Data Driven Model for Short Term PV Power Forecasting using Least Square Support Vector Regression

This paper presents an off-line model for forecasting photovoltaic power. This model is suitable to provide short-term forecasts without the need of Numerical Weather predictions data. This is interesting especially for power system operators as well as for individuals who do not have access to weather data and forecasts. In this paper we investigate the influence of an additional input parameter to the accuracy of an already tested and validated offline model. To rectify the performances of our models we will compare their performances with a usual persistent model. The results of simulation shows the benefits of adding this input to improve the accuracy of our PV forecasting model.

[1]  Boonyang Plangklang,et al.  Forecasting Power output of PV Grid Connected System in Thailand without using Solar Radiation Measurement , 2011 .

[2]  Adel Mellit,et al.  Short-term forecasting of power production in a large-scale photovoltaic plant , 2014 .

[3]  Mohamed Tabaa,et al.  Short-term PV power forecasting using Support Vector Regression and local monitoring data , 2016, 2016 International Renewable and Sustainable Energy Conference (IRSEC).

[4]  Yue Zhang,et al.  Day-Ahead Power Output Forecasting for Small-Scale Solar Photovoltaic Electricity Generators , 2015, IEEE Transactions on Smart Grid.

[5]  Mohamed Tabaa,et al.  Hybrid renewable energy installation for research and innovation: Case of Casablanca city in Morocco , 2017, 2017 15th IEEE International New Circuits and Systems Conference (NEWCAS).

[6]  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.

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

[8]  Bangyin Liu,et al.  Online 24-h solar power forecasting based on weather type classification using artificial neural network , 2011 .

[9]  Peng Wang,et al.  Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines , 2011, IEEE Transactions on Industry Applications.

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

[11]  T. Takashima,et al.  Use of support vector regression and numerically predicted cloudiness to forecast power output of a photovoltaic power plant in Kitakyushu, Japan , 2012 .

[12]  Chen Changsong,et al.  Forecasting power output for grid-connected photovoltaic power system without using solar radiation measurement , 2010, The 2nd International Symposium on Power Electronics for Distributed Generation Systems.

[13]  Mohammed Mestari,et al.  Short-term solar power forecasting using Support Vector Regression and feed-forward NN , 2017, 2017 15th IEEE International New Circuits and Systems Conference (NEWCAS).

[14]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[15]  Renato De Leone,et al.  Photovoltaic energy production forecast using support vector regression , 2015, Neural Computing and Applications.

[16]  Vishwamitra Oree,et al.  A hybrid method for forecasting the energy output of photovoltaic systems , 2015 .

[17]  Francesco Grimaccia,et al.  Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power , 2017, Math. Comput. Simul..

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

[19]  Chao-Ming Huang,et al.  One-day-ahead hourly forecasting for photovoltaic power generation using an intelligent method with weather-based forecasting models , 2015 .

[20]  Chunxiang Yang,et al.  An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data , 2015, IEEE Transactions on Sustainable Energy.

[21]  Xiaoyan Xu,et al.  Comparative study of power forecasting methods for PV stations , 2010, 2010 International Conference on Power System Technology.

[22]  Yongping Yang,et al.  Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines , 2012 .