Novel forecasting model based on improved wavelet transform, informative feature selection, and hybrid support vector machine on wind power forecasting

Novel forecasting model based on improved wavelet transform, informative feature selection, and... Wind speed/power prediction plays an important role in large-scale wind power penetration because of the wind volatility and uncertainty. In this paper, an accurate forecast model is presented based on improved wavelet transform, informative feature selection and hybrid forecast engine. The proposed forecasting engine is based on support vector machine which is an appropriate prediction forecast engine due to its ability to discover natural structures of wind speed/power variation. The mentioned forecast engine is equipped with an intelligent algorithm and enhances its prediction accuracy. For this purpose, we applied a new version of enhanced particle swarm optimization in this work as the optimization algorithm. Effectiveness of the proposed forecast model is extensively evaluated by real-world electricity market through comparison with well-known forecasting methods. Obtained numerical results and analysis demonstrate the validity and superiority of the proposed method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Ambient Intelligence and Humanized Computing Springer Journals

Novel forecasting model based on improved wavelet transform, informative feature selection, and hybrid support vector machine on wind power forecasting

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Robotics and Automation; User Interfaces and Human Computer Interaction
ISSN
1868-5137
eISSN
1868-5145
D.O.I.
10.1007/s12652-018-0886-0
Publisher site
See Article on Publisher Site

Abstract

Wind speed/power prediction plays an important role in large-scale wind power penetration because of the wind volatility and uncertainty. In this paper, an accurate forecast model is presented based on improved wavelet transform, informative feature selection and hybrid forecast engine. The proposed forecasting engine is based on support vector machine which is an appropriate prediction forecast engine due to its ability to discover natural structures of wind speed/power variation. The mentioned forecast engine is equipped with an intelligent algorithm and enhances its prediction accuracy. For this purpose, we applied a new version of enhanced particle swarm optimization in this work as the optimization algorithm. Effectiveness of the proposed forecast model is extensively evaluated by real-world electricity market through comparison with well-known forecasting methods. Obtained numerical results and analysis demonstrate the validity and superiority of the proposed method.

Journal

Journal of Ambient Intelligence and Humanized Computing – Springer Journals

Published: Jun 1, 2018

References

  • Improved time varying inertia weight PSO for solved economic load dispatch with subsidies and wind power effects
    Abedinia, O; Ghasemi, A; Ojaroudi, N
  • Intelligent controller based wide-area control in power system
    Abedinia, O; Bekravi, M; Ghadimi, N
  • Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm
    Abedinia, O; Amjady, N; Ghadimi, N
  • Multi-objective energy management in a micro-grid
    Aghajani, G; Ghadimi, N
  • Extracting appropriate nodal marginal prices for all types of committed reserve
    Akbary, P; Ghiasi, M; Pourkheranjani, MRR; Alipour, H; Ghadimi, N
  • Short term wind power prediction based on improved Kriging interpolation, empirical mode decomposition, and closed-loop forecasting engine
    Amjady, N; Abedinia, O
  • Long-term wind speed and power forecasting using local recurrent neural network models
    Barbounis, TG; Theocharis, JB; Alexiadis, MC; Dokopoulos, PS
  • Optimization of hybrid renewable energy power systems: a review
    Bhandari, B; Lee, KT; Lee, GY; Cho, YM; Ahn, SH
  • Hybrid wavelet-PSO-ANFIS approach for short-term wind power forecasting in Portugal
    Catalao, JPS; Pousinho, HMI; Mendes, VMF
  • Short-term power output forecasting of hourly operation in power plant based on climate factors and effects of wind direction and wind speed
    Dadkhah, M; Rezaee, MJ; Chavoshi, AZ
  • Error analysis of short term wind power prediction models
    Giorgi, MG; Ficarella, A; Tarantino, M
  • Pattern-based local linear regression models for short-term load forecasting
    Dudek, G
  • The price prediction for the energy market based on a new method
    Ebrahimian, H; Barmayoon, S; Mohammadi, M; Ghadimi, N
  • Hybrid wind power balance control strategy using thermal power, hydro power and flow batteries
    Gelažanskas, L; Baranauskas, A; Gamage, KA; Ažubalis, M
  • A new hybrid algorithm based on optimal fuzzy controller in multimachine power system
    Ghadimi, N
  • Short-term management of hydro-power systems based on uncertainty model in electricity markets
    Ghadimi, N; Firouz, MH
  • A PSO-based fuzzy long-term multi-objective optimization approach for placement and parameter setting of UPFC
    Ghadimi, N; Afkousi-Paqaleh, A; Emamhosseini, A
  • A new prediction model based on multi-block forecast engine in smart grid
    Ghadimi, N; Akbarimajd, A; Shayeghi, H; Abedinia, O
  • Application of a new hybrid forecast engine with feature selection algorithm in a power system
    Ghadimi, N; Akbarimajd, A; Shayeghi, H; Abedinia, O
  • A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets
    Gollou, AR; Ghadimi, N
  • A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on Mixed Integer Genetic Algorithm
    Hamian, M; Darvishan, A; Hosseinzadeh, M; Lariche, MJ; Ghadimi, N; Nouri, A
  • A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting
    Hassan, S; Khosravi, A; Jaafar, J; Khanesar, MA
  • Short-term wind speed forecasting using a hybrid model
    Jiang, P; Wang, Y; Wang, J
  • Hybrid methodologies for electricity load forecasting: entropy-based feature selection with machine learning and soft computing techniques
    Jurado, S; Nebot, À; Mugica, F; Avellana, N
  • Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming
    Khodaei, H; Hajiali, M; Darvishan, A; Sepehr, M; Ghadimi, N
  • A review on the forecasting of wind speed and generated power
    Lei, M; Shiyan, L; Chuanwen, J; Hongling, L; Yan, Z
  • A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting
    Leng, H; Li, X; Zhu, J; Tang, H; Zhang, Z; Ghadimi, N
  • Electricity load forecasting by an improved forecast engine for building level consumers
    Liu, Y; Wang, W; Ghadimi, N
  • A new prediction model of battery and wind-solar output in hybrid power system
    Mirzapour, F; Lakzaei, M; Varamini, G; Teimourian, M; Ghadimi, N
  • Optimal location and optimized parameters for robust power system stabilizer using honeybee mating optimization
    Mohammadi, M; Ghadimi, N
  • Short-term wind speed syntheses correcting forecasting model and its application
    Nan, X; Li, Q; Qiu, D; Zhao, Y; Guo, X
  • A new method for probabilistic assessments in power systems, combining monte carlo and stochastic–algebraic methods
    Noruzi, A; Banki, T; Abedinia, O; Ghadimi, N
  • Environmental economic dispatch using improved artificial bee colony algorithm
    Sharifi, S; Sedaghat, M; Farhadi, P; Ghadimi, N; Taheri, B
  • Using the ensemble Kalman filter for electricity load forecasting and analysis
    Takeda, H; Tamura, Y; Sato, S
  • Schedule of air-conditioning systems with thermal energy storage considering wind power forecast errors
    Tang, Y; Zhong, J; Bollen, M
  • The particle swarm optimization algorithm: convergence analysis and parameter selection
    Trelea, IC
  • A review of wind power forecasting models
    Wang, X; Guo, P; Huang, X
  • Statistical distribution for wind power forecast error and its application to determine optimal size of energy storage system
    Wu, J; Zhang, B; Li, H; Li, Z; Chen, Y; Miao, X
  • Forecasting wind power in the Mai Liao Wind Farm based on the multi-layer perceptron artificial neural network model with improved simplified swarm optimization
    Yeh, WC; Yeh, YM; Chang, PC; Ke, YC; Chung, V
  • Prediction of wind farm power ramp rates: a data-mining approach
    Zheng, H; Kusiak, A

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