Comparison of Adaptive Neuro-Fuzzy Inference Systems and Echo State Networks for PV Power Prediction

Abstract Use of photo-voltaic (PV) power as a source of electricity has been rapidly growing. Integration of large PV power into the grid operation introduces several challenges. Uncertainty of PV power generation causes frequency fluctuations and power system instabilities. Due to this, short term PV power prediction has become an important area of study. Short term PV power prediction supports proper decision-making in control centers. Power generation output of a PV plant is highly dependent on different weather conditions such as solar irradiance, temperature and cloud covers. Weather data analysis and prediction can be considered as big data due to its complexity and dynamically changing characteristics. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is developed and compared with an echo state network (ESN) for short term PV power prediction. The ANFIS approach consists of three ANFIS modules for predicting solar irradiance and temperature, and then estimating the PV power, respectively. The ESN on the other hand predicts the PV power based on current weather parameters. A weather database containing data sampled every second is used in developing the ANFIS and ESN based PV power prediction systems. Results are compared under different Clemson, SC weather conditions with the two approaches.

[1]  Ping Wang,et al.  Application of Pattern Recognition and Artificial Neural Network to Load Forecasting in Electric Power System , 2007, Third International Conference on Natural Computation (ICNC 2007).

[2]  Haralambos Sarimveis,et al.  Prediction of daily global solar irradiance on horizontal surfaces based on neural-network techniques , 2008 .

[3]  Hui Shi,et al.  Research on short-term power load time series forecasting model based on BP neural network , 2010, 2010 2nd International Conference on Advanced Computer Control.

[4]  M. A. Hashim,et al.  New forecasting model using type-2 fuzzy multivariate time series , 2011, 2011 National Postgraduate Conference.

[5]  Rong Luo,et al.  Energy efficient neural networks for big data analytics , 2014, 2014 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[6]  Elif Derya Übeyli,et al.  Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients , 2005, Journal of Neuroscience Methods.

[7]  Zainab H. Osman,et al.  Neural network based approach for short-term load forecasting , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[8]  Hasimah Abdul Rahman,et al.  A Novel Hybrid Model for Short-Term Forecasting in PV Power Generation , 2014 .

[9]  Ganesh K. Venayagamoorthy,et al.  Optimal fuzzy logic based coordination controller for improved transient stability of a smart grid , 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[10]  Xin Miao,et al.  The opportunity and challenge of Big Data's application in distribution grids , 2014, 2014 China International Conference on Electricity Distribution (CICED).

[11]  Girish Kumar Singh,et al.  Solar power generation by PV (photovoltaic) technology: A review , 2013 .

[12]  Montserrat Mendoza-Villena,et al.  Short-term power forecasting system for photovoltaic plants , 2012 .

[13]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[14]  Ganesh K. Venayagamoorthy,et al.  Comparison of echo state network and extreme learning machine for PV power prediction , 2014, 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG).

[15]  Shijie Cheng,et al.  Study of the key technologies of electric power big data and its application prospects in smart grid , 2014, 2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[16]  G. Venayagamoorthy,et al.  Reservoir based learning network for control of two-area power system with variable renewable generation , 2015, Neurocomputing.

[17]  M. M. Gupta.  Fuzzy logic, neural networks and virtual cognitive systems , 1993, 1993 (2nd) International Symposium on Uncertainty Modeling and Analysis.

[18]  H. Bevrani,et al.  A fuzzy inference model for short-term load forecasting , 2012, 2012 Second Iranian Conference on Renewable Energy and Distributed Generation.