Photovoltaic Power Prediction Using Recurrent Neural Networks

The intermittent characteristic of the photovoltaic power, due to the variability of the weather conditions, involves many problems in grid energy management. Therefore, the PV power forecasting becomes crucial to ensure grid stability and economic dispatch. Artificial neural network (ANN) techniques present alternative approaches to solve nonlinear problems in various areas. They can be trained and applied for prediction. A particular type of ANN namely the recurrent neural network (RNN) has shown powerful capabilities for PV power forecasting. The paper investigates and compares the efficiency of several RNN architectures specifically the modified Elman, Jordan and the hybrid model combining the latest topologies.

[1]  Otilia Elena Dragomir,et al.  Forecasting of Photovoltaic Power Generation by RBF Neural Networks , 2014 .

[2]  Yanchun Liang,et al.  Identification and control of nonlinear systems by a dissimilation particle swarm optimization-based Elman neural network , 2008 .

[3]  Jun Liu,et al.  Photovoltaic power forecasting based on artificial neural network and meteorological data , 2013, 2013 IEEE International Conference of IEEE Region 10 (TENCON 2013).

[4]  Luigi Piroddi,et al.  Jordan recurrent neural network versus IHACRES in modelling daily streamflows , 2008 .

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

[6]  Luca Delle Monache,et al.  Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble , 2017 .

[7]  Xu Li,et al.  A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks , 2015 .

[8]  Eric Wai Ming Lee,et al.  Short-term prediction of photovoltaic energy generation by intelligent approach , 2012 .

[9]  Maciej Ławryńczuk,et al.  Elman neural network for modeling and predictive control of delayed dynamic systems , 2016 .

[10]  Wei Zhou,et al.  A novel model for photovoltaic array performance prediction , 2007 .

[11]  Duc Truong Pham,et al.  Training of Elman networks and dynamic system modelling , 1996, Int. J. Syst. Sci..

[12]  Jun Wang,et al.  Financial Time Series Prediction Using Elman Recurrent Random Neural Networks , 2016, Comput. Intell. Neurosci..

[13]  Marie-Francine Moens,et al.  A survey on the application of recurrent neural networks to statistical language modeling , 2015, Comput. Speech Lang..

[14]  Francesco Grimaccia,et al.  A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output , 2015 .

[15]  O. S. Babalola,et al.  Photovoltaic Generating System Parameter Sizing for Building , 2014 .

[16]  R. Sathya,et al.  Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification , 2013 .

[17]  Jun Ren,et al.  Using Genetic Programming with Prior Formula Knowledge to Solve Symbolic Regression Problem , 2015, Comput. Intell. Neurosci..

[18]  Aminmohammad Saberian,et al.  Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks , 2014 .

[19]  S. A. Abdulkarim.  Time series prediction with simple recurrent neural networks , 2016 .

[20]  A. Mellit,et al.  A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy , 2010 .

[21]  Rozaida Ghazali,et al.  Jordan Pi-Sigma Neural Network for Temperature Prediction , 2011, UCMA.

[22]  Murat Akcin,et al.  A photovoltaic system model for Matlab/Simulink simulations , 2013, 4th International Conference on Power Engineering, Energy and Electrical Drives.

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

[24]  Soteris A. Kalogirou,et al.  Artificial intelligence techniques for photovoltaic applications: A review , 2008 .

[25]  Maria Grazia De Giorgi,et al.  Photovoltaic power forecasting using statistical methods: impact of weather data , 2014 .