A New Hybrid Approach for Short-Term Electric Load Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Whale Optimization

被引:20
作者
Liu, Tongxiang [1 ]
Jin, Yu [2 ]
Gao, Yuyang [2 ]
机构
[1] Univ Adelaide, Fac Profess, Adelaide, SA 5000, Australia
[2] Dongbei Univ Finance & Econ, Sch Stat, Dalian 116025, Peoples R China
关键词
electric load forecasting; ensemble empirical mode decomposition; whale optimization; support vector machine; WAVELET TRANSFORM; NEURAL-NETWORK; EXPERT-SYSTEM; COMBINATION; ALGORITHM; ARIMA; INTELLIGENCE; METHODOLOGY; SELECTION;
D O I
10.3390/en12081520
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Electrical power system forecasting has been a main focus for researchers who want to improve the effectiveness of a power station. Although some traditional models have been proved suitable for short-term electric load forecasting, its nature of ignoring the significance of parameter optimization and data preprocessing usually results in low forecasting accuracy. This paper proposes a short-term hybrid forecasting approach which consists of the three following modules: Data preprocessing, parameter optimization algorithm, and forecasting. This hybrid model overcomes the disadvantages of the conventional model and achieves high forecasting performance. To verify the forecasting effectiveness of the hybrid method, 30-minutes of electric load data from power stations in New South Wales and Queensland are used for conducting experiments. A comprehensive evaluation, including a Diebold-Mariano (DM) test and forecasting effectiveness, is applied to verify the ability of the hybrid approach. Experimental results indicated that the new hybrid method can perform accurate electric load forecasting, which can be regarded as a powerful assist in managing smart grids.
引用
收藏
页数:20
相关论文
共 62 条
  • [1] 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
    AL-Musaylh, Mohanad S.
    Deo, Ravinesh C.
    Li, Yan
    Adamowski, Jan F.
    [J]. APPLIED ENERGY, 2018, 217 : 422 - 439
  • [2] Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia
    Al-Musaylh, Mohanad S.
    Deo, Ravinesh C.
    Adarnowski, Jan F.
    Li, Yan
    [J]. ADVANCED ENGINEERING INFORMATICS, 2018, 35 : 1 - 16
  • [3] Short-term hourly load forecasting using time-series modeling with peak load estimation capability
    Amjady, N
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2001, 16 (04) : 798 - 805
  • [4] [Anonymous], P 2003 S APPL INT
  • [5] Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm
    Bahrami, Saadat
    Hooshmand, Rahmat-Allah
    Parastegari, Moein
    [J]. ENERGY, 2014, 72 : 434 - 442
  • [6] COMBINATION OF FORECASTS
    BATES, JM
    GRANGER, CWJ
    [J]. OPERATIONAL RESEARCH QUARTERLY, 1969, 20 (04) : 451 - &
  • [7] Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system
    Bennett, Christopher J.
    Stewart, Rodney A.
    Lu, Jun Wei
    [J]. ENERGY, 2014, 67 : 200 - 212
  • [8] Short term load forecast using fuzzy logic and wavelet transform integrated generalized neural network
    Chaturvedi, D. K.
    Sinha, A. P.
    Malik, O. P.
    [J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2015, 67 : 230 - 237
  • [9] Short-term load forecasting using a kernel-based support vector regression combination model
    Che, JinXing
    Wang, JianZhou
    [J]. APPLIED ENERGY, 2014, 132 : 602 - 609
  • [10] Chen H. S., 1998, J GREY SYSTEM, V1, P141