Fuzzy-Neural Model Predictive Control of a Building Heating System

Terziyska, Margarita; Todorov, Yancho; Petrov, Michail

doi:10.3182/20061002-4-bg-4905.00012

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…The second AI tool is used for the predictive control functions of ANN: fuzzy or model-based predictive control (MPC) [ 32 , 42 , 47 , 48 , 50 , 65 , 72 , 75 , 87 , 92 , 94 , 101 , 103 , 104 ]. Predictive control provides feedback of the results of the prediction to the system to allow for the adjustment of a system’s control parameters.…”

Section: Ai Developments and The Applications For Hvac Systemsmentioning

confidence: 99%

“…The fifth type of AI tools is employed for fuzzy control [ 21 , 32 , 33 , 46 , 51 , 59 , 102 ], support vector machines (SVM), and R [ 28 , 30 , 38 , 56 , 79 , 82 , 89 ]. These two AI tools have the same amount of published articles.…”

Section: Ai Developments and The Applications For Hvac Systemsmentioning

confidence: 99%

“…The Harris index compares the variations between the initial control y(0) and y(t + 1). There are several articles discussing the effect of rising time, settling time, and overshooting [ 32 ] on the control performance of the linear system. However, the Harris index and NHI are able to assess the performance of linear, non-linear, feedforward, and feedback control systems [ 109 ], as well as thermal comfort and energy efficiency, etc.…”

Section: Theoretical Analysis Of Ai Assisted Hvac Controlmentioning

confidence: 99%

See 2 more Smart Citations

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

Cheng

Lee

2019

Sensors

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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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Section: Ai Developments and The Applications For Hvac Systemsmentioning

confidence: 99%

Section: Ai Developments and The Applications For Hvac Systemsmentioning

confidence: 99%

Section: Theoretical Analysis Of Ai Assisted Hvac Controlmentioning

confidence: 99%

See 1 more Smart Citation

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

Cheng

Lee

2019

Sensors

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“…Predictive control of heating and cooling of office building with cooperative fuzzy mode (CFMPC) is discussed in [22]. In [23], predictive control based on fuzzy-neural technique of heating buildings is proposed. Cascaded HVAC control of superheat temperature have been applied on AHUs, for which super twisting of sliding mode is presented in [24].…”

Section: Introductionmentioning

confidence: 99%

Robust Sliding Mode Control of Air Handling Unit for Energy Efficiency Enhancement

et al. 2017

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Abstract:In order to achieve feasible and copacetic low energy consuming building, a robust and efficient air conditioning system is necessary. Since heating ventilation and air conditioning systems are nonlinear and temperature and humidity are coupled, application of conventional control is inappropriate. A multi-input multi-output nonlinear model is presented. The temperature and humidity of thermal zone are ascendance by the manipulation of the water and air flow rates. A sliding mode controller (SMC) is designed to ensure robust performance of air handling unit in the presence of uncertainties. A simple proportional-integral-derivative (PID) controller is used as a comparison template to highlight the efficiency of the proposed controller. To accomplish tracking targets, a variety of desired temperature and relative humidity commands (including ramp and combination with sequence of steps) are investigated. According to simulation results, SMC transcends the PID controller in terms of settling time, steady state and rise time, which makes SMC more energy efficient.

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“…Indoor temperature oscillation could be reduced from 3.5 • C for PID control to 0.5 • C for neuro-fuzzy control • No energy savings were observed [18] FC for a small commercial building with eight test rooms…”

mentioning

confidence: 99%

Sustainable Air-Conditioning Systems Enabled by Artificial Intelligence: Research Status, Enterprise Patent Analysis, and Future Prospects

Lee

Chen

2022

Sustainability

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Artificial intelligence (AI) technologies have developed rapidly since 2000. Numerous academic papers have been published regarding energy efficiency improvements for air-conditioning systems. This study reviewed 12 review papers and selected 85 specific cases of applications of AI for HVAC energy usage reduction. In addition to academic studies, 31,221 patents related to HVAC energy-saving equipment filed by 11 companies were investigated. In order to analyze the large amount of data, this study developed a resource description framework (RDF) as an analysis tool. This tool was used with a natural language processing (NLP) program to compare the contents of academic papers and patents. With the automated analysis program, this study aimed to link academic research and corporate research and development, mainly the enterprise patent applications, to analyze the reasons why AI can effectively save energy. This represents a complete analysis of the current status of academic and industrial development. Six methods were identified to save energy effectively, including model-based predictive control (MPC), thermal comfort control, model-free predictive control, control optimization, multi-agent control (MAC), and knowledge-based system/rule set (KBS/RS)-based control. The energy savings of these methods were quantified to be 8.8–25.5%. These methods are widely covered by the examined corporate patent applications. After using NLP to retrieve patent keywords, the landscapes of enterprise patents were constructed and the future research directions were identified. It is concluded that 10 topics, including novel neural network designs, smartphone-assisted machine learning, and transfer learning, can be used to increase the energy-saving effects of AI and enable sustainable air-conditioning systems.

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Fuzzy-Neural Model Predictive Control of a Building Heating System

Cited by 7 publications

References 9 publications

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

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

Robust Sliding Mode Control of Air Handling Unit for Energy Efficiency Enhancement

Sustainable Air-Conditioning Systems Enabled by Artificial Intelligence: Research Status, Enterprise Patent Analysis, and Future Prospects

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