Agent-based modeling and simulation in hospital energy planning

Healthcare facilities consist of various large buildings that have complex energy systems and high consumption volume of energy, as a result, they emit high carbon emissions. In this direction, the main aim of this thesis is to analyze the energy systems of a hospital in terms of energy converters, energy suppliers, and energy consumption, and consequently, providing flexible solutions that meet energy demands by reducing energy costs and carbon emissions. Initially, to analyze and put forth the pros and cons of the related literature, a systematic literature review was conducted by taking into consideration energy-relevant studies carried out for healthcare facilities. Like our reference hospital, it is difficult that reach short-term consumption data such as hourly load demands due to the absence of energy monitoring systems in particular old buildings. For the purpose of acquiring these short-term datasets, it was developed systematic algorithms that were derived from monthly data with the help of different sources of information, such as the data belong an energy feasibility study conducted in the previous years and various climatic factors. In the third phase, data envelopment analysis-based techniques were applied to measure the energy-based environmental efficiency of the hospital under different input variables on a monthly and yearly basis. In the fourth step, a comprehensive forecasting analysis was carried out with help of different machine learning regression models. In the main phase of the thesis, in the light of the aforementioned analyses, a detailed simulation model of the hospital energy system was improved based on an agent-based modeling approach. The simulation models in which three different scenarios are developed including the current situation are as follows; (1) hospital existing energy infrastructure, (2) adding cogeneration system to the current energy infrastructure, (3) adding cogeneration and photovoltaic systems together to the existing energy infrastructure. Once the running of the simulation models, metaheuristic-based simulation optimization approaches like genetic algorithm was employed to determine optimum cogeneration capacity and optimum number of photovoltaic panels under the aims of minimum system energy cost and carbon emission. Furthermore, a parametric analysis with different test problems was conducted to observe the system reaction to the parameter or condition change. The results indicate that the effective presence of cogeneration and photovoltaic is notable in the decreasing total system cost taking into consideration also lower carbon emissions.