Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Linear optimal weighting estimator (LOWE) for efficient parallel hybridization of load forecasts

Linear optimal weighting estimator (LOWE) for efficient parallel hybridization of load forecasts Improving the accuracy and reducing computational costs of predictions, especially the prediction of time series, is one of the most critical parts of the decision-making processes and management in different areas and organizations. One of the best solutions to achieve high accuracy and low computational costs in time series forecasting is to develop and use efficient hybrid methods. Among the combined methods, parallel hybrid approaches are more welcomed by scholars and often have better performance than sequence ones. However, the necessary condition of using parallel combinational approaches is to estimate the appropriate weight of components. This weighting stage of parallel hybrid models is the most effective factor in forecasting accuracy as well as computational costs. In the literature, meta-heuristic algorithms have often been applied to weight components of parallel hybrid models. However, such that algorithms, despite all unique advantages, have two serious disadvantages of local optima and iterative time-consuming optimization processes. The purpose of this paper is to develop a linear optimal weighting estimator (LOWE) algorithm for finding the desired weight of components in the global non-iterative universal manner.Design/methodology/approachIn this paper, a LOWE algorithm is developed to find the desired weight of components in the global non-iterative universal manner.FindingsEmpirical results indicate that the accuracy of the LOWE-based parallel hybrid model is significantly better than meta-heuristic and simple average (SA) based models. The proposed weighting approach can improve 13/96%, 11/64%, 9/35%, 25/05% the performance of the differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO) and SA-based parallel hybrid models in electricity load forecasting. While, its computational costs are considerably lower than GA, PSO and DE-based parallel hybrid models. Therefore, it can be considered as an appropriate and effective alternative weighing technique for efficient parallel hybridization for time series forecasting.Originality/valueIn this paper, a LOWE algorithm is developed to find the desired weight of components in the global non-iterative universal manner. Although it can be generally demonstrated that the performance of the proposed weighting technique will not be worse than the meta-heuristic algorithm, its performance is also practically evaluated in real-world data sets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Modelling in Management Emerald Publishing

Linear optimal weighting estimator (LOWE) for efficient parallel hybridization of load forecasts

Download PDF
21 pages

Loading next page...
 
/lp/emerald-publishing/linear-optimal-weighting-estimator-lowe-for-efficient-parallel-r8UT0Zl0ei
Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1746-5664
eISSN
1746-5664
DOI
10.1108/jm2-05-2021-0116
Publisher site
See Article on Publisher Site

Abstract

Improving the accuracy and reducing computational costs of predictions, especially the prediction of time series, is one of the most critical parts of the decision-making processes and management in different areas and organizations. One of the best solutions to achieve high accuracy and low computational costs in time series forecasting is to develop and use efficient hybrid methods. Among the combined methods, parallel hybrid approaches are more welcomed by scholars and often have better performance than sequence ones. However, the necessary condition of using parallel combinational approaches is to estimate the appropriate weight of components. This weighting stage of parallel hybrid models is the most effective factor in forecasting accuracy as well as computational costs. In the literature, meta-heuristic algorithms have often been applied to weight components of parallel hybrid models. However, such that algorithms, despite all unique advantages, have two serious disadvantages of local optima and iterative time-consuming optimization processes. The purpose of this paper is to develop a linear optimal weighting estimator (LOWE) algorithm for finding the desired weight of components in the global non-iterative universal manner.Design/methodology/approachIn this paper, a LOWE algorithm is developed to find the desired weight of components in the global non-iterative universal manner.FindingsEmpirical results indicate that the accuracy of the LOWE-based parallel hybrid model is significantly better than meta-heuristic and simple average (SA) based models. The proposed weighting approach can improve 13/96%, 11/64%, 9/35%, 25/05% the performance of the differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO) and SA-based parallel hybrid models in electricity load forecasting. While, its computational costs are considerably lower than GA, PSO and DE-based parallel hybrid models. Therefore, it can be considered as an appropriate and effective alternative weighing technique for efficient parallel hybridization for time series forecasting.Originality/valueIn this paper, a LOWE algorithm is developed to find the desired weight of components in the global non-iterative universal manner. Although it can be generally demonstrated that the performance of the proposed weighting technique will not be worse than the meta-heuristic algorithm, its performance is also practically evaluated in real-world data sets.

Journal

Journal of Modelling in Management – Emerald Publishing

Published: Aug 22, 2022

Keywords: Weighting estimators; Time series forecasting; Hybridization structures; Meta-heuristic optimization algorithm; Global non-iterative optimization processes; Load forecasting; Artificial intelligence; Energy; Forecasting

References

  • Long-term/mid-term electric load forecasting based on short-term correlation and annual growth
    Al-Hamadi, H.M.; Soliman, S.A.
  • The combination of forecasts
    Bates, J.M.; Granger, C.W.J.
  • Electric load forecasting use a novelty hybrid model on the basic of data preprocessing technique and multi-objective optimization algorithm
    Bo, H.; Nie, Y.; Wang, J.
  • Comparison of the forecasting accuracy of neural networks with other established techniques
    Brace, M.C.; Schmidt, J.; Hadlin, M.
  • 24-hours demand forecasting based on SARIMA and support vector machines
    Braun, M.; Bernard, T.; Piller, O.; Sedehizade, F.
  • A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression
    Cai, Q.; Zhang, D.; Zheng, W.; Leung, S.C.
  • Forecasting the behavior of multivariate time series using neural networks
    Chakraborty, K.; Mehrotra, K.; Mohan, C.K.; Ranka, S.
  • A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan
    Chen, K.Y.; Wang, C.H.
  • Analysis of univariate time series with connectionist nets: a case study of two classical examples
    De Groot, C.; Diethelm, W.
  • A granular time series approach to long-term forecasting and trend forecasting
    Dong, R.; Pedrycz, W.
  • Modelling monthly rainfall data of Port Harcourt, Nigeria by seasonal box-Jenkins methods
    Etuk, E.H.
  • A hybrid neural network and ARIMA model for water quality time series prediction
    Faruk, D.Ö.
  • Neural network forecasting of short, noisy time series
    Foster, W.R.; Collopy, F.; Ungar, L.H.
  • Functional clustering and linear regression for peak load forecasting
    Goia, A.; May, C.; Fusai, G.
  • A new hybrid model for short-term electricity load forecasting
    Haq, M.R.; Ni, Z.
  • Electric load forecasting by support vector model
    Hong, W.C.
  • Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm – a case study of papermaking process
    Hu, Y.; Li, J.; Hong, M.; Ren, J.; Lin, R.; Liu, Y.; Liu, M.; Man, Y.
  • A comprehensive low-risk and cost parallel hybrid method for electricity load forecasting
    Khashei, M.; Chahkoutahi, F.
  • Improvement of auto-regressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs)
    Khashei, M.; Bijari, M.; Ardali, G.A.R.
  • An artificial neural network (p, d, q) model for time series forecasting
    Khashei, M.; Bijari, M.
  • A novel hybridization of artificial neural networks and ARIMA models for time series forecasting
    Khashei, M.; Bijari, M.
  • Joint bagged-boosted artificial neural networks: using ensemble machine learning to improve short-term electricity load forecasting
    Khwaja, A.S.; Anpalagan, A.; Naeem, M.; Venkatesh, B.
  • Short term load forecasting based on feature extraction and improved general regression neural network model
    Liang, Y.; Niu, D.; Hong, W.C.
  • A fuzzy inference model for short-term load forecasting
    Mamlook, R.; Badran, O.; Abdulhadi, E.
  • Forecasting electricity consumption in New Zealand using economic and demographic variables
    Mohamed, Z.; Bodger, P.
  • Measuring the bullwhip effect for supply chains with seasonal demand components
    Nagaraja, C.H.; Thavaneswaran, A.; Appadoo, S.S.
  • An aggregative machine learning approach for output power prediction of wind turbines
    Netsanet, S.; Zhang, J.; Zheng, D.; Agrawal, R.K.; Muchahary, F.
  • Power load forecasting using support vector machine and ant colony optimization
    Niu, D.; Wang, Y.; Wu, D.D.
  • Forecasting energy consumption in Taiwan using hybrid nonlinear models
    Pao, H.T.
  • Electricity demand loads modeling using Auto Regressive Moving Average (ARMA) models
    Pappas, S.S.; Ekonomou, L.; Karamousantas, D.C.; Chatzarakis, G.E.; Katsikas, S.K.; Liatsis, P.
  • Oil price forecasting using a hybrid model
    Safari, A.; Davallou, M.
  • A new hybrid method for time series forecasting: AR–ANFIS
    Sarıca, B.; Eğrioğlu, E.; Aşıkgil, B.
  • Evaluation of hybrid forecasting approaches for wind speed and power generation time series
    Shi, J.; Guo, J.; Zheng, S.
  • Methodology for long-term prediction of time series
    Sorjamaa, A.; Hao, J.; Reyhani, N.; Ji, Y.; Lendasse, A.
  • Techniques of applying wavelet de-noising into a combined model for short-term load forecasting
    Wang, J.; Wang, J.; Li, Y.; Zhu, S.; Zhao, J.
  • Combined modeling for electric load forecasting with adaptive particle swarm optimization
    Wang, J.; Zhu, S.; Zhang, W.; Lu, H.
  • Stock index forecasting based on a hybrid model
    Wang, J.J.; Wang, J.Z.; Zhang, Z.G.; Guo, S.P.
  • Back propagation neural network with adaptive differential evolution algorithm for time series forecasting
    Wang, L.; Zeng, Y.; Chen, T.
  • A novel hybrid model based on least square support vector machine and weight coefficients optimization: a case study of short-term electric load forecasting
    Wang, Y.; Yang, Y.; Li, C.; Chen, Y.; Li, L.
  • Forecasting spot electricity prices: a comparison of parametric and semiparametric time series models
    Weron, R.; Misiorek, A.
  • A short-term load forecasting method based on GRUCNN hybrid neural network model
    Wu, L.; Kong, C.; Hao, X.; Chen, W.
  • A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting
    Xiao, L.; Shao, W.; Liang, T.; Wang, C.
  • Operational photovoltaics power forecasting using seasonal time series ensemble
    Yang, D.; Dong, Z.
  • Modelling a combined method based on ANFIS and neural network improved by DE algorithm: a case study for short-term electricity demand forecasting
    Yang, Y.; Chen, Y.; Wang, Y.; Li, C.; Li, L.
  • Time series forecasting for building energy consumption using weighted support vector regression with differential evolution optimization technique
    Zhang, F.; Deb, C.; Lee, S.E.; Yang, J.; Shah, K.W.
  • Time series forecasting using a hybrid ARIMA and neural network model
    Zhang, G.P.
  • A neural network ensemble method with jittered training data for time series forecasting
    Zhang, G.P.
  • Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology
    Zhu, B.; Wei, Y.
  • A seasonal hybrid procedure for electricity demand forecasting in China
    Zhu, S.; Wang, J.; Zhao, W.; Wang, J.