Title :
Application of time-series and Artificial Neural Network models in short term load forecasting for scheduling of storage devices
Author :
Ahmed, K.M.U. ; Ampatzis, Michail ; Nguyen, P.H. ; Kling, W.L.
Author_Institution :
Dept. of Electr. Eng., Tech. Univ. Eindhoven, Eindhoven, Netherlands
Abstract :
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 benefit of proper storage scheduling is demonstrated via a use-case. The work is a part of a project focused on photovoltaic generation with integrated energy storage at household level. The methods under study attempt to capture the daily electricity consumption profile of an individual household.
Keywords :
autoregressive moving average processes; energy storage; load forecasting; neural nets; power consumption; power engineering computing; power generation scheduling; power markets; smart power grids; solar cells; time series; ANN; ARIMA model; DER; artificial neural network model; autoregressive integrated moving average model; distributed energy resource; household daily electricity consumption forecasting; integrated energy storage; market integration; photovoltaic generation; renewable energy; residential energy storage device scheduling; short term load forecasting; smart grid; time series application; Artificial neural networks; Biological system modeling; Electricity; Forecasting; Load modeling; Predictive models; Time series analysis; Artificial Neural Network (ANN); AutoRegressive Integrated Moving Average (ARIMA); machine learning; short-term load forecasting (STLF); storage device scheduling;
Conference_Titel :
Power Engineering Conference (UPEC), 2014 49th International Universities
Conference_Location :
Cluj-Napoca
Print_ISBN :
978-1-4799-6556-4
DOI :
10.1109/UPEC.2014.6934761