Solar power forecasting using weather type clustering and ensembles of neural networks

We consider the task of forecasting the electricity power generated by a photovoltaic solar system, for the next day at half-hourly intervals. The forecasts are based on previous power output and weather data, and weather prediction for the next day. We present a new approach that forecasts all the power outputs for the next day simultaneously. It builds separate prediction models for different types of days, where these types are determined using clustering of weather patterns. As prediction models it uses ensembles of neural networks, trained to predict the power output for a given day based on the weather data. We evaluate the performance of our approach using Australian photovoltaic solar data for two years. The results showed that our approach obtained MAE=83.90 kW and MRE=6.88%, outperforming four other methods used for comparison.

[1]  Irena Koprinska,et al.  2D-interval forecasts for solar power production , 2015 .

[2]  Paras Mandal,et al.  Forecasting Power Output of Solar Photovoltaic System Using Wavelet Transform and Artificial Intelligence Techniques , 2012, Complex Adaptive Systems.

[3]  Irena Koprinska,et al.  Forecasting solar power generated by grid connected PV systems using ensembles of neural networks , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[4]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[5]  Daren Ler.  Utilising Regression-based Landmarkers within a Meta-learning Framework for Algorithm Selection , 2005 .

[6]  H. Pedro,et al.  Assessment of forecasting techniques for solar power production with no exogenous inputs , 2012 .

[7]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Germano C. Vasconcelos,et al.  MLP ensembles improve long term prediction accuracy over single networks , 2011 .

[9]  Dazhi Yang,et al.  Very short term irradiance forecasting using the lasso , 2015 .

[10]  Francisco Martinez Alvarez,et al.  Energy Time Series Forecasting Based on Pattern Sequence Similarity , 2011, IEEE Transactions on Knowledge and Data Engineering.

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

[12]  T. Caliński,et al.  A dendrite method for cluster analysis , 1974 .

[13]  Yongping Yang,et al.  Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines , 2012 .

[14]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[15]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[16]  Carlos F.M. Coimbra,et al.  Short-term reforecasting of power output from a 48 MWe solar PV plant , 2015 .

[17]  W. Marsden.  I and J , 2012 .

[18]  Irena Koprinska,et al.  Combining pattern sequence similarity with neural networks for forecasting electricity demand time series , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[19]  Peng Wang,et al.  Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines , 2011, IEEE Transactions on Industry Applications.

[20]  Irena Koprinska,et al.  Correlation and instance based feature selection for electricity load forecasting , 2015, Knowl. Based Syst..

[21]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[22]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[23]  Yan Su,et al.  Analysis of daily solar power prediction with data-driven approaches , 2014 .

[24]  undefined Manoël Rekinger,et al.  Global Market Outlook for Solar Power 2015-2019 , 2014 .

[25]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .