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

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.

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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 ◽

10.3390/s19153440 ◽

2019 ◽

Vol 19 (15) ◽

pp. 3440 ◽

Cited By ~ 2

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.

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Dynamic simulation of indirect air conditioning systems with optimized computational time

E3S Web of Conferences ◽

10.1051/e3sconf/201911104042 ◽

2019 ◽

Vol 111 ◽

pp. 04042

Author(s):

Nicolás Ablanque ◽

Santiago Torras ◽

Carles Oliet ◽

Joaquim Rigola ◽

Carlos-David Pérez-Segarra

Keyword(s):

Air Conditioning ◽

Energy Savings ◽

Control Strategies ◽

Model Potential ◽

Hvac Systems ◽

Object Oriented Programming ◽

Computational Time ◽

Air Conditioning System ◽

Numerical Resolution ◽

Air Conditioning Systems

The simulation of HVAC systems is a powerful tool to improve the energy efficiency in buildings. The modelling of such systems faces several obstacles due to both the physical phenomenology present and the numerical resolution difficulties. The present work is an attempt to develop a robust, fast, and accurate model for HVAC systems that can interact with the other relevant systems involved in buildings thermal management. The whole system model has been developed in the form of libraries under the Modelica language to exploit its advantageous characteristics: object-oriented programming, equationbased modelling, and handling of multi-physics. The global resolution is carried out dynamically so that not only steady-state predictions can be conducted but also control strategies can be studied over meaningful periods of time. This latter aspect is crucial for optimizing energy savings. The libraries include models for all the system individual components such as pumps, compressors or heat exchangers (operating with twophase flows and/or moist air) and also models assemblies to account for vapour compression units and liquid circuits. An illustrative example of an indirect air conditioning system is detailed in the present work in order to highlight the model potential.

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IMPROVING BUILDING ENERGY EFFICIENCY USING DISTRIBUTED ARTIFICIAL INTELLIGENCE

10.7562/se2013.3.01.06 ◽

2013 ◽

Vol 3 (1) ◽

Cited By ~ 1

Author(s):

Milica Radic ◽

Dejan Petkovic

Keyword(s):

Artificial Intelligence ◽

Energy Efficiency ◽

Intelligent Agents ◽

Energy Savings ◽

Building Energy ◽

Hvac Systems ◽

Distributed Artificial Intelligence ◽

Building Energy Efficiency ◽

Reduce Energy Consumption ◽

Temperature Levels

Saving energy in buildings without losing the comfort of the occupants is contradictory request. It has been shown that the use of smart thermostats to HVAC systems reduce energy consumption as much as thirty percent. Depending on the number of different, pre-defined temperature levels, energy savings might be even greater. The method for determining the coefficient of utilization which is based on a normal time temperature distribution is proposed. Key words:Building energy efficiency, TRIZ matrix, Intelligent agents, Coefficient of utilization

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Control and communication between PLC S7-1200 and ATV310 drive via modbus protocol

Thu Dau Mot University Journal of Science ◽

10.37550/tdmu.ejs/2020.03.066 ◽

2020 ◽

pp. 289-297

Keyword(s):

Supervisory Control ◽

Air Conditioning ◽

Energy Savings ◽

Induction Motors ◽

Hvac Systems ◽

Variable Frequency ◽

Efficient Operation ◽

Significant Percentage ◽

Modbus Protocol ◽

Variable Frequency Drives

Variable Frequency Drives (VFDs) are electronic power controllers that allow for accurate control of the speed of alternating current (AC) induction motors that used in many kinds of machines including fans, pumps and compressors. These motors are used in most heating, ventilation and air-conditioning (HVAC) systems and account for a significant percentage of the total HVAC energy consumption. More efficient operation of these motors using VFDs can result in significant energy savings. Besides, the communication between VFDs and PLC for Supervisory Control And Data Acquisition (SCADA) is also important. Modbus protocol has many kinds as RS485, RTU, Profinet,...In this paper we will present the communication between a PLC Siemens S7-1200 and an ATV310 Drive (Schneider) via Modbus RTU protocol which is supported.

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

Energies ◽

10.3390/en14020400 ◽

2021 ◽

Vol 14 (2) ◽

pp. 400 ◽

Cited By ~ 1

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.

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A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Energies ◽

10.3390/en14010156 ◽

2020 ◽

Vol 14 (1) ◽

pp. 156

Author(s):

Paige Wenbin Tien ◽

Shuangyu Wei ◽

John Calautit

Keyword(s):

Deep Learning ◽

Real Time ◽

Energy Demand ◽

Energy Savings ◽

Internal Heat ◽

Electrical Equipment ◽

Building Energy ◽

Hvac Systems ◽

Equipment Usage

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

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Proposal to Refine Solar Radiation of Typical Meteorological Year Database and Evaluation on the Influence of Air-Conditioning Load

Atmosphere ◽

10.3390/atmos12040524 ◽

2021 ◽

Vol 12 (4) ◽

pp. 524

Author(s):

Jihui Yuan ◽

Kazuo Emura ◽

Craig Farnham

Keyword(s):

Solar Radiation ◽

Air Conditioning ◽

Energy Savings ◽

Energy Resource ◽

Global Solar Radiation ◽

Resource Assessment ◽

Weather Data ◽

Diffuse Solar Radiation ◽

Chinese Standard ◽

The Impact

The Typical meteorological year (TMY) database is often used to calculate air-conditioning loads, and it directly affects the building energy savings design. Among four kinds of TMY databases in China—including Chinese Typical Year Weather (CTYW), International Weather for Energy Calculations (IWEC), Solar Wind Energy Resource Assessment (SWERA) and Chinese Standard Weather Data (CSWD)—only CSWD is measures solar radiation, and it is most used in China. However, the solar radiation of CSWD is a measured daily value, and its hourly value is separated by models. It is found that the cloud ratio (diffuse solar radiation divided by global solar radiation) of CSWD is not realistic in months of May, June and July while compared to the other sets of TMY databases. In order to obtain a more accurate cloud ratio of CSWD for air-conditioning load calculation, this study aims to propose a method of refining the cloud ratio of CSWD in Shanghai, China, using observed solar radiation and the Perez model which is a separation model of high accuracy. In addition, the impact of cloud ratio on air-conditioning load has also been discussed in this paper. It is shown that the cloud ratio can yield a significant impact on the air conditioning load.

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Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Entropy ◽

10.3390/e23020198 ◽

2021 ◽

Vol 23 (2) ◽

pp. 198

Author(s):

Stephen Fox

Keyword(s):

Artificial Intelligence ◽

Quality Management ◽

Social Organization ◽

Generative Models ◽

Industrial Engineering ◽

Prediction Errors ◽

Active Inference ◽

For Profit ◽

Different Types ◽

Inference Theory

Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence.

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AUDIT ENERGI PADA SEBUAH HOTEL

GEMA TEKNOLOGI ◽

10.14710/gt.v16i3.4710 ◽

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

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Development and Validation of Online Models With Parameter Estimation for a Building Zone With VAV System

Dynamic Systems and Control, Parts A and B ◽

10.1115/imece2004-59896 ◽

2004 ◽

Author(s):

Jin Wen ◽

Theodore F. Smith

Keyword(s):

Parameter Estimation ◽

Air Flow ◽

Hvac Systems ◽

Test Facility ◽

Prediction Errors ◽

Commercial Building ◽

Variable Air Volume ◽

Online System ◽

System Models ◽

Validation Tests

The energy consumption by building heating, ventilating, and air conditioning (HVAC) systems has evoked more attention for energy efficient HVAC control and operation. Application of advanced control and operation strategies requires robust online system models. In this research, online models with parameter estimation for a building zone with variable air volume (VAV) system, which is one of the most common HVAC systems, are developed and validated using experimental data. Building zone temperature and VAV entering air flow are modeled based on physical rules and using only the measurements that are commonly available in a commercial building. Different series of validation tests were performed in a real-building test facility to examine the prediction accuracies for system outputs. Using the online system models with parameter estimation, the prediction errors for all the validation tests are less than 0.5°F for temperature outputs, and less than 50 ft3/min for air flow outputs. The online models can be further used for local and supervisory control, as well as fault detection applications.

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