Enhanced Energy Consumption Prediction in Smart Homes through Hybridized Prophet Algorithm with Adaptive Optimization Techniques

Abstract

The escalating integration of smart homes with smart grids underscores the critical need for precise and timely predictions of energy consumption, essential for optimizing resource allocation and bolstering overall energy efficiency. This research work pioneers an innovative approach to enhance energy consumption predictions within smart homes by seamlessly integrating the robust time series forecasting capabilities of the Prophet algorithm with adaptive optimization techniques – ADAM (Adaptive Moment Estimation), SGD, ADAGRAD, and RMSPROP. Prophet's inherent proficiency in handling daily patterns and seasonality is further amplified by the adaptability conferred by optimization algorithms, addressing the intricate dynamics of non-linear patterns inherent in smart home energy consumption. Utilizing the extensive Pecan dataset, encompassing historical energy consumption of various appliances in a smart home, the proposed hybridized model undergoes rigorous evaluation against traditional Prophet and baseline models. Metrics such as Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) serve as comprehensive benchmarks for assessing the model's performance. The hybridized model demonstrates a notable enhancement in accuracy and efficiency in predicting energy consumption, marking a substantial contribution to the ongoing evolution of energy management practices within smart homes connected to smart grids. As smart homes continue their trajectory of evolution, the primary aim of this research is to foster sustainable energy practices and optimize resource utilization, aligning with the ethos of smart living.