"Ann" artifical neural networks and fuzzy logic models for cooling load prediction

Thesis (Master)--Izmir Institute of Technology, Mechanical Engineering, Izmir, 2005Includes bibliographical references (leaves: 44-45)Text in English; Abstract: Turkish and Englishx, 45 leavesIn this thesis Artificial Neural Networks (ANN) and fuzzy logic models of the building energy use predictions were created. Data collected from a Hawaian 42 storey commercial building chiller plant power consumption and independent hourly climate data were obtained from the National Climate Data Center of the USA. These data were used in both ANN and the fuzzy model setting up and testing. The tropical climate data consisted of dry bulb temperature, wet bulb temperature, dew point temperature, relative humidity percentage, wind speed and wind direction.Both input variables and the output variable of the central chiller plant power consumption were fuzzified, and fuzzy membership functions were employed. The Mamdani fuzzy rules (32 rule) in If .Then format with the centre of gravity (COG; centroid) defuzzification were employed. The average percentage error levels in the fuzzy model and the ANN model were end up with 11.6% (R2.0.88) and 10.3% (R2.0.87), respectively. The fuzzy model is successfully presented for predicting chiller plant energy use in tropical climates with small seasonal and daily variations that makes this fuzzy model

A study on neural network based system identification with application to heating, ventilating and air conditioning (hvac)system

Recent efforts to incorporate aspects of artificial intelligence into the design and operation of automatic control systems have focused attention on techniques such as fuzzy logic, artificial neural networks, and expert systems. Although LMS algorithm has been considered to be a popular method of system identification but it has been seen in many situations that accurate system identification is not achieved by employing this technique. On the other hand, artificial neural network (ANN) has been chosen as a suitable alternative approach to nonlinear system identification due to its good function approximation capabilities i.e. ANNs are capable of generating complex mapping between input and output spaces. Thus, ANNs can be employed for modeling of complex dynamical systems with reasonable degree of accuracy. The use of computers for direct digital control highlights the recent trend toward more effective and efficient heating, ventilating, and air-conditioning (HVAC) control methodologies. The HVAC field has stressed the importance of self learning in building control systems and has encouraged further studies in the integration of optimal control and other advanced techniques into the formulation of such systems. In this thesis we describe the functional link artificial neural network (FLANN), Multi-Layer Perceptron (MLP) with Back propagation (BP) and MLP with modified BP called the emotional BP and Neuro fuzzy approaches for the HVAC System Identification. The thesis describes different architectures together with learning algorithms to build neural network based nonlinear system identification schemes such as Multi-Layer Perceptron (MLP) neural network, Functional Link Artificial Neural Network (FLANN) and ANFIS structures. In the case of MLP used as an identifier, different structures with regard to hidden layer selection and nodes in each layer have been considered. It may be noted that difficulty lies in choosing the number of hidden layers for achieving a correct topology of MLP neural identifier. To overcome this, in the FLANN identifier hidden layers are not required whereas the input is expanded by using trigonometric polynomials i.e. with cos(nπu) and sin(nπu), for n=0,1,2,…. The above ANN structures MLP, FLANN and Neuro-fuzzy (ANFIS Model) have been extensively studied

Variable Structure-Based Control for Dynamic Temperature Setpoint Regulation in Hospital Extreme Healthcare Zones

In critical healthcare units, such as operation theaters and intensive care units, healthcare workers require specific temperature environments at different stages of an operation, which depends upon the condition of the patient and the requirements of the surgical procedures. Therefore, the need for a dynamically controlled temperature environment and the availability of the required heating/cooling electric power is relatively more necessary for the provision of a better healthcare environment as compared to other commercial and residential buildings, where only comfortable room temperature is required. In order to establish a dynamic temperature zone, a setpoint regulator is required that can control the zone temperature with a fast dynamic response, little overshoot, and a low settling time. Thus, two zone temperature regulators have been proposed in this article, including double integral sliding mode control (DISMC) and integral terminal sliding mode control (ITSMC). A realistic scenario of a hospital operation theater is considered for evaluating their responses and performance to desired temperature setpoints. The performance analysis and superiority of the proposed controllers have been established by comparison with an already installed Johnson temperature controller (JTC) for various time spans and specific environmental conditions that require setpoints based on doctors’ and patients’ desires. The proposed controllers showed minimal overshoot and a fast settling response, making them ideal controllers for operation theater (OT) zone temperature control

Energy research in airports: A review

The main function of an airport is to provide access to air transport both for passengers and cargo. The number of air operations over the past 20 years has increased rapidly, and this has led to a rise in the energy needs of airports to satisfy this demand. As a consequence, the cost of energy supply for airport managers has escalated. At the same time, global energy consumption has soared due to the needs of emerging countries like China and India, with the consequent environmental impact. This complex scenario of environmental and economic factors has made airport managers become aware of the need to reduce energy consumption as well as a more efficient use of it. The aim of this article is to analyze the main behaviors and energy trends at airports in more recent research, starting with the description of the main energy sources and consumers, the application of energy conservation and energy efficiency measures, the establishment of energy indicators and benchmarking between airports, as well as energy modeling and simulation

A meta-synthesis review of occupant comfort assessment in buildings (2002-2022)

Occupant comfort in buildings is one of the most crucial considerations in designing a building. Accordingly, there is a growing interest in this area. Aspects of comfort include thermal comfort, visual comfort, acoustic comfort, and indoor air quality (IAQ) satisfaction. The objective of this state-of-the-art review was to provide a comprehensive, explicit, and up-to-date literature review on occupant comfort in buildings, since this issue has a great impact on the lifestyle, health, and productivity of occupants. A meta-synthesis method was also used for an analytical-interpretive review of previous studies. In this research, scientific research studies related to the subject of indoor occupant comfort in the period 2002–2022 were reviewed. Previous reviews have often covered the fundamental concepts and principles related to indoor occupant comfort. Although innumerable studies have focused on thermal comfort, other aspects of occupant comfort have not been considered. The review is analyzed and discussed in reference to type of study, case study geographical locations and climate zones, case study building types, decision-making models, assessment criteria, datacollection tools, and data analysis strategies. Finally, future research recommendations are presented. Through the review, we find that the comfort models used in research are mostly based on comfort perception votes collected from experimental studies, which may not reflect the preferences of users well. In addition, only the influence of environmental factors on the models has been investigated, and other personal factors have been ignored. This study presents a useful guide for researchers to determine their outlines for future research in this field

Intelligent Approaches For Modeling And Optimizing Hvac Systems

Advanced energy management control systems (EMCS), or building automation systems (BAS), offer an excellent means of reducing energy consumption in heating, ventilating, and air conditioning (HVAC) systems while maintaining and improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. This research will evaluate model-based optimization processes (OP) for HVAC systems utilizing MATLAB, genetic algorithms and self-learning or self-tuning models (STM), which minimizes the error between measured and predicted performance data. The OP can be integrated into the EMCS to perform several intelligent functions achieving optimal system performance. The development of several self-learning HVAC models and optimizing the process (minimizing energy use) will be tested using data collected from the HVAC system servicing the Academic building on the campus of NC A&T State University. Intelligent approaches for modeling and optimizing HVAC systems are developed and validated in this research. The optimization process (OP) including the STMs with genetic algorithms (GA) enables the ideal operation of the building’s HVAC systems when running in parallel with a building automation system (BAS). Using this proposed optimization process (OP), the optimal variable set points (OVSP), such as supply air temperature (Ts), supply duct static pressure (Ps), chilled water supply temperature (Tw), minimum outdoor ventilation, reheat (or zone supply air temperature, Tz), and chilled water differential pressure set-point (Dpw) are optimized with respect to energy use of the HVAC’s cooling side including the chiller, pump, and fan. HVAC system component models were developed and validated against both simulated and monitored real data of an existing VAV system. The optimized set point variables minimize energy use and maintain thermal comfort incorporating ASHRAE’s new ventilation standard 62.1-2013. The proposed optimization process is validated on an existing VAV system for three summer months (May, June, August). This proposed research deals primarily with: on-line, self-tuning, optimization process (OLSTOP); HVAC design principles; and control strategies within a building automation system (BAS) controller. The HVAC controller will achieve the lowest energy consumption of the cooling side while maintaining occupant comfort by performing and prioritizing the appropriate actions. Recent technological advances in computing power, sensors, and databases will influence the cost savings and scalability of the system. Improved energy efficiencies of existing Variable Air Volume (VAV) HVAC systems can be achieved by optimizing the control sequence leading to advanced BAS programming. The program’s algorithms analyze multiple variables (humidity, pressure, temperature, CO2, etc.) simultaneously at key locations throughout the HVAC system (pumps, cooling coil, chiller, fan, etc.) to reach the function’s objective, which is the lowest energy consumption while maintaining occupancy comfort