# Energy Analysis in Green Building via Machine Learning: A Case Study in a Hospital
## 1 Introduction
created a collage of simulation studies on energy efficiency in green buildings [5]. [PERSON] et al. investigated the quality of data transfer in green buildings where data were obtained through energy analysis [6]. [PERSON] et al. estimated energy consumption in various buildings using machine learning in order to improve energy efficiency and sustainability [7]. [PERSON] et al. studied and analyzed the energy consumption of hotel buildings by establishing an SVM energy consumption prediction model [8]. This work found that, although the SVM model shows a high prediction accuracy, in the modeling process, the energy consumption of the buildings is greatly affected by factors such as the flow of people. [PERSON] et al. estimated energy consumption in public buildings in China [9]. [PERSON] et al. published a study to predict the cost performance of a building with SVM [10]. [PERSON] et al. conducted a study to predict energy consumption in a low-cost building [11]. [PERSON] et al. predicted the hourly cooling load of a building in their study [12]. [PERSON] et al. used parallel SVM to predict energy consumption [13]. Additionally, various analysis studies using SVM are also available in the literature [14]. It can be seen that the SVM method is generally used for energy analysis and estimation in buildings. We will use the LSTM method in this study. There are also studies involving the LSTM method in energy analysis in buildings. [PERSON] et al. performed an LSTM-based long-term energy prediction in their paper [15]. [PERSON] et al. published a study to improve energy consumption prediction using LSTM [16]. [PERSON] et al. worked on energy prediction of industrial robots on LSTM [17]. [PERSON] et al. predicted residential energy consumption with LSTM in their paper [18]. A summary of studies on various predictions, comparisons, and similar works using different machine learning models is listed in Table 1.  
When it comes to big data in modeling studies, classical methods may not be sufficient. For this reason, we used machine learning methods in this study. We aim to analyze the energy data in a hospital with green building status by interpreting it with one of the machine learning techniques, LSTM. There are many similar studies in the literature on related topics. However, to the authors' knowledge and experiences during this study, research based on the LSTM method to analyze energy in green buildings was not met. There are many studies on energy analysis in green buildings [19; 20; 21; 22; 23; 24; 25], but these studies include analysis with methods other than artificial intelligence or machine learning techniques and address building types far from the hospital example in this study. In our study, three main subjects stand out. These are LSTM, green building, and energy analysis. Generally, studies in this field do not include all three areas. In many studies, energy analysis performed with machine learning characterizes cities, suburbs, or a specific region. Some studies focused on energy analysis in green buildings, but machine-learning techniques were not used. Additionally, articles related to green buildings generally focus on the building's structure, insulation, and material type. The difference in our study from others is that it collects the topics of machine learning method LSTM, green building, and energy analysis in a single article and presents a suitable study. In this study, energy data taken from a hospital in Istanbul was analyzed with some LSTM machine-learning methods. The time period used in the study is 14 March-11 June 2023. The reason for choosing this interval is that it is one of the most intensive periods of the year when the hospital and its devices operate. After debugging and simplifying the data, a data set consisting of 779,280 rows and 102 columns remained. With the analysis studies carried out on this data set, the change in the harmonic values of the hospital was observed. Total power factor (TPF) values were estimated with these methods.