This dissertation provides one methodology to determine potential energy savings of buildings with limited information. This methodology is based upon the simplified energy analysis procedure of HVAC systems and the control of the comfort conditions. Numerically, the algorithm is a tailored exhaustive search over all the independent variables that are commonly controlled for a specific type of HVAC system. The potential energy savings methodology has been applied in several buildings that have been retrofitted and/or commissioned previously. Results from the determined savings for the Zachry building at Texas A&M after being commissioned show a close agreement to the calculated potential energy savings (about 85%). Differences are mainly attributed to the use of simplified models. Due to the restriction of limited information about the building characteristics and operational control, the potential energy savings method requires the determination of parameters that characterize its thermal performance. Thus, a calibrated building is needed. A general procedure has been developed to carry out automated calibration of building energy use simulations. The methodology has been tested successfully on building simulations based on the simplified energy analysis procedure. The automated calibration is the minimization of the RMSE of the energy use over daily conditions. The minimization procedure is fulfilled with a non-canonical optimization algorithm, the Simulated Annealing, which mimics the Statistical Thermodynamic performance of the annealing process. That is to say, starting at a specified temperature the algorithm searches variable-space states that are steadier, while heuristically, by the Boltzmann distribution, the local minima is avoided. The process is repeated at a new lower temperature that is determined by a specific schedule until the global minimum is found. This methodology was applied to the most common air-handler units producing excellent results for ideal cases or for samples modified with a 1% white noise.

This thesis deals with design of appropriate optimization algorithms for purposes of newly developed tool Mechlab’s parameter estimation, which serves for parameter estimation of simulation models in Matlab/Simulink. Levenberg-Marquardt algorithm had been chosen among other gradient methods. On the other hand, genetic algorithm and simulated annealing had been chosen from category of soft computing techniques to be implemented. Chosen algorithms were tested on artifical problem of mechanical oscilator and also on real datasets from electronic throttle. Proposed simulated annealing worked in both cases whith sufficient results, nevertheless was time-costly. For the oscilator problem, all the algorithms provided accurate solutions, but in the case of real dataset, Levenberg-Marquardt functionality was limited, while genetic algorithm still provided excelent results.

First part of the thesis is about literature research of optimization algorithms. Three of the algorithms were implemented and tested, concretely the ant colony algorithm, tabu search and simulated annealing. All three algorithms were implemented to solve the traveling salesman problem. In second part of the thesis the algorithms were tested and compared. In last part the influence of the ant colony parameters was evaluated.

This thesis deals with the optimization of the structural design of large technology compounds. The main emphasis is placed on the participation of available optimization instruments in the design of supporting structures of these constructions. The introductory part is primarily focused on the optimization itself. In this part are further specified also used optimization methods, computational finite element method, implemented code checks and calculation of cost of construction. The main part is dedicated to application of optimization procedure for the design of steel supporting structure of the auxiliary technological object of thermal power plant. The objective criterium shall then constitute the total cost of construction works along with operating costs. For the solution is used Scia Engineer software together with optimization module Scia Engineer Optimization Toolbox. The whole analysis is presented comprehensively from the description of computational model to results evaluation. The attention is paid to the optimization task settings, technical solution of optimization, optimization methods deployment and during the calculation process (especially to the development of objective function values and desing constraints during the optimization solution).