Performance comparison of artificial intelligence techniques in short term current forecasting for photovoltaic system

This paper presents artificial intelligence approach of artificial neural network (ANN) and random forest (RF) that used to perform short-term photovoltaic (PV) output current forecasting (STPCF) for the next 24-hours. The input data for ANN and RF is consists of multiple time lags of hourly solar irradiance, temperature, hour, power and current to determine the movement pattern of data that have been denoised by using wavelet decomposition. The Levenberg-Marquardt optimization technique is used as a back-propagation algorithm for ANN and the bagging based bootstrapping technique is used in the RF to improve the results of forecasting. The information of PV output current is obtained from Green Energy Research (GERC) University Technology Mara Shah Alam, Malaysia and is used as the case study in estimation of PV output current for the next 24-hours. The results have shown that both proposed techniques are able to perform forecasting of future hourly PV output current with less error.

[1]  Reinaldo Castro Souza,et al.  Long Term Electricity Forecast: A Systematic Review , 2015, ITQM.

[2]  Truong Q. Nguyen,et al.  Forecasting of Solar Photovoltaic System Power Generation Using Wavelet Decomposition and Bias-Compensated Random Forest , 2017, 2017 Ninth Annual IEEE Green Technologies Conference (GreenTech).

[3]  José Francisco Moreira Pessanha,et al.  ScienceDirect Information Technology and Quantitative Management ( ITQM 2015 ) Forecasting long-term electricity demand in the residential sector , 2015 .

[4]  T. Hoff,et al.  Validation of short and medium term operational solar radiation forecasts in the US , 2010 .

[5]  Mohammad Lutfi Othman,et al.  Sequential process of feature extraction methods for artificial neural network in short term load forecasting , 2015 .

[6]  Muhammad Murtadha Othman,et al.  Short Term Load Forecasting (STLF) Using Artificial Neural Network Based Multiple Lags of Time Series , 2008, ICONIP.

[7]  Giuseppina Ciulla,et al.  Artificial Neural Networks to Predict the Power Output of a PV Panel , 2014 .

[8]  Muhsin Tunay Gencoglu,et al.  The performance comparison of Multiple Linear Regression, Random Forest and Artificial Neural Network by using photovoltaic and atmospheric data , 2017, 2017 14th International Conference on Engineering of Modern Electric Systems (EMES).

[9]  Henrik Madsen,et al.  Online short-term solar power forecasting , 2009 .

[10]  I. Musirin,et al.  Short term load forecasting using artificial neural network with feature extraction method and stationary output , 2012, 2012 IEEE International Power Engineering and Optimization Conference Melaka, Malaysia.

[11]  Lei Wang,et al.  An ANN-based Approach for Forecasting the Power Output of Photovoltaic System , 2011 .

[12]  Wilfried Elmenreich,et al.  Modeling of the output current of a photovoltaic grid-connected system using random forests technique , 2018 .

[13]  Muhammad Murtadha Othman,et al.  Design of a small renewable resource model based on the stirling engine with alpha and beta configurations , 2017 .

[14]  I. Musirin,et al.  Forecasting short term electric load based on stationary output of artificial neural network considering sequential process of feature extraction methods , 2012, 2012 IEEE International Power Engineering and Optimization Conference Melaka, Malaysia.

[15]  Muhammad Murtadha Othman,et al.  Gamma Stirling engine for a small design of renewable resource model , 2017 .

[16]  Jeyraj Selvaraj,et al.  Global prospects, progress, policies, and environmental impact of solar photovoltaic power generation , 2015 .

[17]  Soteris A. Kalogirou,et al.  Machine learning methods for solar radiation forecasting: A review , 2017 .

[18]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[19]  Muhammad Murtadha Othman,et al.  Short term load forecasting (STLF) using artificial neural network based multiple lags and stationary time series , 2010, 2010 4th International Power Engineering and Optimization Conference (PEOCO).

[20]  Fei Wang,et al.  Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters , 2012 .

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

[22]  Muhammad Murtadha Othman,et al.  Optimizing size and operation of hybrid energy systems , 2013, 2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO).