A Review on Energy-Efficient Smart Home Load Forecasting Techniques

Abstract

The aim of this study survey is to analyze energy-efficient smart home load forecasting techniques and determine the usage of energy or power with high spectrum allocation in future smart home with the help of clustering in data mining. The study work starts presenting an overview of the smart home energy sector and the challenges it is facing; it is observed a change on the energy policies promoting the energy efficiency, encouraging an active role of the consumer, instructing them about the importance of the consumer behavior and protecting consumer rights. Electricity is gaining room as energy source; its share will keep increasing constantly in the following decades. In this close future, smart homes and smart meters' deployment will benefit both the utility and the consumer. In this environment, new services and new business appear, focusing on the energy management field and tools, they require specialization in fields such as, computer science, software development and data science. This study work has segmented the smart home according to the similarities of their electrical load profiles, using the proportion of energy usage per hour (%) as a common framework with analysis done in this proposed research. The objective behind this energy consumption segmentation is to be able to provide personalized recommendations to each group to reduce their energy consumption and the associated costs, fostering energy efficiency measures and improving the consumer engagement for future smart homes.