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Short term solar power forecasting using hybrid minimum variance expanded RVFLN and Sine-Cosine Levy Flight PSO algorithm

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  • Dash, Deepak Ranjan
  • Dash, P.K.
  • Bisoi, Ranjeeta

Abstract

With the increase in solar power integration, the necessity for accurate solar power forecasting is of paramount importance for various energy markets, grid management system, power dispatching and so on. This paper analyses a new and efficient hybrid forecasting approach consisting of empirical wavelet transform (EWT) and Robust minimum variance Random Vector Functional Link Network (RRVFLN) with random weight vector for the enhancement nodes along with a functionally expanded direct link to the output node from input nodes. The RRVFLN contains dual activation functions in each hidden neuron of the network and its parameters are optimized by an efficient Sine Cosine and levy flight based particle swarm optimization (PSOLVSC). The proposed RRVFLN has some similarity with recently proposed broad learning system that can replace neural networks with deep architecture for handling big and time varying data bases. For examining the solar power prediction accuracy of the proposed EWT-RRVFLN model, the historical solar power data for different seasons of Alabama, Ikitelli, and Berlin are taken into consideration which are divided into three time horizon intervals of 5min, 15min, 30min and 60 min, respectively.

Suggested Citation

  • Dash, Deepak Ranjan & Dash, P.K. & Bisoi, Ranjeeta, 2021. "Short term solar power forecasting using hybrid minimum variance expanded RVFLN and Sine-Cosine Levy Flight PSO algorithm," Renewable Energy, Elsevier, vol. 174(C), pages 513-537.
    • Handle: RePEc:eee:renene:v:174:y:2021:i:c:p:513-537
    DOI: 10.1016/j.renene.2021.04.088
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    1. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
    2. Lima, Francisco J.L. & Martins, Fernando R. & Pereira, Enio B. & Lorenz, Elke & Heinemann, Detlev, 2016. "Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks," Renewable Energy, Elsevier, vol. 87(P1), pages 807-818.
    3. Ueckerdt, Falko & Brecha, Robert & Luderer, Gunnar, 2015. "Analyzing major challenges of wind and solar variability in power systems," Renewable Energy, Elsevier, vol. 81(C), pages 1-10.
    4. Hu, Jianming & Wang, Jianzhou, 2015. "Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression," Energy, Elsevier, vol. 93(P2), pages 1456-1466.
    5. Shah, Rakibuzzaman & Mithulananthan, N. & Bansal, R.C. & Ramachandaramurthy, V.K., 2015. "A review of key power system stability challenges for large-scale PV integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1423-1436.
    6. Xun Zhang & Juelong Li & Jianchun Xing & Ping Wang & Qiliang Yang & Ronghao Wang & Can He, 2014. "Optimal Sensor Placement for Latticed Shell Structure Based on an Improved Particle Swarm Optimization Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-12, June.
    7. Tang, Pingzhou & Chen, Di & Hou, Yushuo, 2016. "Entropy method combined with extreme learning machine method for the short-term photovoltaic power generation forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 243-248.
    8. Jinling Lu & Bo Wang & Hui Ren & Daqian Zhao & Fei Wang & Miadreza Shafie-khah & João P. S. Catalão, 2017. "Two-Tier Reactive Power and Voltage Control Strategy Based on ARMA Renewable Power Forecasting Models," Energies, MDPI, vol. 10(10), pages 1-13, October.
    9. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    10. Prema, V. & Rao, K. Uma, 2015. "Development of statistical time series models for solar power prediction," Renewable Energy, Elsevier, vol. 83(C), pages 100-109.
    11. Li, Yanting & He, Yong & Su, Yan & Shu, Lianjie, 2016. "Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines," Applied Energy, Elsevier, vol. 180(C), pages 392-401.
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    Cited by:

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    2. Cheng, Hsu-Yung & Yu, Chih-Chang & Lin, Chih-Lung, 2021. "Day-ahead to week-ahead solar irradiance prediction using convolutional long short-term memory networks," Renewable Energy, Elsevier, vol. 179(C), pages 2300-2308.
    3. Wang, Lining & Mao, Mingxuan & Xie, Jili & Liao, Zheng & Zhang, Hao & Li, Huanxin, 2023. "Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model," Energy, Elsevier, vol. 262(PB).
    4. Sabadus, Andreea & Blaga, Robert & Hategan, Sergiu-Mihai & Calinoiu, Delia & Paulescu, Eugenia & Mares, Oana & Boata, Remus & Stefu, Nicoleta & Paulescu, Marius & Badescu, Viorel, 2024. "A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches," Renewable Energy, Elsevier, vol. 226(C).
    5. Du, Pei & Guo, Ju'e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2022. "A novel two-stage seasonal grey model for residential electricity consumption forecasting," Energy, Elsevier, vol. 258(C).
    6. Juan Li & Qing An & Hong Lei & Qian Deng & Gai-Ge Wang, 2022. "Survey of Lévy Flight-Based Metaheuristics for Optimization," Mathematics, MDPI, vol. 10(15), pages 1-27, August.
    7. Hou, Guolian & Wang, Junjie & Fan, Yuzhen & Zhang, Jianhua & Huang, Congzhi, 2024. "A novel wind power deterministic and interval prediction framework based on the critic weight method, improved northern goshawk optimization, and kernel density estimation," Renewable Energy, Elsevier, vol. 226(C).
    8. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2022. "Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    9. Wang, Han & Zhang, Ning & Du, Ershun & Yan, Jie & Han, Shuang & Li, Nan & Li, Hongxia & Liu, Yongqian, 2023. "An adaptive identification method of abnormal data in wind and solar power stations," Renewable Energy, Elsevier, vol. 208(C), pages 76-93.

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