Optimizing Energy Prediction in Smart Home Area Networks and Buildings Using Artificial Neural Networks and Machine Learning Techniques

Abstract

Smart home area networks (HANs) and buildings have become increasingly popular in recent years, with the integration of various smart devices into these networks. However, managing energy consumption in these networks is a major challenge. In this paper, we propose a hybrid artificial neural network-based energy prediction model to predict energy consumption of smart devices in HANs and smart buildings. Our proposed model utilizes a combination of artificial neural networks (ANNs) and machine learning (ML) techniques to predict energy consumption in smart HANs and buildings. The ANN component of the model is used to model the complex relationships between different variables, while the ML component is used to improve the accuracy of the predictions. To evaluate the performance of our proposed model, we collected data from a smart building and a smart HAN. Our results show that the proposed model outperforms traditional prediction methods, with an average prediction error of less than 3%. The proposed model can be used to optimize energy consumption in smart HANs and buildings, by providing accurate predictions of energy consumption. This can help to reduce energy costs and improve the overall energy efficiency of these networks. Additionally, the proposed model can be easily adapted to other types of smart networks, such as smart cities and industrial networks