Smart Temperature Control Using Neuro-Fuzzy Model

Abstract

The temperature control model employs a neuro-fuzzy approach with a defined universe of discourse encompassing temperature (20℃-50℃), humidity (30%-90%), and fan speed (20%-70%). Membership functions were established, utilizing generalized bell functions for temperature and humidity, along with trapezoidal functions for fan speed. A rule base comprising nine rules was developed, incorporating temperature and humidity as linguistic input variables and fan speed as the linguistic output variable. In the data preprocessing phase using Python, 60% of the dataset was designated for training, while 40% was set aside for testing with the scikit-learn model. A convolutional neural network (CNN) was created using TensorFlow’s Keras API, featuring 64 neurons, ReLU activation, and two input shape features. The model underwent training for 100 epochs with the Adam optimizer and a batch size of 16, achieving a training loss of 0.9951 and a test loss of 1.0239. The closely matched and relatively low values of both training and test loss indicate that the model is not overfitting and has successfully captured the underlying patterns. For instance, when the current temperature and humidity were set to 35℃ and 65%, the recommended fan speed was 48%. Moreover, predicted fan speeds were 20.14%, 35.21%, and 43.64% for temperature and humidity settings of (35℃, 45%), (45℃, 75%), and (55℃, 85%), respectively.