A wavelet-based method for high resolution multi-step PV generation forecasting

Forecasts play a vital role in maintaining power system stability and maximizing economic benefits of distributed energy resources. The issue with PV generation forecasting is that it relies on forecasts of solar irradiation which, due to the complex, nonlinear relationship between humidity, pressure, temperature, and cloud transients, can be quite difficult to model. Two important decisions in the forecasting process are selection of the forecasted variable (model output) and selection of explanatory variables (model inputs). This paper proposes a new method to forecast PV generation using wavelet based input selection and an output variable that directly represents clouds transients. We model this cloud effect by first determining a clear sky model (CSM) and forecasting the difference between the CSM and actual measurements of global horizontal irradiance (GHI). Potential model inputs are first decomposed using wavelet multi resolution analysis and final input selection is based on the correlation between the inputs and output at various timescales. Two separate neural network structures are designed to separately forecast sunny and cloudy days. Using the high resolution forecast of GHI (20 min increments), the next day's PV generation is determined. This method improves on the persistence method by 69% on sunny days, 26% on cloudy days.

[1]  T. Hoff,et al.  Validation of short and medium term operational solar radiation forecasts in the US , 2010 .

[2]  E. Assareh,et al.  Solar radiation forecasting based on meteorological data using artificial neural networks , 2009, 2009 7th IEEE International Conference on Industrial Informatics.

[3]  Tomonobu Senjyu,et al.  Application of neural network to 24-hour-ahead generating power forecasting for PV system , 2008, PES 2008.

[4]  Shuanghua Cao,et al.  Forecast of solar irradiance using recurrent neural networks combined with wavelet analysis , 2005 .

[5]  Cyril Voyant,et al.  Optimization of an artificial neural network dedicated to the multivariate forecasting of daily glob , 2011 .

[6]  Cyril Voyant,et al.  24-hours ahead global irradiation forecasting using Multi-Layer Perceptron , 2013 .

[7]  Eleonora D'Andrea,et al.  24-hour-ahead forecasting of energy production in solar PV systems , 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

[8]  Bangyin Liu,et al.  Online 24-h solar power forecasting based on weather type classification using artificial neural network , 2011 .

[9]  Lei Wang,et al.  An ANN-based Approach for Forecasting the Power Output of Photovoltaic System , 2011 .

[10]  Maria Paula da Costa Couto.  Review of input determination techniques for neural network models based on mutual information and genetic algorithms , 2009, Neural Computing and Applications.

[11]  C. Coimbra,et al.  Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database , 2011 .

[12]  Guilherme De A. Barreto,et al.  A New Look at Nonlinear Time Series Prediction with NARX Recurrent Neural Network , 2006, 2006 Ninth Brazilian Symposium on Neural Networks (SBRN'06).

[13]  Hoay Beng Gooi,et al.  Solar radiation forecast based on fuzzy logic and neural networks , 2013 .