This paper explores the integration of attention networks in the realm of home energy management systems (HEMS) to enhance the robustness and efficiency of energy consumption optimization. With the growing demand for smart grid technologies, the need to achieve demand side response becomes paramount. The proposed solution leverages attention networks to dynamically allocate significance to various aspects of energy consumption patterns, considering the diverse load types and dynamic loading scenarios present in households. In this investigation, we focus on the AMpds2 dataset, characterized by intricate loading patterns, and assess its performance across various time series forecasting methodologies, including (RNN), (LSTM), (TCN), and transformers. Multiple methodologies undergo performance evaluation using diverse hyperparameter combinations. Evaluation metrics, specifically (RMSE) and (MAE), are employed. Advanced optimizers such as (Adam) and (Adamax) are applied, and activation functions, including sigmoid, linear, tanh, and ReLU, are implemented. A comprehensive performance analysis involves 16 hyperparameter combinations across four distinct time series models. Through meticulous scrutiny, it is determined that the utilization of transformers in forecasting energy and load patterns results in a 4% increase in accuracy, as elucidated in the results section. The implementation of this study is carried out on the Python 3.2 platform, and the matplotlib library is employed to visualize the comparison between actual and predicted data.
Keywords: Deep learning; Home energy management systems; Machine learning; Smart grid; Transformers.
© 2024 The Author(s).