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http://dx.doi.org/10.22937/IJCSNS.2021.21.2.9

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

Farhat, Arwa Ben (University of Carthage)
Chandel, Shyam.Singh (Shoolini University)
Woo, Wai Lok (Northumbria University)
Adnene, Cherif (Faculty of sciences of Tunisia)
Publication Information
International Journal of Computer Science & Network Security / v.21, no.2, 2021 , pp. 77-87 More about this Journal
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
Artificial Neural Network (ANN); Radial Basis Function Neural Network (RBFNN); Load forecasting; Electrical systems; Photovoltaic systems;
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1 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 .   DOI
2 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.   DOI
3 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.   DOI
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.   DOI
6 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.   DOI
7 Yang Zhang et.al., A novel integrated price and load forecasting method in smart grid environment based on multi-level structure, Vol95, N103852, 2019.   DOI
8 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.
9 Hernandez Luis et.al. Artificial neural network for shortterm load forecasting in distribution systems, Energies, Vol7, N 1576-1598, 2014.   DOI
10 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.
11 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.   DOI
12 Ravil Bikmetov et.al. "Dynamic prediction capabilities of Smart Metering Infrastructure", Conference Paper, October 2015, DOI: 10.1109 / NAPS.2015.7335235 .   DOI
13 Seung-Mook Baek, Mid Term Load Pattern Forecasting with recurrent Artificial Neural Network, IEEE Access, Vol7, P 172830 a 172838, 2017.   DOI
14 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.   DOI
15 Xingyu Yan et.al. Solar radiation forecasting causing Artificial Neuronal Network for local power reserve, CISTEM, Vol 106,N 288-297, 2014.
16 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.   DOI
17 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
18 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.
19 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
20 Farshid Keynia, A new feature selection algorithm and composite neural network for electricity price forecasting, Engineering Applications of Artificial Intelligence, Vol25, P1687-1697, 2012.   DOI
21 Jingwen Tian, Meijuan Gao, Fan Zhang, Network intrution detection method based on radial basis function neural network, IEEE Natural Computation workshop, 2009.
22 Liu, J. (2013). Radial Basis Function (RBF) neural network control for mechanical systems: design, analysis and Matlab simulation. Springer Science & Business Media.
23 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.   DOI
24 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.   DOI
25 Florin Dragomir, Forecasting of Photovoltaic Power Generation by RBF Neural Networks, Advanced Materials Research, ISSN: 1662-8985, Vol. 918, pp 200-205, 2017.   DOI
26 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.
27 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   DOI
28 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.   DOI
29 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.
30 New England ISO Database, available at this link: https://www.iso-ne.com/.
31 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.
32 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.   DOI
33 Hernandez Luis, Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems, Energies,Vol°7,N° 1576-1598, 2014.   DOI
34 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.
35 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.   DOI
36 Guillaume Prez et.al. "Impact of the power consumption in the buildings in Central School of Lille", P°1-30, 2018.
37 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.
38 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.   DOI