Hybrid Method for Dynamic Thermal Modelling of Buildings Based on the Resistance- Capacitance Analogy

U ovoj disertaciji istražena je izrada termodinamičkog modela zgrade i HVAC sustava, s namjerom da se model koristi kao temelj upravljanja u modelskom prediktivnom upravljanju, a sve s ciljem poboljšavanja energetske učinkovitosti u zgradi. Cilj istraživanja bio je razvoj metode za izradu hibridnog termodinamičkog modela zgrade tipa siva kutija koji se temelji na otporničkokapacitivnoj analogiji za određivanje strukture modela i te određivanju parametara modela na temelju raspoloživih mjernih podataka. Predstavljen je algoritam koji omogućuje automatiziranu izradu strukture termodinamičkog modela zgrade u obliku prostora stanja iz građevinskog nacrta zgrade. Početni parametri modela postavljaju se na temelju nazivnih podataka o svojstvima korištenih materijala, no u drugom koraku koriste se mjerenja sa zgrade za prilagodbu modela kroz prepodešavanje njegovih parametara. Za prilagodbu se koristi minimalizacija funkcije greške definirane razlikom između mjerenja i izlaza modela. Razvijena metoda i njome dobiveni modeli testirani su korištenjem mjernih podataka sa stvarne zgrade te uspoređeni s mjerenjima i rezultatima dobivenim pomoću umjetnih neuronskih mreža. Rezultati pokazuju da se predložena metoda može koristiti za automatiziranu izradu termodinamičkog modela zgrade, s rezultatima koji su dovoljno točni za korištenje u modelskom prediktivnom upravljanju, a usporedba pokazuje da hibridni model daje bolje rezultate od umjetnih neuronskih mreža.This thesis investigates the development of thermodynamic model of building and HVAC system, with purpose of using this model as a basis for Model Predictive Control, with goal of increasing the energy efficiency in buildings. The goal of the research was to develop a method for developing grey-box thermodynamic model of building based on resistance-capacitance analogy for structure of model and estimation of model parameters based on available measured data. It proposes an algorithm that enables automated development of structure of thermodynamic model in state-space representation based on construction drawing of building. Initial parameters of the model are based on nominal information of building materials' properties, but in second step, measured data from a building are used for fitting of model. The fitting is accomplished by minimization of error-function defined as difference between the measurements and outputs of the model. The method and developed models are tested with data from a real building and compared to measurements and results from Artificial Neural Network. Results show that proposed method enables automated development of thermal model of building, with results acceptable for use in Model Predictive Control, while comparison shows that hybrid model gives better results than Artificial Neural Network

Simplified Optimal Control in HVAC systems

Abstract — This paper presents simplified optimal control of a heating, ventilating, and air-conditioning (HVAC) system. This HVAC system is a typical one composed of two heat exchangers: an air-to-air heat exchanger (a rotary wheel heat recovery) and a water-to-air heat exchanger. First the optimal control strategy which was developed in [1] is adopted for implementation in a real life HVAC system. Then the bypass flow problem is addressed and a controller is introduced to deal with this problem. Finally a simplified control structure is proposed for optimal control of the HVAC system. Results of implementing the simplified optimal controller show all control objectives are met (The cost function consists of electrical and thermal energy consumption by the HVAC system). I