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Big Data Analytics and Visualization of Residential Electricity Consumption Behavior based on Smart Meter Data

Detecting unusual electricity usage behavior has grown increasingly crucial in recent years. It is now possible for homeowners to collaborate with utilities in the electricity consumption of their appliances due to the lively growth of IoT-based home appliances and smart meter system. As a result, the proposed work permits homeowners to regulate their home appliances' energy use and compare it to the total consumption of their local community. To increase the stability of the grid and efficiency of the energy, smart meter system is implemented and also to improve customer service. Its goal is to assist utilities and consumers in gaining a better understanding of power usage patterns. Existing research, on the other hand, frequently concentrates on algorithm enhancement while ignoring the process of getting features. In this paper, with an optimal big data mathematical technique, we use a computational cluster which is based on Hadoop Distributed File System (HDFS). To gain access to individual homes, more smart meter data are simulated. For managing energy big data, utility companies can use a computing cluster and distributed storage to undertake customer load analysis and visualization on a larger scale.