In the current scenario, worldwide renewable energy systems receive renewed interest because of the global reduction of greenhouse gas emissions. This paper proposes a long-term wind speed prediction model based on various artificial neural network approaches such as Improved Back-Propagation Network (IBPN), Multilayer Perceptron Network (MLPN), Recursive Radial Basis Function Network (RRBFN), and Elman Network with five inputs such as wind direction, temperature, relative humidity, precipitation of water content and wind speed. The proposed ANN-based wind speed forecasting models help plan, integrate, and control power systems and wind farms. The simulation result confirms that the proposed Recursive Radial Basis Function Network (RRBFN) model improves the wind speed prediction accuracy and minimizes the error to a minimum compared to other proposed IBPN, MLPN, and Elman Network-based wind speed prediction models.
Citation: Manogaran Madhiarasan. Long-term wind speed prediction using artificial neural network-based approaches[J]. AIMS Geosciences, 2021, 7(4): 542-552. doi: 10.3934/geosci.2021031
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In the current scenario, worldwide renewable energy systems receive renewed interest because of the global reduction of greenhouse gas emissions. This paper proposes a long-term wind speed prediction model based on various artificial neural network approaches such as Improved Back-Propagation Network (IBPN), Multilayer Perceptron Network (MLPN), Recursive Radial Basis Function Network (RRBFN), and Elman Network with five inputs such as wind direction, temperature, relative humidity, precipitation of water content and wind speed. The proposed ANN-based wind speed forecasting models help plan, integrate, and control power systems and wind farms. The simulation result confirms that the proposed Recursive Radial Basis Function Network (RRBFN) model improves the wind speed prediction accuracy and minimizes the error to a minimum compared to other proposed IBPN, MLPN, and Elman Network-based wind speed prediction models.
[1] | Madhiarasan M (2018) Certain algebraic criteria for design of hybrid neural network models with applications in renewable energy forecasting. Anna University, Chennai, India. |
[2] |
Madhiarasan M, Deepa SN (2016) A novel criterion to select hidden neuron numbers in improved back propagation networks for wind speed forecasting. Appl Intell 44: 878-893. doi: 10.1007/s10489-015-0737-z
![]() |
[3] |
Madhiarasan M, Deepa SN (2017) Comparative analysis on hidden neurons estimation in multi layer perceptron neural networks for wind speed forecasting. Artif Intell Rev 48: 449-471. doi: 10.1007/s10462-016-9506-6
![]() |
[4] | Madhiarasan M, Deepa SN (2016) Comprehensive study of various forecasting techniques for forecast of wind speed in the field of wind energy system. TIDEE 15: 439-457. |
[5] | Madhiarasan M, Deepa SN (2016) Performance investigation of six artificial neural networks for different time scale wind speed forecasting in three wind farms of coimbatore region. Int J Innovation Sci Res 23: 380-411. |
[6] | Madhiarasan M, Deepa SN (2018) A novel method to select hidden neurons in ELMAN neural network for wind speed prediction application. WSEAS Trans Power Syst 13: 13-30. |
[7] | Madhiarasan M, Deepa SN (2016) New criteria for estimating the hidden layer neuron numbers for recursive radial basis function networks and its application in wind speed forecasting. Asian J Inf Technol 15: 4377-4391. |
[8] |
Madhiarasan M (2020) Accurate prediction of different forecast horizons wind speed using a recursive radial basis function neural network. Prot Control Mod Power Syst 5: 1-9. doi: 10.1186/s41601-019-0145-1
![]() |
[9] | Perez-Llera C, Fernandez-Baizanb MC, Feitoc JL, et al. (2002) Local short term prediction of wind speed: A Neural Network Analysis. IEMS 124-129. |
[10] |
Bilgili M, Sahin B, Yasar A (2007) Application of artificial neural networks for the wind speed prediction of target station using reference stations data. Renewable Energy 32: 2350-2360. doi: 10.1016/j.renene.2006.12.001
![]() |
[11] |
Torres JL, Garcia A, De Blas M, et al. (2005) Forecast of hourly average wind speed with ARMA models in Navarre (Spain). Sol Energy 79: 65-77. doi: 10.1016/j.solener.2004.09.013
![]() |
[12] | Li JF, Zhang BH, Mao CX, et al. (2010) Wind speed prediction based on the Elman recursion neural networks. Proc 2010 Int Conf Modell Identif Control 2010: 728-732. |
[13] | Selcuk Nogay H, Cetin Akinci T, Eiduke Viciute M (2012) Application of Artificial neural networks for short term wind speed forecasting in mardin, Turkey. J Energy South Afr 23: 2-7. |
[14] |
Karakuş O, Kuruoğlu EE, Altınkaya MA (2017) One‐day ahead wind speed/power prediction based on polynomial autoregressive model. IET Renew Power Gener 11: 1430-1439. doi: 10.1049/iet-rpg.2016.0972
![]() |
[15] |
Azad HB, Mekhilef S, Ganapathy VG (2014) Long-term wind speed forecasting and general pattern recognition using neural networks. IEEE Trans Sustainable Energy 5: 546-553. doi: 10.1109/TSTE.2014.2300150
![]() |
[16] |
Madhiarasan M, Deepa SN (2016) Long-term wind speed forecasting using spiking neural network optimized by improved modified grey wolf optimization algorithm. Int J Adv Res 4: 356-368. doi: 10.21474/IJAR01/1132
![]() |
[17] |
Neshat M, Nezhad MM, Abbasnejad E, et al. (2021) Wind turbine power output prediction using a new hybrid neuro-evolutionary method. Energy 229: 120617. doi: 10.1016/j.energy.2021.120617
![]() |
[18] |
Chen X, Yu R, Ullah S, et al. (2021) A novel loss function of deep learning in wind speed forecasting. Energy 238: 121808. doi: 10.1016/j.energy.2021.121808
![]() |
[19] |
Neshat M, Nezhad MM, Abbasnejad E, et al. (2021) A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm. Energy Convers Manage 236: 114002. doi: 10.1016/j.enconman.2021.114002
![]() |
[20] |
Liang T, Zhao Q, Lv Q, et al. (2021) A novel wind speed prediction strategy based on Bi-LSTM, MOOFADA and transfer learning for centralized control centers. Energy 230: 120904. doi: 10.1016/j.energy.2021.120904
![]() |
[21] |
Lahouar A, Slama JB (2017) Hour-ahead wind power forecast based on random forests. Renewable Energy 109: 529-541. doi: 10.1016/j.renene.2017.03.064
![]() |
[22] |
Casella L (2019) Wind speed reconstruction using a novel Multivariate Probabilistic method and Multiple Linear Regression: advantages compared to the single correlation approach. J Wind Eng Ind Aerodyn 191: 252-65. doi: 10.1016/j.jweia.2019.05.020
![]() |
[23] |
Madhiarasan M, Tipaldi M, Siano P (2020) Analysis of Artificial Neural Network Performance Based on Influencing Factors for Temperature Forecasting Applications. J High Speed Netw 26: 209-223. doi: 10.3233/JHS-200639
![]() |
[24] | Wilks DS (2020) Statistical methods in the atmospheric sciences: an introduction, San Diego, CA, USA. |
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2. | Joachim von Below, José A. Lubary, Baptiste Vasseur, Some Remarks on the Eigenvalue Multiplicities of the Laplacian on Infinite Locally Finite Trees, 2013, 63, 1422-6383, 1331, 10.1007/s00025-012-0271-9 | |
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