A comparative study on short-term PV power forecasting using decomposition based optimized extreme learning machine algorithm

Abstract Solar irradiance fluctuates within a very short period of time that creates a lot of hindrances to estimate the injection of output power into the grid. During the operation of solar power plant, short-term PV power forecasting supports load dispatching, planning, and also the regulatory actions. But this short term PV power forecasting is a very complicated problem in order to solve it. This paper represents short-term PV power forecasting by constructing a 3-stage approach which is formed by combining empirical mode decomposition (EMD) technique, sine cosine algorithm (SCA), and extreme learning machine (ELM) technique. At the initial phase of the proposed technique, a de-noised series is obtained by adopting a signal filtering strategy based on EMD decomposition technique. Next three different time interval data series are opted for the training and forecasting stage. The selected sets of data are quarterly, half-hourly and hourly PV data observations. The simulation results signify that the recommended technique performs in an out-standing manner than the conventional ones while addressing short term PV power forecasting.

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

[2]  Zechun Hu,et al.  Photovoltaic and solar power forecasting for smart grid energy management , 2015 .

[3]  Huaguang Zhang,et al.  Weather prediction with multiclass support vector machines in the fault detection of photovoltaic system , 2017, IEEE/CAA Journal of Automatica Sinica.

[4]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[5]  Mehdi Khashei,et al.  A new hybrid artificial neural networks and fuzzy regression model for time series forecasting , 2008, Fuzzy Sets Syst..

[6]  Ping Wang,et al.  Cuckoo Search and Particle Filter-Based Inversing Approach to Estimating Defects via Magnetic Flux Leakage Signals , 2016, IEEE Transactions on Magnetics.

[7]  R. Venkatesh Babu,et al.  No-reference image quality assessment using modified extreme learning machine classifier , 2009, Appl. Soft Comput..

[8]  Jing Zhao,et al.  Power generation and renewable potential in China , 2014 .

[9]  Seyedali Mirjalili,et al.  SCA: A Sine Cosine Algorithm for solving optimization problems , 2016, Knowl. Based Syst..

[10]  Yi Liu,et al.  Hilbert-Huang Transform and the Application , 2020, 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS).

[11]  Giorgio Graditi,et al.  Comparison of Photovoltaic plant power production prediction methods using a large measured dataset , 2016 .

[12]  Niranjan Nayak,et al.  Solar photovoltaic power forecasting using optimized modified extreme learning machine technique , 2018, Engineering Science and Technology, an International Journal.

[13]  S. Safi,et al.  Prediction of global daily solar radiation using higher order statistics , 2002 .

[14]  M. Hariharan,et al.  Sine–cosine algorithm for feature selection with elitism strategy and new updating mechanism , 2017, Neural Comput. Appl..

[15]  N. Huang,et al.  A study of the characteristics of white noise using the empirical mode decomposition method , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

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

[17]  Aboul Ella Hassanien,et al.  Sine cosine optimization algorithm for feature selection , 2016, 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA).

[18]  Zvonimir Glasnović,et al.  Sustainable Electric Power System: Is It Possible? Case Study: Croatia , 2010 .

[19]  Minh Phuong Nguyen,et al.  Mathematical modeling of photovoltaic cell/module/arrays with tags in Matlab/Simulink , 2015, Environmental Systems Research.

[20]  Ning An,et al.  Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting , 2013 .

[21]  Cyril Voyant,et al.  Optimization of an artificial neural network dedicated to the multivariate forecasting of daily glob , 2011 .

[22]  Generalized inverse matrices and their applications , 1982 .

[23]  Feilong Cao,et al.  A study on effectiveness of extreme learning machine , 2011, Neurocomputing.

[24]  Giorgio Graditi,et al.  Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production , 2017 .

[25]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[26]  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 .

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

[28]  Eleni Kaplani,et al.  A model to predict expected mean and stochastic hourly global solar radiation I(h;nj) values , 2007 .

[29]  J. Contreras,et al.  ARIMA models to predict next-day electricity prices , 2002 .

[30]  Rui Zhang,et al.  Short-term load forecasting of Australian National Electricity Market by an ensemble model of extreme learning machine , 2013 .

[31]  Nima Amjady,et al.  Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm , 2018, Comput. Intell..

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

[33]  Abdel-Ouahab Boudraa,et al.  EMD-Based Signal Filtering , 2007, IEEE Transactions on Instrumentation and Measurement.

[34]  Hans-Georg Beyer,et al.  Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems , 2009, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[35]  Sundaram Suresh,et al.  Performance enhancement of extreme learning machine for multi-category sparse data classification problems , 2010, Eng. Appl. Artif. Intell..

[36]  Pierluigi Siano,et al.  Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects , 2018 .

[37]  Taher Niknam,et al.  Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel , 2011 .

[38]  Eleni Kaplani,et al.  Stochastic prediction of hourly global solar radiation for Patra, Greece , 2010 .