Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms

To support regional electricity markets, accurate and reliable energy demand (G) forecast models are vital stratagems for stakeholders in this sector. An online sequential extreme learning machine (OS-ELM) model integrated with a maximum overlap discrete wavelet transform (MODWT) algorithm was developed using daily G data obtained from three regional campuses (i.e., Toowoomba, Ipswich, and Springfield) at the University of Southern Queensland, Australia. In training the objective and benchmark models, the partial autocorrelation function (PACF) was first employed to select the most significant lagged input variables that captured historical fluctuations in the G time-series data. To address the challenges of non-stationarities associated with the model development datasets, a MODWT technique was adopted to decompose the potential model inputs into their wavelet and scaling coefficients before executing the OS-ELM model. The MODWT-PACF-OS-ELM (MPOE) performance was tested and compared with the non-wavelet equivalent based on the PACF-OS-ELM (POE) model using a range of statistical metrics, including, but not limited to, the mean absolute percentage error (MAPE%). For all of the three datasets, a significantly greater accuracy was achieved with the MPOE model relative to the POE model resulting in an MAPE = 4.31% vs. MAPE = 11.31%, respectively, for the case of the Toowoomba dataset, and a similarly high performance for the other two campuses. Therefore, considering the high efficacy of the proposed methodology, the study claims that the OS-ELM model performance can be improved quite significantly by integrating the model with the MODWT algorithm.

[1]  Ravinesh C. Deo,et al.  Self-adaptive differential evolutionary extreme learning machines for long-term solar radiation prediction with remotely-sensed MODIS satellite and Reanalysis atmospheric products in solar-rich cities , 2018, Remote Sensing of Environment.

[2]  John Quilty,et al.  Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new forecasting framework , 2018, Journal of Hydrology.

[3]  Khubaib Amjad Alam,et al.  Support vector regression based prediction of global solar radiation on a horizontal surface , 2015 .

[4]  Mohd Tahir Ismail,et al.  A Comparative Study between Discrete Wavelet Transform and Maximal Overlap Discrete Wavelet Transform for Testing Stationarity , 2013 .

[5]  Jan Adamowski,et al.  Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model , 2018, Stochastic Environmental Research and Risk Assessment.

[6]  Sancho Salcedo-Sanz,et al.  Daily global solar radiation prediction based on a hybrid Coral Reefs Optimization – Extreme Learning Machine approach , 2014 .

[7]  Zichen Zhang,et al.  A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting , 2018 .

[8]  R. Deo,et al.  Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm , 2017 .

[9]  Hongbin Liu,et al.  General models for estimating daily global solar radiation for different solar radiation zones in mainland China , 2013 .

[10]  M. Katz.  Validation of models , 2006 .

[11]  Z. Wan,et al.  Quality assessment and validation of the MODIS global land surface temperature , 2004 .

[12]  T. Chai,et al.  Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature , 2014 .

[13]  Sancho Salcedo-Sanz,et al.  Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm – Extreme Learning Machine approach , 2016 .

[14]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[15]  Z. Tan,et al.  Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models , 2010 .

[16]  A.J. Conejo,et al.  Day-ahead electricity price forecasting using the wavelet transform and ARIMA models , 2005, IEEE Transactions on Power Systems.

[17]  Wei-Chiang Hong,et al.  SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting , 2011 .

[18]  Ravinesh C. Deo,et al.  Wavelet-based 3-phase hybrid SVR model trained with satellite-derived predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction , 2019, Renewable and Sustainable Energy Reviews.

[19]  Basant Yadav,et al.  Discharge forecasting using an Online Sequential Extreme Learning Machine (OS-ELM) model: A case study in Neckar River, Germany , 2016 .

[20]  X. Wen,et al.  Wavelet Analysis-Support Vector Machine Coupled Models for Monthly Rainfall Forecasting in Arid Regions , 2015, Water Resources Management.

[21]  R. Deo,et al.  Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland , 2017 .

[22]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[23]  Ravinesh C. Deo,et al.  Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction , 2019, Energies.

[24]  Youngmin Seo,et al.  River Stage Modeling by Combining Maximal Overlap Discrete Wavelet Transform, Support Vector Machines and Genetic Algorithm , 2017 .

[25]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[26]  D. Legates,et al.  Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation , 1999 .

[27]  Rahim Barzegar,et al.  Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models. , 2017, The Science of the total environment.

[28]  Ravinesh C. Deo,et al.  Multi-stage hybridized online sequential extreme learning machine integrated with Markov Chain Monte Carlo copula-Bat algorithm for rainfall forecasting , 2018, Atmospheric Research.

[29]  X. Wen,et al.  A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset , 2016 .

[30]  Wei-Chiang Hong,et al.  Electric Load Forecasting by Hybrid Self-Recurrent Support Vector Regression Model With Variational Mode Decomposition and Improved Cuckoo Search Algorithm , 2020, IEEE Access.

[31]  C. Willmott.  Some Comments on the Evaluation of Model Performance , 1982 .

[32]  Zhao Yang Dong,et al.  An adaptive neural-wavelet model for short term load forecasting , 2001 .

[33]  Chandranath Chatterjee,et al.  Development of an accurate and reliable hourly flood forecasting model using wavelet–bootstrap–ANN (WBANN) hybrid approach , 2010 .

[34]  Yuan Lan,et al.  Ensemble of online sequential extreme learning machine , 2009, Neurocomputing.

[35]  Jan Adamowski,et al.  Urban water demand forecasting and uncertainty assessment using ensemble wavelet‐bootstrap‐neural network models , 2013 .

[36]  Michael R. Chernick,et al.  Wavelet Methods for Time Series Analysis , 2001, Technometrics.

[37]  Sancho Salcedo-Sanz,et al.  Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization – Extreme learning machine approach , 2014 .

[38]  P. Krause,et al.  COMPARISON OF DIFFERENT EFFICIENCY CRITERIA FOR HYDROLOGICAL MODEL ASSESSMENT , 2005 .

[39]  Robert J. Abrahart,et al.  HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts , 2007, Environ. Model. Softw..

[40]  John O. Carter,et al.  Using spatial interpolation to construct a comprehensive archive of Australian climate data , 2001, Environ. Model. Softw..

[41]  Alexandre Bryan Heinemann,et al.  Sensitivity of APSIM/ORYZA model due to estimation errors in solar radiation , 2012 .

[42]  Mohanad S. Al-Musaylh,et al.  Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting , 2018 .

[43]  Tomonobu Senjyu,et al.  Neural-wavelet Approach for Short Term Price Forecasting in Deregulated Power Market , 2011 .

[44]  Narasimhan Sundararajan,et al.  A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks , 2006, IEEE Transactions on Neural Networks.

[45]  Vinit Sehgal,et al.  Effect of Utilization of Discrete Wavelet Components on Flood Forecasting Performance of Wavelet Based ANFIS Models , 2014, Water Resources Management.

[46]  Rathinasamy Maheswaran,et al.  Comparative study of different wavelets for hydrologic forecasting , 2012, Comput. Geosci..

[47]  A. Castelletti,et al.  Tree‐based iterative input variable selection for hydrological modeling , 2013 .

[48]  Cort J. Willmott,et al.  On the Evaluation of Model Performance in Physical Geography , 1984 .

[49]  Jan Adamowski,et al.  Multiscale streamflow forecasting using a new Bayesian Model Average based ensemble multi-wavelet Volterra nonlinear method , 2013 .

[50]  Yan Li,et al.  Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia , 2018, Adv. Eng. Informatics.

[51]  Z. Wan.  MODIS Land-Surface Temperature Algorithm Theoretical Basis Document (LST ATBD) , 1999 .

[52]  Ravinesh C. Deo,et al.  Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia , 2019, Renewable and Sustainable Energy Reviews.

[53]  C. Willmott,et al.  A refined index of model performance , 2012 .

[54]  C. W. Tong,et al.  A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation , 2015 .