30,922 research outputs found
Optimal Control of Energy Efficient Buildings
The building sector consumes a large part of the energy used in the United States and is responsible for nearly 40% of greenhouse gas emissions. Therefore, it is economically and environmentally important to reduce the building energy consumption to realize massive energy savings. Commercial buildings are complex, multi-physics, and highly stochastic dynamic systems. Recent work has focused on integrating modern modeling, simulation, and control techniques to solving this challenging problem. The overall focus of this thesis is directed toward designing an energy efficient building by controlling room temperature. One approach is based on a distributed parameter model represented by a three dimensional (3D) heat equation in a room with heater/cooler located at ceiling. The finite element method is implemented as part of a novel solution to this problem. A reduced order model of only few states is derived using Proper Orthogonal Decomposition (POD). A Linear Quadratic Regulator (LQR) is computed based on the reduced model, and applied to the full order model to control room temperature. Also, a receding horizon constrained linear quadratic Gaussian (LQG) controller is developed by minimizing energy cost of heating and cooling while satisfying hard and probabilistic temperature constraints. A stochastic receding horizon controller (RHC) is employed to solve the optimization problem with the so-called chance constraints governed by probability temperature levels. Furthermore, a constrained stochastic linear quadratic control (SLQC) approach was developed for such purposes. The cost function to be minimized is quadratic, and two different cases are considered. The first case assumes the disturbance is Gaussian and the problem is formulated to minimize the expected cost subject to a linear constraint and a probabilistic constraint. The second case assumes the disturbance is norm-bounded with distribution unknown and the problem is formulated as a min-max problem. By using SLQC, both problems are reduced to semidefinite optimization problems, where the optimal control may be computed efficiently. Later, some discussions on solving more requirements by SLQC are provided. Simulation and numerical results are given to demonstrate the validity of the proposed techniques shown in this thesis
Screening of energy efficient technologies for industrial buildings' retrofit
This chapter discusses screening of energy efficient technologies for industrial buildings' retrofit
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
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High-Performance Integrated Window and FaƧade Solutions for California
The researchers developed a new generation of high-performance faƧade systems and supporting design and management tools to support industry in meeting Californiaās greenhouse gas reduction targets, reduce energy consumption, and enable an adaptable response to minimize real-time demands on the electricity grid. The project resulted in five outcomes: (1) The research team developed an R-5, 1-inch thick, triplepane, insulating glass unit with a novel low-conductance aluminum frame. This technology can help significantly reduce residential cooling and heating loads, particularly during the evening. (2) The team developed a prototype of a windowintegrated local ventilation and energy recovery device that provides clean, dry fresh air through the faƧade with minimal energy requirements. (3) A daylight-redirecting louver system was prototyped to redirect sunlight 15ā40 feet from the window. Simulations estimated that lighting energy use could be reduced by 35ā54 percent without glare. (4) A control system incorporating physics-based equations and a mathematical solver was prototyped and field tested to demonstrate feasibility. Simulations estimated that total electricity costs could be reduced by 9-28 percent on sunny summer days through adaptive control of operable shading and daylighting components and the thermostat compared to state-of-the-art automatic faƧade controls in commercial building perimeter zones. (5) Supporting models and tools needed by industry for technology R&D and market transformation activities were validated. Attaining Californiaās clean energy goals require making a fundamental shift from todayās ad-hoc assemblages of static components to turnkey, intelligent, responsive, integrated building faƧade systems. These systems offered significant reductions in energy use, peak demand, and operating cost in California
Using Personal Environmental Comfort Systems to Mitigate the Impact of Occupancy Prediction Errors on HVAC Performance
Heating, Ventilation and Air Conditioning (HVAC) consumes a significant
fraction of energy in commercial buildings. Hence, the use of optimization
techniques to reduce HVAC energy consumption has been widely studied. Model
predictive control (MPC) is one state of the art optimization technique for
HVAC control which converts the control problem to a sequence of optimization
problems, each over a finite time horizon. In a typical MPC, future system
state is estimated from a model using predictions of model inputs, such as
building occupancy and outside air temperature. Consequently, as prediction
accuracy deteriorates, MPC performance--in terms of occupant comfort and
building energy use--degrades. In this work, we use a custom-built building
thermal simulator to systematically investigate the impact of occupancy
prediction errors on occupant comfort and energy consumption. Our analysis
shows that in our test building, as occupancy prediction error increases from
5\% to 20\% the performance of an MPC-based HVAC controller becomes worse than
that of even a simple static schedule. However, when combined with a personal
environmental control (PEC) system, HVAC controllers are considerably more
robust to prediction errors. Thus, we quantify the effectiveness of PECs in
mitigating the impact of forecast errors on MPC control for HVAC systems.Comment: 21 pages, 13 figure
Numerical Methods for Nonlinear Optimal Control Problems and Their Applications in Indoor Climate Control
Efficiency, comfort, and convenience are three major aspects in the design of control systems for residential Heating, Ventilation, and Air Conditioning (HVAC) units. In this dissertation, we study optimization-based algorithms for HVAC control that minimizes energy consumption while maintaining a desired temperature, or even human comfort in a room. Our algorithm uses a Computer Fluid Dynamics (CFD) model, mathematically formulated using Partial Differential Equations (PDEs), to describe the interactions between temperature, pressure, and air flow. Our model allows us to naturally formulate problems such as controlling the temperature of a small region of interest within a room, or to control the speed of the air flow at the vents, which are hard to describe using finite-dimensional Ordinary Partial Differential (ODE) models. Our results show that our HVAC control algorithms produce significant energy savings without a decrease in comfort.
Also, we formulate a gradient-based estimation algorithm capable of reconstructing the states of doors in a building, as well as its temperature distribution, based on a floor plan and a set of thermostats. The estimation algorithm solves in real time a convection-diffusion CFD model for the air flow in the building as a function of its geometric configuration. We formulate the estimation algorithm as an optimization problem, and we solve it by computing the adjoint equations of our CFD model, which we then use to obtain the gradients of the cost function with respect to the flowās temperature and door states. We evaluate the performance of our method using simulations of a real apartment in the St. Louis area. Our results show that the estimation method is both efficient and accurate, establishing its potential for the design of smarter control schemes in the operation of high-performance buildings.
The optimization problems we generate for HVAC system\u27s control and estimation are large-scale optimal control problem. While some optimal control problems can be efficiently solved using algebraic or convex methods, most general forms of optimal control must be solved using memory-expensive numerical methods. In this dissertation we present theoretical formulations and corresponding numerical algorithms that can find optimal inputs for general dynamical systems by using direct methods. The results show these algorithms\u27 performance and potentials to be applied to solve large-scale nonlinear optimal control problem in real time
Optimization on fresh outdoor air ratio of air conditioning system with stratum ventilation for both targeted indoor air quality and maximal energy saving
Stratum ventilation can energy efficiently provide good inhaled indoor air quality with a proper operation (e.g., fresh outdoor air ratio). However, the non-uniform CO2 distribution in a stratum-ventilated room challenges the provision of targeted indoor air quality. This study proposes an optimization on the fresh outdoor air ratio of stratum ventilation for both the targeted indoor air quality and maximal energy saving. A model of CO2 concentration in the breathing zone is developed by coupling CO2 removal efficiency in the breathing zone and mass conservation laws. With the developed model, the ventilation parameters corresponding to different fresh outdoor air ratios are quantified to achieve the targeted indoor air quality (i.e., targeted CO2 concentration in the breathing zone). Using the fresh outdoor air ratios and corresponding ventilation parameters as inputs, energy performance evaluations of the air conditioning system are conducted by building energy simulations. The fresh outdoor air ratio with the minimal energy consumption is determined as the optimal one. Experiments show that the mean absolute error of the developed model of CO2 concentration in the breathing zone is 1.9%. The effectiveness of the proposed optimization is demonstrated using TRNSYS that the energy consumption of the air conditioning system with stratum ventilation is reduced by 6.4% while achieving the targeted indoor air quality. The proposed optimization is also promising for other ventilation modes for targeted indoor air quality and improved energy efficiency
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