International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
권호 표지
DOI QR코드

DOI QR Code

A Novel Second Order Radial Basis Function Neural Network Technique for Enhanced Load Forecasting of Photovoltaic Power Systems

  • Received : 2021.02.05
  • Published : 2021.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical system.

Keywords

References

  1. Kukuca Peter et.al. From Smart Metering to Smart Grid, Measurement Science Review, Vol16, N3, 2016. DOI: https://doi.org/10.1515/msr-2016-0017.
  2. A. Abdul Khadar, et.al. Research Advancements Towards in Existing Smart Metering over Smart Grid, International Journal of Advanced Computer Science and Applications, Vol8 ,N5, , 2017. DOI: 10.14569 / IJACSA.2017.080511 . https://doi.org/10.14569/IJACSA.2017.080511
  3. Zheng J., Gao D., and Lin L., "Smart Meters in Smart Grid: An Overview," presented at the 2013 IEEE Green Technologies Conference, Denver, CO, 2013, DOI: 10.1109 / GreenTech.2013.17. https://doi.org/10.1109/GreenTech.2013.17
  4. Etuahene Samual et.al., Artificial Neural Network based Artificial Intelligent Algorithms for Accurate Monthly Load Forecasting of Power Consumption, London Journal of Research in Science, Vol19, N°2, P°1-15, 2019.
  5. Gams Nalcaci et.al., Long-term load forecasting: Models based on MARS, ANN and LR methods, Central European Journal of Operations Research,Vol27, P°1033-1049, 2018. https://doi.org/10.1007/s10100-018-0531-1
  6. Hamed H.H.Aly.,A Proposed Hybrid Load Forecasting Models of ANN,WNN and KF based on clustering Techniques for Smart Grids, Electric Power Systems Research, Vol182, N106191, 2020. https://doi.org/10.1016/j.epsr.2019.106191
  7. Hernandez Luis et.al. Artificial neural network for shortterm load forecasting in distribution systems, Energies, Vol7, N 1576-1598, 2014. https://doi.org/10.3390/en7031576
  8. A.S Kahweja et.al. Joint Bagged-Boosted ANN: Using ensemble Machine Learning to improve short-term electricity Load Forecasting, Electric Power Systems Research, Vol179, N 106080, 2019.
  9. E. Juan Zarate Perez, Performance Analysis of Bagging Feed-Forward Neural Network for Forecasting Building Energy Demand, Current Journal of Applied Science and Technology Vol 30, N2, P1-12, 2018. https://doi.org/10.9734/CJAST/2018/44836
  10. Ravil Bikmetov et.al. "Dynamic prediction capabilities of Smart Metering Infrastructure", Conference Paper, October 2015, DOI: 10.1109 / NAPS.2015.7335235 . https://doi.org/10.1109/NAPS.2015.7335235
  11. Seung-Mook Baek, Mid Term Load Pattern Forecasting with recurrent Artificial Neural Network, IEEE Access, Vol7, P 172830 a 172838, 2017. https://doi.org/10.1109/ACCESS.2019.2957072
  12. New England ISO Database, available at this link: https://www.iso-ne.com/.
  13. Hernandez Luis, et al., A Survey on Electric power demand forecasting: future trends in smart grids, micro-grids and smart buildings. IEEE Communication Survey Tutorial, Vol16, N3, 2014.
  14. Amit Kumar Yadav, S.S. Chandel, Solar radiation prediction using Artificial Neural Network techniques: A review, Renewable and Sustainable Energy Reviews,Vol 33,N 772-781, 2017. https://doi.org/10.1016/j.rser.2013.08.055
  15. T. T. Teo, T. Logenthiran and W. L. Woo, "Forecasting of photovoltaic power using extreme learning machine," IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA), Bangkok, pp. 1-6, 2015. DOI: 10.1109/ISGTAsia.2015.7387113.
  16. Hernandez Luis, Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems, Energies,Vol°7,N° 1576-1598, 2014. https://doi.org/10.3390/en7031576
  17. Bodgan M. et.al., A Novel RBF Training Algorithm for Short-Term Electric Load Forecasting and Comparative Studies, IEEE Transactions on Indistrual Electronics, Vol.62, P. 6519 a 6529, 2015. https://doi.org/10.1109/TIE.2015.2424399
  18. Xingyu Yan et.al. Solar radiation forecasting causing Artificial Neuronal Network for local power reserve, CISTEM, Vol 106,N 288-297, 2014.
  19. Guillaume Prez et.al. "Impact of the power consumption in the buildings in Central School of Lille", P°1-30, 2018.
  20. Tiantian Xie, Fast and Efficient Second-Order Method for Training Radial Basis Function Networks, IEEE Transactions on Neural Networks and Learning Systems, Vol. 23, N4, 2012.
  21. Haoyan Yang et.al., Short-term forecasting of Micro-grid based on grey correletion analysis and neural networ optimized by mind evolutionary algorithm, IEEE PES Innovative Smart Grid Technologies Asia, P° 2738-2742, 2019.
  22. Amit Kumar Yadav, SS Chandel, Artificial Neural Network based Prediction of Solar Radiation for Indian Stations, International Journal of Computer Applications, Vol50 - No.9, 2012.
  23. Amit Kumar Yadav, Vikrant Sharma, Hasmat Malik, SS Chandel ,Daily array yeild of grid-interactive photovoltaic plant using relief attribute evaluator based Radial Basis Function Neural Network, Renewable and Sustainable Energy Reviews, Vol81, N2, P° 2115-2127, 2017. https://doi.org/10.1016/j.rser.2017.06.023
  24. Amit Kumar Yadav, Hasmat Malik, SS Chandel, Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models ; Renewable and Sustainable Energy Reviews, Vol31, N31, P° 509-519, 2014. https://doi.org/10.1016/j.rser.2013.12.008
  25. Gabriel Trierweiler Ribeiro et.al., Enhanced ensemble structures using wavelet neural networks applied to short-term load forecasting, Engineering Applications of Artificial Intelligence, Vol95, P°103852, 2020. https://doi.org/10.1016/j.engappai.2020.103852
  26. Yang Zhang et.al., A novel integrated price and load forecasting method in smart grid environment based on multi-level structure, Vol95, N103852, 2019. https://doi.org/10.1016/j.engappai.2020.103852
  27. Chao-Ming Huang et.al., One-day-ahead hourly forecasting for photovoltaic power generation using an intelligent method with weather-based forecasting models, IET Generation, Transmission & Distribution, Vol. 9, Iss. 14, pp. 1874-1882, 2015. https://doi.org/10.1049/iet-gtd.2015.0175
  28. Weibiao Qiao et.al., A Novel Hybrid Prediction Model for Hourly Gas Consumption in Supply Side Based on Improved Whale Optimization Algorithm and Relevance Vector Machine, IEEE Access, 2019.
  29. Yuxin Wen et.al., Performance Evaluation of Probabilistic Methods Based on Bootstrap and Quantile Regression to Quantify PV Power Point Forecast Uncertainty, IEEE Transactions On Neural Networks And Learning Systems, Vol.,31, N4, 2020
  30. Florin Dragomir, Forecasting of Photovoltaic Power Generation by RBF Neural Networks, Advanced Materials Research, ISSN: 1662-8985, Vol. 918, pp 200-205, 2017. https://doi.org/10.4028/www.scientific.net/AMR.918.200
  31. Amit Kumar Yadav , S.S. Chandel, Artificial Neural Network based Prediction of Solar Radiation for Indian Stations, International Journal of Computer Applications, Vol50 - No.9, July 2012.
  32. Samuel Atuahene, et.al. Artificial Neural Network based Artificial Intelligent Algorithms for Accurate Monthly Load Forecasting of Power Consumption, London Journal of Research in Science, Natural and Formal, Vol 19, Issue2, 2019
  33. Muhammed Wasseem et.al. Data-Driven Load Forecasting of Air Conditioners for Demand Response Using Levenberg-Marquardt Algorithm-Based ANN, Big data and Cognitive Computing,Vol3,N36, 2019.
  34. E. Juan Zarate Perez, Performance Analysis of Bagging Feed-Forward Neural Network for Forecasting Building Energy Demand, Current Journal of Applied Science and Technology Vol 30, N2, P1-12, 2018. https://doi.org/10.9734/CJAST/2018/44836
  35. Farshid Keynia, A new feature selection algorithm and composite neural network for electricity price forecasting, Engineering Applications of Artificial Intelligence, Vol25, P1687-1697, 2012. https://doi.org/10.1016/j.engappai.2011.12.001
  36. Ramesh KumarV et.aL, Daily peak load forecast using artificial neural network, International Journal of Electrical and Computer Engineering, Vol 9, N4, P. 2256~2263, 2019 https://doi.org/10.11591/ijece.v9i4.pp2256-2263
  37. Jingwen Tian, Meijuan Gao, Fan Zhang, Network intrution detection method based on radial basis function neural network, IEEE Natural Computation workshop, 2009.
  38. Liu, J. (2013). Radial Basis Function (RBF) neural network control for mechanical systems: design, analysis and Matlab simulation. Springer Science & Business Media.