IOT BASED SMART METER DATA ANALYSIS USING MACHINE LEARNING CLASSIFIERS FOR ENERGY CONSUMPTION PREDICTION

Abstract

The emergence of Internet of Things (IoT) technology has revolutionized various sectors, including the energy industry. In particular, IoT-based smart meters have gained significant attention due to their ability to provide real-time data on energy consumption. These smart meters offer a plethora of data, which can be leveraged to optimize energy usage, improve efficiency, and reduce costs. One promising avenue is the application of machine learning techniques to analyze smart meter data and predict energy consumption patterns accurately. Traditionally, energy consumption monitoring relied on manual meter readings, which were often infrequent and prone to human errors. This approach limited the ability to obtain timely insights into energy usage patterns, leading to inefficiencies in energy management. Moreover, traditional systems lacked the capability to adapt to changing consumption patterns or provide proactive suggestions for optimization. The challenge is the sheer volume and complexity of the data generated by IoT devices, which often requires sophisticated analytical techniques to extract meaningful insights. Additionally, traditional regression-based approaches may not fully capture the nonlinear relationships and temporal dynamics present in energy consumption data. The system aims to address these challenges by leveraging machine learning classifiers for energy consumption prediction based on IoT smart meter data. By integrating advanced analytics with IoT technology, the proposed system offers several key advantages. Firstly, machine learning algorithms can capture complex patterns and relationships within the data, leading to more accurate consumption predictions. Secondly, real-time analysis enables proactive decision-making and optimization strategies. Thirdly, by employing techniques such as anomaly detection, the system can identify and mitigate potential issues, thereby enhancing efficiency and reliability