A comprehensive review on the application of machine learning techniques for analyzing the smart meter data

Abstract

The deployment of smart meters has developed through technical developments. A fine-grained analysis and interpretation of metering data is important to deliver benefits to multiple stakeholders of the smart grid. The deregulation of the power industry, particularly on the distribution side, has been continuously moving forward worldwide. How to use broad smart meter data to improve and enhance the stability and efficiency of the power grid is a critical matter. So far, extensive work has been done on smart meter data processing. This chapter provides a thorough overview of the current research outcomes for the study of smart meter data using machine learning techniques. An application-oriented analysis is being addressed. The main applications, such as load profiling, load forecasting and load scheduling, are taken into account. A summary of the state-of-the-art machine learning-based methodologies, customized for each intended application, is provided.