Authors: Xiang Zheng; Xiong Chen
Addresses: Intelligent Control Research Lab, Fudan University, Shanghai 200433, China ' Intelligent Control Research Lab, Fudan University, Shanghai 200433, China
Abstract: The production of energy from renewable energy systems (RES) arrays has the properties of nonlinearity and uncertainty and usually fluctuates with the changes of weather and other factors. In this paper, an integrated method based on case-based reasoning (CBR), empirical mode decomposition (EMD) and artificial neural network (ANN) is proposed to predict the productivity of the RES. At first, the non-stationary time series is pre-processed by CBR to find out the ten days with the most similarity to the predicted day. Then uses EMD decomposed times series of ten days we selected into a series of IMFs (Intrinsic Mode Functions) with features of stationarity and multiple time scale, for each IMF component, constructing models of ANN to predict. Finally would be straight line fit to final predict result. The simulation results show that the productivity predicted by using this integrated algorithm is more accurate than that of single artificial neutral network model.
Keywords: renewable energy; productivity prediction; case-based reasoning; CBR; empirical mode decomposition; EMD; artificial neural networks; ANNs; short-term wind power; wind energy; nonlinearity; uncertainty; simulation.
International Journal of Wireless and Mobile Computing, 2016 Vol.10 No.4, pp.378 - 385
Received: 28 Mar 2016
Accepted: 21 Apr 2016
Published online: 29 Jul 2016 *