Time Series Analysis with Machine Learning for Smart Meter Data Based Appliances Categorization

As technology has advanced, the trend of deploying smart meters instead of conventional ones is rapidly growing. These smart meters are essential parts of intelligent grids, offering significant advantages to many stakeholders regarding social, environmental, and economic constraints. A detailed method of fine-grained collection and analysis of the metering data is necessary to benefit the different smart grid stakeholders. This results in collecting, transmitting, and processing a large amount of data with an exponentially raised dimension. It seriously constrains the system processing resources, latency, data management, transmission effectiveness, and analysis response time. In this regard, a new hybridization of machine learning and time series analysis based feature extraction is suggested for the automatic classification of appliances through the processing of their power consumption smart meter data. This feature extraction makes up for the previously noted deficiency and can result in a notable data dimension reduction without sacrificing important information. Data in compressed form can therefore be processed, examined, stored, and transmitted with efficiency. It offers a significant reduction in the post classification latency along with computational and transmission effectiveness. The classification is carried out using the robust machine learning (ML) classifiers. The performance of considered ML classifiers is evaluated for the case of a real multi-class and multi-device smart meter dataset. The devised solution achieves a highest average classification accuracy of 98.00% for a nine-class problem while securing a dimension reduction of 4.091 fold.