A New Hybrid ARAR and Neural Network Model for Multi-Step Ahead Wind Speed Forecasting in Three Regions of Pakistan

Wind is one of the most essential sources of clean, environmental friendly, socially constructive, economically beneficial, and renewable energy. To intuit the potential of this energy in a region the accurate wind speed modeling and forecasting are crucially important, even for planning, conversion of wind energy to electricity, energy trading, and reducing instability. However, accurate prediction is difficult due to intermittency and intrinsic complexity in wind speed data. This study aims to suggest a more appropriate model for accurate wind speed forecasting in the Jhimpir, Gharo, and Talhar, regions of Sindh, Pakistan. Therefore, the present study combined the Autoregressive-Autoregressive (ARAR) and Artificial Neural Network (ANN) models to propose a new hybrid ARAR-ANN model for better prediction by precisely capturing different patterns of the wind speed time-series data sets. The proposed hybrid model is efficient in modeling, reducing statistical errors, and forecasting the wind speed effectively. The performance of the proposed hybrid ARAR-ANN model is compared using three error-statistics and Nash-Sutcliffe efficiency-coefficient. The empirical results of the four performance indices fully demonstrated the superiority of the hybrid ARAR-ANN model than persistence model, ARAR, ANN and SVM. Indeed, the proposed model is an effective and feasible approach for wind speed forecasting.

[1]  Shervin Motamedi,et al.  Extreme learning machine approach for sensorless wind speed estimation , 2016 .

[2]  Muhammad Khalid,et al.  A method for short-term wind speed time series forecasting using Support Vector Machine Regression Model , 2017, 2017 6th International Conference on Clean Electrical Power (ICCEP).

[3]  Saad Mekhilef,et al.  Long-Term Wind Speed Forecasting and General Pattern Recognition Using Neural Networks , 2014, IEEE Transactions on Sustainable Energy.

[4]  Jing Shi,et al.  On comparing three artificial neural networks for wind speed forecasting , 2010 .

[5]  Fong-Lin Chu,et al.  Analyzing and forecasting tourism demand with ARAR algorithm , 2008 .

[6]  B. LeBaron,et al.  A test for independence based on the correlation dimension , 1996 .

[7]  Wei-Chiang Hong,et al.  An Improved Neural Network Model in Forecasting Arrivals , 2005 .

[8]  A. Shabri,et al.  A comparison of time series forecasting using support vector machine and artificial neural network model , 2010 .

[9]  Emanuel Parzen,et al.  ARARMA models for time series analysis and forecasting , 1982 .

[10]  İnci Okumuş,et al.  Current status of wind energy forecasting and a hybrid method for hourly predictions , 2016 .

[11]  Richard A. Davis,et al.  ITSM: An Interactive Time Series Modelling Package for the PC , 1991, Springer Berlin Heidelberg.

[12]  D. M. Vinod Kumar,et al.  A Hybrid Forecasting Model Based on Artificial Neural Network and Teaching Learning Based Optimization Algorithm for Day-Ahead Wind Speed Prediction , 2019 .

[13]  Tingting Zhu,et al.  Short-term wind speed forecasting using empirical mode decomposition and feature selection , 2016 .

[14]  Abbas Parsaie,et al.  Prediction of discharge coefficient of combined weir-gate using ANN, ANFIS and SVM , 2019, International Journal of Hydrology Science and Technology.

[15]  R. Saidur,et al.  Application of support vector machine models for forecasting solar and wind energy resources: A review , 2018, Journal of Cleaner Production.

[16]  Qing Cao,et al.  Forecasting wind speed with recurrent neural networks , 2012, Eur. J. Oper. Res..

[17]  Nabil Benoudjit,et al.  Multiple architecture system for wind speed prediction , 2011 .

[18]  Carolina M. Affonso,et al.  Hybrid Approach Combining SARIMA and Neural Networks for Multi-Step Ahead Wind Speed Forecasting in Brazil , 2018, IEEE Access.

[19]  Aijun Hu,et al.  Forecasting Short-Term Wind Speed Based on IEWT-LSSVM Model Optimized by Bird Swarm Algorithm , 2019, IEEE Access.

[20]  Jirapan Liangrokapart,et al.  A STUDY OF THE HYBRID MODEL PERFORMANCE FOR TIME SERIES FORECASTING , 2018 .

[21]  Ping-Feng Pai,et al.  Time series forecasting by a seasonal support vector regression model , 2010, Expert Syst. Appl..

[22]  Jianzhou Wang,et al.  A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China , 2016 .

[23]  Jing Shi,et al.  Fine tuning support vector machines for short-term wind speed forecasting , 2011 .

[24]  M. Ahmed,et al.  A review of wind energy potential in Sindh, Pakistan , 2019, 5TH INTERNATIONAL CONFERENCE ON ENERGY, ENVIRONMENT AND SUSTAINABLE DEVELOPMENT (EESD-2018).

[25]  Osamah Basheer Shukur,et al.  Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA , 2015 .

[26]  Carlos Gershenson,et al.  Wind speed forecasting for wind farms: A method based on support vector regression , 2016 .

[27]  R. Kavasseri,et al.  Day-ahead wind speed forecasting using f-ARIMA models , 2009 .

[28]  Haikun Wei,et al.  A Gaussian process regression based hybrid approach for short-term wind speed prediction , 2016 .

[29]  Hussin Nor Hafizah,et al.  ARAR Algorithm In Forecasting Electricity Load Demand In Malaysia , 2016 .

[30]  G. U. Ebuh,et al.  Modified Wilcoxon Signed-Rank Test , 2012 .

[31]  Mojtaba Qolipour,et al.  Performance of different hybrid algorithms for prediction of wind speed behavior , 2019 .

[32]  Christopher Heard,et al.  Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model , 2016 .

[33]  O. D. Ohijeagbon,et al.  New model to estimate daily global solar radiation over Nigeria , 2014 .

[34]  Guoqiang Peter Zhang,et al.  Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.

[35]  Khanji Harijan,et al.  Modeling of Future Electricity Generation and Emissions Assessment for Pakistan , 2019, Processes.

[36]  Hui Liu,et al.  Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction , 2012 .

[37]  Ergin Erdem,et al.  ARMA based approaches for forecasting the tuple of wind speed and direction , 2011 .

[38]  Erasmo Cadenas,et al.  Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model , 2010 .

[39]  Lucas Borges Ferreira,et al.  Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM – A new approach , 2019, Journal of Hydrology.

[40]  Claire Y. Barlow,et al.  Wind turbine blade end-of-life options: An eco-audit comparison , 2019, Journal of Cleaner Production.

[41]  Mohamed Mohandes,et al.  Support vector machines for wind speed prediction , 2004 .

[42]  Edward Maibach,et al.  Fossil fuels are harming our brains: identifying key messages about the health effects of air pollution from fossil fuels , 2019, BMC Public Health.

[43]  Stefan Rüping,et al.  SVM Kernels for Time Series Analysis , 2001 .

[44]  Ali Mostafaeipour,et al.  Forecasting the wind power generation using Box–Jenkins and hybrid artificial intelligence , 2019, International Journal of Energy Sector Management.

[45]  Haiyan Lu,et al.  A case study on a hybrid wind speed forecasting method using BP neural network , 2011, Knowl. Based Syst..

[46]  Ziming Zhu,et al.  Short term forecast of wind power generation based on SVM with pattern matching , 2016, 2016 IEEE International Energy Conference (ENERGYCON).

[47]  Kyungdoo “Ted” Nam,et al.  FORECASTING INTERNATIONAL AIRLINE PASSENGER TRAFFIC USING NEURAL NETWORKS , 1995 .

[48]  Xiping Wang,et al.  A Hybrid Neural Network and ARIMA Model for Energy Consumption Forecasting , 2014 .

[49]  Deepa Subramaniam Nachimuthu,et al.  New SVM kernel soft computing models for wind speed prediction in renewable energy applications , 2020, Soft Comput..

[50]  Wenyu Zhang,et al.  A novel hybrid approach for wind speed prediction , 2014, Inf. Sci..