Forecasting Solar Power Using Wavelet Transform Framework Based on ELM

Forecasting solar power with good precision is necessary for ensuring the reliable and economic operation of electricity grid. In this paper, we consider the task of predicting a given day photovoltaic power (PV power) outputs in 30 min intervals from previous solar power and weather data. We proposed a method combining extreme learning machine and wavelet transform (WT-ELM), which build a separation prediction model for every moment using weather data and corresponding PV power, the weather characteristics are treated as input features and the PV power data are the corresponding ground truth. In addition, we also compared our method with K Nearest Neighbour (K-NN) and support vector machine (SVM) based on clustering using the same data. Then we evaluated the performance of our approach for all data with different time interval. The results show that our result performs much more better than KNN based on clustering.

[1]  F. Hocaoglu,et al.  Hourly solar radiation forecasting using optimal coefficient 2-D linear filters and feed-forward neural networks , 2008 .

[2]  Qinghua Zhang,et al.  Wavelet networks , 1992, IEEE Trans. Neural Networks.

[3]  I. Daubechies.  Orthonormal bases of compactly supported wavelets II: variations on a theme , 1993 .

[4]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[5]  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).

[6]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[7]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[8]  A. Bensoussan,et al.  Forecasting of daily global solar radiation using wavelet transform-coupled Gaussian process regression: Case study in Spain , 2016, 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia).

[9]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[10]  C. K. Chan,et al.  Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN , 2011 .

[11]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  X. Wen,et al.  A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset , 2016 .

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

[14]  Yagyensh C. Pati,et al.  Analysis and synthesis of feedforward neural networks using discrete affine wavelet transformations , 1993, IEEE Trans. Neural Networks.

[15]  I. Daubechies,et al.  PAINLESS NONORTHOGONAL EXPANSIONS , 1986 .

[16]  S. Mallat.  Multiresolution approximations and wavelet orthonormal bases of L^2(R) , 1989 .

[17]  Zheng Wang,et al.  Clustering based methods for solar power forecasting , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).