2.4. Developed Long-Short Term Memory (LSTM) Model
In the developed study, the data collectors shown in Figure 1, located in 3 different locations, transferred the humidity and temperature data of the environment they were in for six months to the server. Since the locations are used for different purposes, it is seen that the temperature and humidity values ​​remain within certain ranges. It is seen that the first location has 0-15° degrees, the second location has 15-25° degrees, and the third location has 23-30° degrees. There are six different classifications for heating and cooling settings within the model. It has datasets containing only two classifications in three locations. Modeling of systems that take such narrow data ranges and classification with artificial intelligence systems will be limited. The model learned with data from a single location will not produce correct results when it encounters data from other classifications. For this reason, in our study, it is aimed to train the data of different locations with narrow data ranges on central servers and to transfer their weights to all locations. Thus, classification will be performed against a temperature and humidity value encountered for the first time in the number one location, and the system will continue to operate without any problems. The data obtained after the data collection process was stored on the central server. As given in Table 2, six different order classifications have been determined for the operation of the heating and cooling system needed according to the ranges of temperature and humidity values.
Table 2. Temperature ranges and classification groups to be used for model training