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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 260351, 6 pages
http://dx.doi.org/10.1155/2013/260351
Research Article

Photovoltaic Power Prediction Based on Scene Simulation Knowledge Mining and Adaptive Neural Network

School of Economics and Management, North China Electric Power University, Beijing 102206, China

Received 20 March 2013; Revised 3 September 2013; Accepted 3 September 2013

Academic Editor: Chuandong Li

Copyright © 2013 Dongxiao Niu 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

Influenced by light, temperature, atmospheric pressure, and some other random factors, photovoltaic power has characteristics of volatility and intermittent. Accurately forecasting photovoltaic power can effectively improve security and stability of power grid system. The paper comprehensively analyzes influence of light intensity, day type, temperature, and season on photovoltaic power. According to the proposed scene simulation knowledge mining (SSKM) technique, the influencing factors are clustered and fused into prediction model. Combining adaptive algorithm with neural network, adaptive neural network prediction model is established. Actual numerical example verifies the effectiveness and applicability of the proposed photovoltaic power prediction model based on scene simulation knowledge mining and adaptive neural network.