# Energy Analysis in Green Building via Machine Learning: A Case Study in a Hospital
## 1 Introduction  
In recent years, due to the worldwide climate crisis, measures have been taken one after another in the energy sector. Events, such as changing average temperatures due to global warming, waste released during energy production, and the depletion of natural resources, have led to the formation of some standards in the energy sector. Therefore, environmentally friendly systems have come to the fore and, as a result, the green building concept has emerged. Green buildings are also referred to as sustainable buildings, ecological buildings, and energy-efficient buildings. These terms mean that buildings exhibit high performance throughout their lifetime and cause little harm to the environment. More than 40% of the energy used in the world is spent in buildings [1]. In this study, energy analysis was conducted in a hospital with a green building. One of the most important steps to be taken to optimize energy consumption in a building is energy analysis. Data science and machine learning have come to the fore inrecent years in energy analysis in buildings [2]. The number of articles using machine learning techniques has been on a steady rise in recent years [3]. Predicting building energy loads is an application of big data science. Analyzes, such as building load estimation and determination of the relationship between energy load and consumption, can be made. Depending on the type of analysis to be performed, options such as classification, regression, or clustering can be used. There are many machine learning methods that can be used for these processes. Some of these are long short-term memory (LSTM), support vector machines (SVMs), and group method of data handling (GMDH). One of the most effective and easiest methods to increase efficiency and save energy in electrical energy systems is reactive power compensation. Another valid way to improve energy quality is to examine and eliminate the negative effects of harmonic components. With the development of semiconductor elements, the use of rectifiers has increased and as a result, harmonic distortions have begun to be seen in electrical installations today [4]. There are many factors that affect energy consumption levels in the hospital subject of the study. While the density of people generally increases on weekdays, it decreases on weekends or public holidays. On the other hand, the density of patients and visitors changes during the day. While the hospital is very full during some hours, it may be relatively empty at other times. In addition, the most important reason for the increase and decrease in energy consumption is the devices used in the hospital. Among these devices, those that draw particularly high loads and operate only at certain times are the main reasons that cause fluctuations and harmonic distortions in the system. The hospital in our study consists of a 19-story building with an area of 55,000 square meters and contains many different outpatient clinics and health units. There is one absorption chiller and three centrifugal chillers used for energy and air conditioning purposes in the hospital. In addition, more than 40 air handling units, air conditioning systems with HEPA filters in certain areas, a fully equipped operating room consisting of 19 different units and sleep-wake-up units, fire pump systems, and jet-fans ready to be used in emergencies are some of the factors that enable the hospital to reach high consumption values. In addition, in some parts of the hospital, especially in radiology, there are devices that draw high energy when used, such as X-ray machines, MRI machines, and PET MRIs. Energy levels constantly change throughout the day as such devices and units are used and turned off as needed. This causes harmonic distortions and an increase in the total harmonic distortion (THD) value in the hospital's main distribution panel. In order to solve the problem, there is a compensation room in the hospital. Many studies on energy analysis have been published in recent years. [PERSON] et al.