In the context of the smart grid, scheduling residential energy storage device is necessary to optimize technical and market integration of distributed energy resources (DERs), especially the ones based on renewable energy. The first step to achieve proper scheduling of the storage devices is electricity consumption forecasting at individual household level. This paper compares the forecasting ability of Artificial Neural Network (ANN) and AutoRegressive Integrated Moving Average (ARIMA) model. The performance evaluation of storage devices scheduling is demonstrated via different use-cases. The work is a part of a project focused on photovoltaic generation with integrated energy storage at household level. The ultimate goal of this research is to ensure end-use participation at local market to trade surplus of stored energy.
|Date of Award||31 Aug 2014|
|Supervisor||Phuong H. Nguyen (Supervisor 1) & Hans Edin (External coach)|