high and consumes less electricity and the expected energy savings is 20%.

scholarly journals HVAC Control System Using Predicted Mean Vote Index for Energy Savings in Buildings

Buildings ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 38
Author(s):  
Daniel Fernando Espejel-Blanco ◽  
José Antonio Hoyo-Montaño ◽  
Jaime Arau ◽  
Guillermo Valencia-Palomo ◽  
Abel García-Barrientos ◽  
...  
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Temperature Control ◽  
Air Conditioning ◽  
Energy Savings ◽  
Sensor Nodes ◽  
Temperature Control System ◽  
Raspberry Pi ◽  
Predicted Mean Vote ◽  
The Coordinator

Nowadays, reducing energy consumption is the fastest way to reduce the use of fossil fuels and, therefore, greenhouse gas emissions. Heating, Ventilation, and Air Conditioning (HVAC) systems are used to maintain an indoor environment in comfortable conditions for its occupants. The combination of these two factors, energy efficiency and comfort, is a considerable challenge for building operations. This paper introduces a design approach to control an HVAC, focused on an energy consumption reduction in the operation of the HVAC system of a building. The architecture was developed using a Raspberry Pi as a coordinator node and wireless connection with sensor nodes for environmental variables and electrical measurement nodes. The data received by the coordinator node is sent to the cloud for storage and further processing. The control system manages the setpoint of the HVAC equipment, as well as the turning on and off the HVAC compressor using an XBee-based solid state relay. The HVAC temperature control system is based on the Predicted Mean Vote (PMV) index calculation, which is used by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) to find the appropriate setpoint to meet the thermal comfort of 80% of users. This method combines the values of humidity and temperature to define comfort zones. The coordinator node makes the compressor control decisions depending on the value obtained in the PMV index. The proposed PMV-based temperature control system for the HVAC equipment achieves energy savings ranging from 33% to 44% against the built-in control of the HVAC equipment, when operating with the same setpoint of 26.5 grades centigrade.

scholarly journals Online Methodology for Separating the Power Consumption of Lighting Sockets and Air-Conditioning in Public Buildings Based on an Outdoor Temperature Partition Model and Historical Energy Consumption Data

2021 ◽  
Vol 11 (3) ◽  
pp. 1031
Author(s):  
Tianyi Zhao ◽  
Chengyu Zhang ◽  
Terigele Ujeed ◽  
Liangdong Ma
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Air Conditioning ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Public Buildings ◽  
Energy Monitoring ◽  
Consumption Data ◽  
Monitoring Platform

Among sub-items of energy consumption in public buildings, lighting sockets play an important role in energy-saving analysis. So, the energy consumption data quality of lighting sockets is important. However, limited by the initial cost of energy monitoring platform, it is difficult to install electricity meters covering all branches and to retrofit the incompact classification electricity branches, which results in a mixture of the lighting socket energy consumption and other components. In this study, a separation methodology is proposed. First, the abnormal data in the energy monitoring platform are cleaned and screened using a clustering algorithm. Second, the average outdoor air temperature partitioning model (OATPM) method and the k-nearest neighbor (KNN) clustering algorithm method are proposed for identifying and separating the abnormal data. These two methods have complementary advantages in the best applicable scenarios, including calculation accuracy and other aspects. The verification results for three buildings show that the relative error of this separation methodology is less than 15%. Finally, this paper presents the optimization parameters of the KNN method. Through this methodology, building managers need only historical data in an energy monitoring platform to separate the combined power consumption of the lighting sockets and air-conditioning online, independent of detailed information statistics.

scholarly journals Alternativa energética sustentable mediante la utilización de aislantes térmicos de diferentes materiales en edificaciones con sistemas de aire acondicionado

2020 ◽  
pp. 1-8
Author(s):  
Edzel Jair Casados-López ◽  
Alvaro Casados-Sánchez ◽  
Raúl Cruz-Vicencio ◽  
Alvaro Horst-Sánche
Keyword(s):  
Energy Consumption ◽  
Air Conditioning ◽  
Energy Savings ◽  
Cooling Load ◽  
Test Models ◽  
Scale Models ◽  
Conditioning Equipment ◽  
Actual Energy Consumption ◽  
Reduction Of Co2 ◽  
The City

A methodology is proposed for calculating the cooling load and the energy consumption of air conditioning equipment in three scale models of buildings under study, using the ASHRAE CLTD / SCL / CLF method. The building in which the mentioned method is used are three scale models of buildings located in the city of Poza Rica, state of Veracruz, Mexico. This method is applied in order to obtain the cooling load as exact as possible and thus avoid oversizing in air conditioning equipment, and by using thermal insulation, achieve a decrease in energy consumption and thus contribute to the reduction of CO2 emissions, to energy saving and therefore to sustainable development. The cooling load is calculated by applying the proposed methodology to three cases: model A, B and C. The results for the three test models, object of this study, are compared. Measurements of energy consumption are made to perform the error analysis of the actual energy consumption with respect to that calculated using the method. Finally, energy savings are quantified, in the cases mentioned.

Energy Consumption Simulation of the Air Conditioning System in Large-Scale Buildings

2013 ◽  
Vol 724-725 ◽  
pp. 1506-1509
Author(s):  
Guang Ming Zhang ◽  
Xue Shen ◽  
Gui Zhong Tang
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Air Conditioning ◽  
Large Scale ◽  
Working Environment ◽  
Operation Condition ◽  
Air Conditioning System ◽  
Factors Affecting ◽  
Network Training ◽  
Consumption Data

The working environment of air conditioning system in large-scale building is very complex, and there is no significant linear relationship between factors affecting energy consumption and energy demand of air conditioning system. This study adopts a nonlinear regression model: ANN (artificial neural network) model as energy model of air conditioning system. Take outdoor temperature, categorical day-of-week variable, equipment efficiency and terminal load as input, energy demand as output. Use energy consumption data in 2011 for network training, and energy consumption data in 2012 to verify the reliability of model. Based on energy analysis, the operation condition and the characteristics of energy consumption of air conditioning system for large-scale buildings in Nanjing could be precisely represented.

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