In designing a modern home with a focus on the comfort of residents and energy usage optimization simultaneously

the rise of the Internet of Things and its incorporation with sensor technology plays a vital role these days. The first and foremost task is to predict the energy consumption based on the available data. This study investigates the integration of artificial neural networks in smart home technology to improve energy usage prediction and efficiency

without compromising the comfort of occupants. A dynamic model based on an artificial neural network model is designed in this study which artificially controls light

heating process

and cooling to cut down energy wastage. Energy consumption data of 114 single-family apartments was collected between 2014 and 2016. Energy consumption is predicted by the current model with an accuracy of up to 99.9% for energy usage patterns

which helps to optimize resource management in real time. A robust modeling approach i.e. multi-layer perceptron networks was implemented along with energy usage data. Seventy percent of the data is used for training the neural networks

and the rest is used for testing and validation purposes. The current defined model shows a significant improvement in prediction accuracy of energy usage and efficiency when compared to state-of-the-art models. Metrics such as R-values and mean square error are employed to check accuracy. These results show the essential role of artificial intelligence in improving energy management for smart buildings

with potential benefits including significant energy usage and loss management to help improve sustainable living.