Smart grid components such as smart home and battery energy management systems, high penetration of renewable energy systems, and demand response activities, require accurate electricity demand forecasts for the successful operation of the electricity distribution networks.
Smart meter data can be used for various purposes within smart grids, including residential energy applications, such as Home Energy Management Systems (HEMS) and Battery Energy Management Systems (BEMS).
Methods and techniques for using smart meter data are analysed; forecasting clustering, classification and optimization.
End use applications of smart meter data are reviewed.
Performance of state of the art models are compared.
Challenges associated with methods and application are identified.
A new analysis guideline is proposed.
The uptake of smart grid technologies and increasing deployment of smart meters have brought greater attention on the analysis of individual household electricity consumption. Within the smart grid framework, home and battery energy management systems are becoming important demand side management tools with various benefits to households, utilities and networks. Load forecasting is a vital component of these tools, as it can be used in optimizing the schedule of household appliances and energy operations.