Forecasting Solar Irradiance with Weather Classification and Chaotic Gravitational Search Algorithm Based Wavelet Kernel Extreme Learning Machine

Alok Kumar Pani, Niranjan Nayak

Abstract


In this work an improved KELM based forecasting model is being proposed, which attains a specific level of prediction of solar irradiance affecting PV power management. The new method is known as Wavelet KELM. A new optimization technique known as Gravitational Search Algorithm (GSA) is implemented to optimize various parameters of the kernel function. The novelty of this work is that, it focuses on a KELM learning algorithm with parameter optimization for exact solar irradiance forecasting. The exact prediction of irradiance is highly essential for system level stability and future large scale PV installations. The GSA based optimized KELM (GSA-KELM) is implemented for short term solar irradiance forecasting based on various weather conditions.  A hit and trial method was used for the selection of kernel function parameters which affects the forecasting model performance. The optimized kernel structure not only minimizes the arbitrariness of the variables but also makes the process fast by extenuating the choice of parameter on the basis of the user which has to experience the repeated trial method for various kernel functions. Thus OKELM impart more accuracy in prediction within less time and outperforms the basic KELM. In this work, data collected from a photovoltaic power plant in India (the capacity being 1 MW) has been considered for forecasting model validation.


Keywords


Solar Irradiance; KELM; PV power forecasting; GSA; OKELM; CGSA

Full Text:

PDF

References


Rasmus Luthandera, Joakim Widéna, Daniel Nilssonb, Jenny Palm, ‘Photovoltaic self-consumption in buildings A review ’ Applied Energy, Elsiever,Vol.142, PP. 80-94.

Diagne, Maimouna, et al. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids." Renewable and Sustainable Energy Reviews 27 (2013): 65-76.

Moreno-Munoz, A., et al. "Very short term forecasting of solar radiation."Photovoltaic Specialists Conference, 2008. PVSC'08. 33rd IEEE. IEEE, 2008.

Bacher, Peder, Henrik Madsen, and Henrik Aalborg Nielsen. "Online short-term solar power forecasting." Solar Energy 83.10 (2009): 1772-1783.

Sobrina Sobria, Sam Koohi-Kamalia,, Nasrudin Abd. Rahim, ‘Solar photovoltaic generation forecasting methods: A review’ Energy Conversion and Management, Elsiver, Vol.156,2018, PP.459-497.

Lida Barba, Nibaldo Rodriguez, and Cecilia Montt, ‘Smoothing Strategies Combined with ARIMA and Neural Networks to Improve the Forecasting of Traffic Accidents ’ Scientific World Journal, Vol. 2014, 2015, PP.123-126.

Donghun Lee and Kwanho Kim , ‘Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power output using meteorological Information’

Mora-Lopez, L. L., and M. Sidrach-de-Cardona. "Multiplicative ARMA models to generate hourly series of global irradiation." Solar Energy 63.5 (1998): 283-291.

Boata, Remus St, and Paul Gravila. "Functional fuzzy approach for forecasting daily global solar irradiation." Atmospheric Research Vol. 112, 2012, 79-88.

B. Sivaneasan, C. Y. Yu, and K. P. Goh, ‘Solar Forecasting using ANN with Fuzzy Logic Pre-processing ’ Energy Procedia, Elsevier, Vol. 143,2017, PP. 727–732.

PremalathaNeelamegama,ValanArasuAmirtham, ‘ Prediction of solar radiation for solar systems by using ANN models with different backpropagation algorithms’ JournalofAppliedResearchandTechnology Vol.14,2016,PP.206-214.

Guido Cervone, Laura Clemente-Harding, Stefano Alessandrini, Luca Delle Monache, ‘Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble’ renewable Energy, Institute for Cyber Science (ICS), Geography, Vol. 108, 2017, PP. 274-286.

Ramanpreet Kaura,b, Amrit Lal Sangala, Krishan Kumar,‘Modelling and simulation of adaptive Neuro-fuzzy based intelligentsystem for predictive stabilization in structured overlay networks’ Engineering Science and Technology, an International Journal,Vol.20,2017, PP.310-320.

Akinobu Murata, Hideaki Ohtakeb, Takashi Oozeki, ‘Modelling of uncertainty of solar irradiance forecasts on numericalweather predictions with the estimation of multiple confidence intervals’Renewable Energy, Elsevier, Vol. 117, 2018,PP.193-201.

Seul-Gi Kim, Jae-Yoon Jung and Min Kyu Sim, ‘A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning’Sustainability,2019, Vol.11, PP. 1501-1521.

Antonanzas, J., et al. "Solar irradiation mapping with exogenous data from support vector regression machines estimations." Energy Conversion and Management 100 (2015): 380-390.

Urraca, R., et al. "Estimation of solar global irradiation in remote areas."Journal of Renewable and Sustainable Energy 7.2 ,2015.

Chu, Yinghao, et al. "Real-time forecasting of solar irradiance ramps with smart image processing." Solar Energy 114,2015, PP.91-104.

Islam, Farzana, Ahmed Al-Durra, and S. M. Muyeen. "Smoothing of wind farm output by prediction and supervisory-control-unit-based FESS." IEEE Transactions on Sustainable Energy 4.4 ,2013,PP.925-933.

Jing Huang, Małgorzata Korolkiewicz, Manju Agrawal,John Boland, ‘Forecasting solar radiation on an hourly time scale using a Coupled Auto Regressive and Dynamical System (CARDS) model’ Solar Energy, Elsevier, Vol. 87, 2013, PP. 136-149.

Stéphanie Monjoly,André, Rudy andTed Soubdhan, ‘Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model’ Energies 2019, Vol.12 No.12,PP. 2264-2281.

Dong, Zibo, et al. "Satellite image analysis and a hybrid ESSS/ANN model to forecast solar irradiance in the tropics." Energy Conversion and Management, Vol. 79,2014, PP. 66-73.

Hsu, Chih-Ming. "A hybrid procedure for stock price prediction by integrating self-organizing map and genetic programming." Expert Systems with Applications, Vol. 38. No.11,2011, PP.14026-14036.

Manoja Kumar Behera, Niranjan Nayak, ‘A comparative study on short-term PV power forecasting using decomposition based optimized extreme learning machine algorithm’Engineering Science and Technology, an international Journal,, Elsevier, Vol. in press.

Salcedo-Sanz, S., et al. "Daily global solar radiation prediction based on a hybrid Coral Reefs Optimization–Extreme Learning Machine approach." Solar Energy, Elsevier, Vol.105,2014, PP. 91-98.

Rui Yang , Shuliang Xu ID, Lin Feng 2, ‘An Ensemble Extreme Learning Machine for Data Stream Classification’ Algorithms, MDPI, Vol. 11, No.107, PP.1-16.

Manoja Kumar Behera, Irani Majumder, Niranjan Nayak, ‘Solar photovoltaic power forecasting using optimized modified extreme learning machine technique’Engineering Science and Technology, an International Journal, Elsevier, Vol.21, No.3, PP. 428-438.

Ding, Shifei, et al. "A Novel Extreme Learning Machine Based on Hybrid Kernel Function." JCP , Vol.8.8,2013, PP. 2110-2117.

Huang, Guang-Bin, et al. "Extreme learning machine for regression and multiclass classification." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics, Vol. 42.2, 2012, PP. 513-529.

Anita , Anupam Yadav, ‘AEFA: Artificial electric field algorithm for global optimization’ Swarm and Evolutionary Computation, Elsevier, Vol. 48,2019, PP. 93–108.

Catalao, J. P. S., H. M. I. Pousinho, and V. M. F. Mendes. "Hybrid wavelet-PSO-ANFIS approach for short-term wind power forecasting in Portugal."IEEE Transactions on Sustainable Energy 2.1 (2011): 50-59.

Wang, Xiping, and Yaqi Wang. "A Hybrid Model of EMD and PSO-SVR for Short-Term Load Forecasting in Residential Quarters." Mathematical Problems in Engineering 2016 (2016).

Mehmet Yesilbudak, Medine Colak, Ramazan Bayindir, What are the Current Status and Future Prospects in Solar Irradiance and Solar Power Forecasting? International Journal of Renewable Energy Research, Vol. 8, No. 1 (2018) March

David Motyka;Martina Kajanova;Peter Bracinik, The Impact of Embedded Generation on Distribution Grid Operation. 2018 7th International Conference on Renewable Energy research and Applications (ICRERA),Paris France.

Abdelkader HARROUZ, Meriem ABBES, Ilhami COLAK, Korhan KAYSILI; Smart Grid and Renewable Energy in Algeria; 2017 6th International Conference on Renewable Energy research and Applications (ICRERA), San Deigo USA.

Yu Shimizu,Taichiro Sakagami, Hiraoki Kitano; Prediction of weather Dependant Energy Consumption of Residential Housing; 2017 6th International Conference on Renewable Energy research and Applications (ICRERA), San Deigo USA.

Luis Felipe Normandia Lourenco, Mauricio Barbosa de Camargo Salles; Matheus Mingatos Fernandes Gemignani, Marcos Roberto Gouvea, Nelson Kagan; Time Series Modelling for Solar Irradiance Estimation in Northeast Brazil. 2017 6th International Conference on Renewable Energy research and Applications (ICRERA), San Deigo USA.

Rendani Mbuvha, Mattias Jonsson, Niclas Ehn and Pawel Herman; Bayesian Neural Networks For One-hour Ahead Wind Power Forecasting; 2017 6th International Conference on Renewable Energy research and Applications (ICRERA), San Deigo USA.

Jun Liu, Wanliang Fang, Xudong Zhang, Chunxiang Yang, An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data. IEEE Transactions on Sustainable Energy, Volume: 6 , Issue: 2 , April 2015.

Yu Liu ; Zhi Li ; Kai Bai ; Zhaoguang Zhang ; Xining Lu ; Xiaomeng Zhang, Short-term power-forecasting method of distributed PV power system for consideration of its effects on load forecasting. The Journal of Engineering Volume: 2017 , Issue: 13 , 2017.

Can Wan ; Jin Lin ; Yonghua Song ; Zhao Xu ; Guangya Yang Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach. IEEE Transactions on Power Systems Volume: 32, Issue: 3, May 2017




DOI (PDF): https://doi.org/10.20508/ijrer.v9i4.10028.g7767

Refbacks

  • There are currently no refbacks.


Online ISSN: 1309-0127

Publisher: Gazi University

IJRER is cited in SCOPUS, EBSCO, WEB of SCIENCE (Clarivate Analytics);

IJRER has been cited in Emerging Sources Citation Index from 2016 in web of science.

WEB of SCIENCE between 2020-2022; 

h=30,

Average citation per item=5.73

Impact Factor=(1638+1731+1808)/(189+170+221)=9.24

Category Quartile:Q4