An Intelligent Energy Aware Approaches for Smart Appliances

Abstract

Energy consumption is rising rapidly due to population proliferation, urbanization, and industrialization. Residential electricity demand is increasing rapidly, constituting about a quarter of total energy consumption. A large amount of energy can not be accumulated or transported; therefore, energy supply should be synchronized with consumption. With the advancement in information communication technology and rapid escalation in population, IoT paradigm has been adopted in homes for managing and optimizing energy consumption of home appliances. Electricity demand prediction is one of the sustainable solutions to improve energy efficiency in real-world scenarios. The non-linear and nonstationary consumption patterns in residential buildings make electricity prediction more challenging. Initially, comprehensive literature has been studied to explore energy-aware prediction and optimization techniques. The energy-aware prediction models have been reviewed and classified into machine learning and deep learning algorithms. From the literature, it has been inferred that non-linear and non-stationary consumption patterns in residential buildings make electricity prediction more challenging. The prediction models can be integrated with optimization approaches to optimize the predictive performance in residential buildings. Therefore, there is a need to develop an intelligent energy prediction and optimization approach for IoT-based smart homes and buildings. A multi-step prediction approach based on decomposition, reconstruction, and prediction models has been proposed to address these issues. Firstly, cluster analysis has been conducted to identify seasonal consumption patterns. Secondly, an improved CEEMDAN method and autoencoder model have been deployed to remove irregular patterns, noise, and redundancy from electricity load time series. Finally, the LSTM model and Bidirectional LSTM have been trained to predict electricity consumption by considering historical, seasonal, and temporal data dependencies. Further