A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation

A hybrid photovoltaic (PV) forecasting model is proposed for the ultrashort-term prediction of PV output. The model contains two parts: offline modeling and online forecasting. The offline module uses historical monitoring data to establish a weather type classification model and PV output regression submodels. The online module uses real-time monitoring data for weather type identification on target days and the forecasting of irradiation intensity and temperature time series. The appropriate regression submodel can be selected based on the subsequent results, and the ultrashort-term real-time forecasting of PV output can be performed over a short time scale. The model incorporates power generation and historical meteorological data from the PV station and is suitable for practical engineering applications. In addition to the irradiation intensity and temperature, other factors related to photovoltaic output are evaluated; however, they are excluded from the model for simplicity and efficiency. The performance of the model is verified by practical modeling analysis.

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

[2]  Yun Long,et al.  A Study on the Conduction Mechanism and Evaluation of the Comprehensive Efficiency of Photovoltaic Power Generation in China , 2017 .

[3]  Hoay Beng Gooi,et al.  Solar radiation forecast based on fuzzy logic and neural networks , 2013 .

[4]  Glenn Platt,et al.  Machine learning for solar irradiance forecasting of photovoltaic system , 2016 .

[5]  Guangya YANG,et al.  Voltage regulation in LV grids by coordinated volt-var control strategies , 2014 .

[6]  R. Urraca,et al.  Review of photovoltaic power forecasting , 2016 .

[7]  Carlos F.M. Coimbra,et al.  History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining , 2018, Solar Energy.

[8]  Yan Su,et al.  Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines , 2016 .

[9]  Sílvio Mariano,et al.  Impact of Rural Grid-Connected Photovoltaic Generation Systems on Power Quality , 2016 .

[10]  Francisco J. Batlles,et al.  Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast , 2013 .

[11]  Abinet Tesfaye Eseye,et al.  Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information , 2018 .

[12]  C. Coimbra,et al.  Intra-hour DNI forecasting based on cloud tracking image analysis , 2013 .

[13]  R. Inman,et al.  Solar forecasting methods for renewable energy integration , 2013 .

[14]  Dan Keun Sung,et al.  Hourly Solar Irradiance Prediction Based on Support Vector Machine and Its Error Analysis , 2017, IEEE Transactions on Power Systems.

[15]  Gao Yan.  Machine Learning-Based Adaptive Very-Short-Term Forecast Model for Photovoltaic Power , 2015 .

[16]  Cyril Voyant,et al.  Numerical Weather Prediction (NWP) and hybrid ARMA/ANN model to predict global radiation , 2012, ArXiv.

[17]  Zhao Xu,et al.  Daily Clearness Index Profiles Cluster Analysis for Photovoltaic System , 2017, IEEE Transactions on Industrial Informatics.

[18]  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.

[19]  Thomas Reindl,et al.  A novel hybrid approach based on self-organizing maps, support vector regression and particle swarm optimization to forecast solar irradiance , 2015 .

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

[21]  Maria Grazia De Giorgi,et al.  Error analysis of hybrid photovoltaic power forecasting models: A case study of mediterranean climate , 2015 .

[22]  Guang Yang,et al.  Solar irradiance feature extraction and support vector machines based weather status pattern recognition model for short-term photovoltaic power forecasting , 2015 .

[23]  Guangya YANG,et al.  Voltage rise mitigation for solar PV integration at LV grids , 2015 .

[24]  M. Raza,et al.  On recent advances in PV output power forecast , 2016 .

[25]  Ce Zhang,et al.  Performance Evaluation of Cluster Validity Indices (CVIs) on Multi/Hyperspectral Remote Sensing Datasets , 2016, Remote. Sens..

[26]  Wang Fe.  A Classified Forecasting Approach of Power Generation for Photovoltaic Plants Based on Weather Condition Pattern Recognition , 2013 .

[27]  Jan Kleissl,et al.  Solar irradiance forecasting using a ground-based sky imager developed at UC San Diego , 2014 .

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

[29]  Ran Ran,et al.  Short-Term Photovoltaic Power Generation Forecasting Based on Environmental Factors and GA-SVM , 2017 .

[30]  A. Massi Pavan,et al.  A hybrid model (SARIMA-SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant , 2013 .

[31]  Sishaj P. Simon,et al.  Day-ahead forecasting of solar photovoltaic output power using multilayer perceptron , 2017, Neural Computing and Applications.