Smart Home Prototype for Rain Prediction Using Artificial Neural Network Algorithm

Unpredictable weather changes, such as sudden rain, can disrupt daily activities, particularly tasks like drying clothes. To address this challenge, this study presents a smart home system that forecasts rain using a trained Artificial Neural Network (ANN) algorithm. The system employs IoT sensors to collect five critical weather parameters—temperature, humidity, air pressure, wind speed, and light intensity. Data from these sensors are used to train the Backpropagation ANN model. The practical application of this model is demonstrated through a prototype that integrates a smart house roof and a servo-driven clothesline system. When rain is predicted, the servo moves the clothesline under the roof; otherwise, it positions the clothesline outside. The system achieved an accuracy of 0.86334, validating its effectiveness in mitigating the impact of sudden weather changes on clothes drying.