# Energy Analysis in Green Building via Machine Learning: A Case Study in a Hospital
## 4 Conclusions  
Hospitals that provide green building features not only have devices that draw high power but also have systems that reduce the effects of these features. Therefore, precise measurement and analysis are extremely important in such buildings. In previous studies, the effects of different LSTM methods on energy analysis along with different machine learning techniques have been examined. The most appropriate analysis method varies depending on whether the place to be studied is a building or a house. When it comes to buildings, different situations, such as whether the building is a public or private building, whether it is a production center or a user center, whether it is crowded or sparse, and the nature of the devices inside, have brought different prediction techniques to the fore. LSTM comes to the fore in buildings that consume high energy. Previous studies have shown that different techniques of LSTM are more suitable for the building being studied. In this study, the LSTM model, which is a sensitive energy analysis method suitable for such structures, was used. The energy data of a hospital were analyzed with five different LSTM models. These are vanilla LSTM, stacked LSTM, bidirectional LSTM, CNN-LSTM, and encoder-decoder LSTM. Different LSTM techniques were applied to different parameters. TPF, which had the lowest error rate among these parameters, was selected as the analysis parameter. In the analysis, the change in TPF value according to days and hours was observed. An estimation study on TPF was also performed through the data set. It has been observed that the prediction and actual results are consistent to a good degree. The effect of five different LSTM models on the results was observed. It was found that the vanilla LSTM model had the highest accuracy and lowest error rate. The error rate was calculated with three different methods. The MAE error rate was calculated as 0.00049273, the MSE error rate was calculated as 0.0000045, and the RMSE error rate was calculated as 0.00067232 for the vanilla LSTM method. With these low error rates obtained, it can be concluded that the vanilla LSTM model is suitable for green buildings with high consumption. In buildings with high-energy-consuming machines, power factor values deviate from the ideal value due to energy fluctuation. This situation prevents the smooth operation of the electrical system. Different techniques are used in buildings to bring the power factor to values close to ideal. The building in question currently has components, such as energy storage devices and compensation panels, to correct this problem. Although the values obtained in this study are acceptable values, the following operations can be applied to improve the system:  
Regional energy analysis can be performed, and local compensation devices can be integrated into the relevant regions for special precautions by determining which units experience density at which hour intervals. Filtering can be performed by considering the total harmonic distortion values. Considering the load diversity of the building in question, it has been observed that an active filtering option is more suitable instead of a simple passive inductor-capacitor filtering. Inductive current values can be balanced by adding power factor correction capacitors and providing regular inspection and preventive maintenance. The energy monitoring system in the building in question receives detailed values only from the main panel and only basic values from regional panels. In order to produce correct solutions according to the regions, there should be an energy analyzer that receives detailed data in each different polylcinic panel in the hospital. In this study, the focus is on which method is more appropriate rather than the seasonal variability of the data. In the further studies to be conducted consecutively, 18 months of data will be used.