Forecasting Photovoltaic Power using a Convolutional Neural Network and the Wavelet Transformation

In order to reduce the pressure caused by the precipitous depletion of natural resources and realize the sustainable development, renewable energy resource is playing a more and more significant role with an exponential growth speed in the smart grid, especially for solar energy. Given the stochastic characteristics of the Photovoltaic (PV) generation and to maintain the security and reliability of the power grid operation, finding a forecasting method with higher accuracy is a pressing need. Facing with this challenge, a novel prediction model that combines Convolutional Neural Network with Wavelet Decomposition (CNN+WD) is presented in this paper. WD technique is aimed to decompose the historical photovoltaic power data into several wavelets with different frequencies. CNN is used to train the decomposed wavelets separately and predict the future output PV power. Three other kinds of models are investigated to compare with the presented approach. The performance is respectively assessed for 15mins, 30mins, 45mins, 60mins, 90mins and 120mins ahead power forecasting in deterministic prediction. Numerical results presented in simulations show that the proposed model has good reliability and high accuracy in comparison with other models selected for this study.

[1]  Chao-Ming Huang,et al.  A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output , 2014, IEEE Transactions on Sustainable Energy.

[2]  Yitao Liu,et al.  Deep learning based ensemble approach for probabilistic wind power forecasting , 2017 .

[3]  Yonghua Song,et al.  Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach , 2017, IEEE Transactions on Power Systems.

[4]  Yan-Fang Sang,et al.  A Practical Guide to Discrete Wavelet Decomposition of Hydrologic Time Series , 2012, Water Resources Management.

[5]  Tara L. Jensen,et al.  Forecasting ground-level irradiance over short horizons: Time series, meteorological, and time-varying parameter models , 2017 .

[6]  Fang Cui,et al.  Short-term wind speed forecasting using the wavelet decomposition and AdaBoost technique in wind farm of East China , 2016 .

[7]  Irena Koprinska,et al.  Univariate and multivariate methods for very short-term solar photovoltaic power forecasting , 2016 .

[8]  Yitao Liu,et al.  Deep belief network based deterministic and probabilistic wind speed forecasting approach , 2016 .

[9]  Mladen Kezunovic,et al.  Islanding Detection for Inverter-Based Distributed Generation Using Support Vector Machine Method , 2014, IEEE Transactions on Smart Grid.

[10]  Xiyun Yang,et al.  Photovoltaic power forecasting with a rough set combination method , 2016, 2016 UKACC 11th International Conference on Control (CONTROL).

[11]  Yash Pal,et al.  Photovoltaic power forecasting methods in smart power grid , 2015, 2015 Annual IEEE India Conference (INDICON).