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Solar Radiation Time-Series Prediction Based on Empirical Mode Decomposition and Artificial Neural Networks

  • Petros-Fotios Alvanitopoulos
  • Ioannis Andreadis
  • Nikolaos Georgoulas
  • Michalis Zervakis
  • Nikolaos Nikolaidis
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 436)

Abstract

This paper presents a new model for daily solar radiation prediction. In order to capture the hidden knowledge of existing data, a time-frequency analysis on past measurements of the solar energy density is carried out. The Hilbert-Huang transform (HHT) is employed for the representation of the daily solar irradiance time series. A set of physical measurements and simulated signals are selected for the time series analysis. The empirical mode decomposition is applied and the adaptive basis of each raw signal is extracted. The decomposed narrow-band amplitude and frequency modulated signals are modelled by using dynamic artificial neural networks (ANNs). Nonlinear autoregressive networks are trained with the average daily solar irradiance as exogenous (independent) input. The instantaneous value of solar radiation density is estimated based on previous values of the time series and previous values of the independent input. The results are promising and they reveal that the proposed system can be incorporated in intelligent systems for better load management in photovoltaic systems.

Keywords

Solar Radiation Solar Irradiance Empirical Mode Decomposition Integrate Water Resource Management Intrinsic Mode Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Petros-Fotios Alvanitopoulos
    • 1
  • Ioannis Andreadis
    • 1
  • Nikolaos Georgoulas
    • 1
  • Michalis Zervakis
    • 2
  • Nikolaos Nikolaidis
    • 3
  1. 1.Department of Electrical and Computer Engineering, School of EngineeringDemocritus University of ThraceXanthiGreece
  2. 2.Laboratory of Digital Signal & Image Processing, Department of Electronic and Computer EngineeringTechnical University of Crete (TUC)ChaniaGreece
  3. 3.Laboratory of Hydrochemical Engineering and Remediation of Soil (HERSLab), Dept. of Environmental EngineeringTechnical University of Crete (TUC)ChaniaGreece

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