1,720,963 research outputs found
Energy Efficient Control and Fault Detection for HVAC Systems
Full text linkThe interest in HVAC (Heating, Ventilation and Air-Conditioning) technology
has rapidly increased in the last years. HVAC systems have become important in the design of medium-large buildings in order to ensure thermal comfort in the environments with respect to the temperature and humidity of the air. Control, optimisation and maintenance procedures are fundamental in HVAC systems in order to guarantee people comfort and energy efficient solutions in their management.
Two different topics are covered in this thesis.
Energy Efficient Control of Ice Thermal Energy Storage Systems
HVAC plants have recently begun to be matched with thermal energy storage systems. If properly designed, installed, and maintained, these systems can be used to store energy when its cost is low and exploiting it when the price increases. In particular, in HVAC cooling systems, a common thermal storage medium is ice. From a control and optimisation point of view, a cooling plant with ice storage proves to be a complex system. Standard control strategies seem not to be able to achieve the right trade-off between energy efficiency and demand satisfaction.
In this thesis, in order to design efficient control strategies for storage systems, a HVAC model with ice storage is developed in a simulation environment. The thermal behaviour of the HVAC system is derived from the mass and energy conservation equations; in particular the ice storage is considered a hybrid system, thus taking into consideration both sensible and latent heat. Three standard control methods are compared with a non-linear predictive control strategy. The simulations results show that the implemented non-linear predictive control strategy provides the best control for the efficient energy management of ice storage systems.
Fault Detection in HVAC Systems
Operating problems associated with degraded equipment, poor maintenance, and improperly implemented controls, plague many HVAC systems. Fault detection methods can therefore play a key role in monitoring complex HVAC plants, detecting anomalous behaviours in such a way as to keep the systems in their best operational conditions with minimum costs.
In this thesis, fault detection and diagnosis methods on variable air volume (VAV) systems are first designed. To this aim, a VAV system model with two zones is developed; the control of system is obtained with a direct feedback linearisation technique. Supervised classification methods are used to detect and diagnose the simulated faults in the model. The simulations results show the good performances of the classification in the detection and diagnosis of the most common faults in VAV systems.
Detection methods are then developed for the most relevant faults affecting chillers. To this aim, data collected in the research project 1043-RP promoted by ASHRAE (American Society of Heating, Refrigerating and Air Conditioning Engineers) are used. In this project experimental studies were conducted on a centrifugal water-cooled chiller in order to collect data in both normal and faulty situations. The developed technique is based on one-class classification methods with a novelty detection approach, where only normal data are used to characterize the correct system behaviour. The classification results confirm the effectiveness of the proposed method for the detection of the most common faults in chillers
Energy efficient control of HVAC systems with ice cold thermal energy storage
No full textIn heating, ventilation and air conditioning (HVAC) systems of medium/high cooling capacity, energy demands can be matched with the help of thermal energy storage (TES) systems. If properly designed, TES systems can reduce energy costs and consumption, equipment size and pollutant emissions. In order to design efficient control strategies for TES systems, we present a model-based approach with the aim of increasing the performance of HVAC systems with ice cold thermal energy storage (CTES). A simulation environment based on Matlab/Simulink® is developed, where thermal behaviour of the plant is analysed by a lumped formulation of the conservation equations. In particular, the ice CTES is modelled as a hybrid system, where the water phase transitions (solid-melting-liquid and liquid-freezing-solid) are described by combining continuous and discrete dynamics, thus considering both latent and sensible heat. Standard control strategies are compared with a non-linear model predictive control (NLMPC) approach. In the simulation examples model predictive control proves to be the best control solution for the efficient management of ice CTES systems
Load forecasting for the efficient energy management of HVAC systems
No full textIn this paper, Artificial Neural Networks (ANNs) are used to achieve cooling load forecasting in HVAC (Heating, Ventilating, and Air Conditioning) systems. Load forecasting is crucial in plant configurations making use of thermal storage technologies, where, during the nighttime, part or most of the energy required during daytime is produced at lower cost by cooling or icing water. Load forecasting is then needed to quantify the energy to be stored for the following daytime and to set up strategies for its release during daytime. Although many algorithms have been presented in the literature for load forecasting, they often need as input a large data set, that is not always available in practical situations. In this paper, we present an algorithm based on ANNs that allows to obtain sufficiently accurate load predictions by exploiting a limited data set, obtained by measuring quantities that are typically available in standard HVAC installations. Furthermore, knowledge of the current thermal load (which is needed to setup the data set for ANN training) can be obtained by using a load estimation algorithm previously proposed by some of the authors, that only need basic knowledge of the system hydronics. Another distinctive feature of the algorithm is the use of the AHU schedule as a means for inferring information on the internal loads, which is in general not available in practice. Simulation results for both CAV and VAV HVAC systems confirm the viability of the approach
Modeling and control of HVAC systems with ice-cold thermal energy storage
No full textIn this paper we present a model-based approach
for designing efficient control strategies with the aim of increasing
the performance of Heating, Ventilation and Air-
Conditioning (HVAC) systems with ice Cold Thermal Energy
Storage (ice CTES). The use of TES systems ensures reduced
energy costs and energy consumption, increased flexibility of
operation, reduced equipment size and pollutant emissions. A
simulation environment based on Matlab/Simulink® is developed,
where the thermal behaviour of the plant is analysed
by a lumped formulation of the conservation equations. In
particular, the ice CTES is modelled as a hybrid system,
where the water phase transitions (solid-melting-liquid, liquidfreezing-
solid) are described by combining continuous and discrete
dynamics, thus considering both latent and sensible heat.
Three standard control strategies and a model predictive control
approach are developed and compared. Extensive simulations
confirm that the MPC provides the best control in terms of
energy efficiency and cooling load demand satisfaction with
respect to standard control strategies
A process-history based fault detection and diagnosis for VAVAC systems
No full textFaulty operations of Heating, Ventilation and Air
Conditioning (HVAC) systems can lead to discomfort for the
occupants, energy wastage, unreliability and shorter equipment
life. Cost-effective Fault Detection and Diagnosis (FDD) methods
can therefore ensure an increase in the system uptime,
reliability, and overall efficiency. In this paper, a simulation
environment based on Matlab/Simulink
R is used in order to
evaluate the performance of a FDD method using Support
Vector Machines (SVMs). In detail, the proposed method is
evaluated by performing extensive simulations to allow the
investigation of the most common and relevant faults affecting
this kind of systems
A One-Class SVM Based Tool for Machine Learning Novelty Detection in HVAC Chiller Systems
No full text
Model Predictive Control for Efficient Management of Energy Resources in Smart Buildings
Full text linkEfficient management of energy resources is crucial in smart buildings. In this work, model predictive control (MPC) is used to minimize the economic costs of prosumers equipped with production units, energy storage systems, and electric vehicles. To this purpose, the predictive control manages the available energy resources by exploiting future information about energy prices, absorption and production power profiles, and electric vehicle (EV) usage, such as times of departure and arrival and predicted energy consumption. The predictive control is compared with a rule-based technique, herein referred to as a heuristic approach, that acts in an instant-by-instant fashion without considering any future information. The reported results show that the studied predictive approach allows one to achieve charging profiles that adapt to variable operating conditions, aiming at optimal performances in terms of economic cost minimization in time-varying price scenarios, reduction of rms current stresses, and recharging capability of EV batteries. Specifically, unlike the heuristic method, the MPC approach is proven to be capable of efficiently managing the available energy resources to ensure a full recharge of the EV battery during nighttime while always respecting all system constraints. In addition, the proposed control is shown to be capable of keeping the peak power absorption from the grid constrained within set limits, which is a valuable feature in scenarios with widespread adoption of EVs in order to limit the stress on the electrical system
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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