Table of Contents Author Guidelines Submit a Manuscript
Journal of Control Science and Engineering
Volume 2015 (2015), Article ID 174203, 7 pages
http://dx.doi.org/10.1155/2015/174203
Research Article

Power Forecasting of Combined Heating and Cooling Systems Based on Chaotic Time Series

1School of Energy and Power Engineering, Shandong University, Jinan 250061, China
2School of Mechanical, Electrical & Information Engineering, Shandong University at Weihai, Weihai 264209, China

Received 21 October 2014; Revised 2 May 2015; Accepted 4 May 2015

Academic Editor: Kalyana C. Veluvolu

Copyright © 2015 Liu Hai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Theoretic analysis shows that the output power of the distributed generation system is nonlinear and chaotic. And it is coupled with the microenvironment meteorological data. Chaos is an inherent property of nonlinear dynamic system. A predicator of the output power of the distributed generation system is to establish a nonlinear model of the dynamic system based on real time series in the reconstructed phase space. Firstly, chaos should be detected and quantified for the intensive studies of nonlinear systems. If the largest Lyapunov exponent is positive, the dynamical system must be chaotic. Then, the embedding dimension and the delay time are chosen based on the improved C-C method. The attractor of chaotic power time series can be reconstructed based on the embedding dimension and delay time in the phase space. By now, the neural network can be trained based on the training samples, which are observed from the distributed generation system. The neural network model will approximate the curve of output power adequately. Experimental results show that the maximum power point of the distributed generation system will be predicted based on the meteorological data. The system can be controlled effectively based on the prediction.