Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors

被引:22
|
作者
Piotrowski, Pawel [1 ]
Rutyna, Inajara [1 ,2 ]
Baczynski, Dariusz [1 ]
Kopyt, Marcin [1 ]
机构
[1] Warsaw Univ Technol, Elect Power Engn Inst, Koszykowa 75 St, PL-00662 Warsaw, Poland
[2] Ideas NCBR Sp Zoo, Nowogrodzka St 47A, PL-00695 Warsaw, Poland
关键词
forecasting error; evaluation criteria metrics; wind power forecasting; wind turbine; wind farm; statistical analysis of errors; hybrid methods; ensemble methods; machine learning; deep neural network; NEURAL-NETWORK; PERFORMANCE EVALUATION; PREDICTION; MODEL; SPEED; ALGORITHM; GENERATION; STRATEGY; OPTIMIZATION; TRANSFORM;
D O I
10.3390/en15249657
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
Power generation forecasts for wind farms, especially with a short-term horizon, have been extensively researched due to the growing share of wind farms in total power generation. Detailed forecasts are necessary for the optimization of power systems of various sizes. This review and analytical paper is largely focused on a statistical analysis of forecasting errors based on more than one hundred papers on wind generation forecasts. Factors affecting the magnitude of forecasting errors are presented and discussed. Normalized root mean squared error (nRMSE) and normalized mean absolute error (nMAE) have been selected as the main error metrics considered here. A new and unique error dispersion factor (EDF) is proposed, being the ratio of nRMSE to nMAE. The variability of EDF depending on selected factors (size of wind farm, forecasting horizons, and class of forecasting method) has been examined. This is unique and original research, a novelty in studies on errors of power generation forecasts in wind farms. In addition, extensive quantitative and qualitative analyses have been conducted to assess the magnitude of forecasting error depending on selected factors (such as forecasting horizon, wind farm size, and a class of the forecasting method). Based on these analyses and a review of more than one hundred papers, a unique set of recommendations on the preferred content of papers addressing wind farm generation forecasts has been developed. These recommendations would make it possible to conduct very precise benchmarking meta-analyses of forecasting studies described in research papers and to develop valuable general conclusions concerning the analyzed phenomena.
引用
收藏
页数:38
相关论文
共 50 条
  • [41] Comprehensive evaluation model of wind power accommodation ability based on macroscopic and microscopic indicators
    Li, Gengyin
    Li, Guodong
    Zhou, Ming
    PROTECTION AND CONTROL OF MODERN POWER SYSTEMS, 2019, 4 (01)
  • [42] Advancements in wind power forecasting: A comprehensive review of artificial intelligence-based approaches
    Kumar K.
    Prabhakar P.
    Verma A.
    Saroha S.
    Singh K.
    Multimedia Tools and Applications, 2025, 84 (10) : 8331 - 8360
  • [43] Statistical Analysis of Wind Power Using Weibull Distribution to Maximize Energy Yield
    Aldaoudeyeh, Al-Motasem, I
    Alzaareer, Khaled
    2020 IEEE PES & IAS POWERAFRICA CONFERENCE, 2020,
  • [44] Evaluation of Wind Resources Potential and Economic Analysis of Wind Power Generation in South Africa
    Adefarati, T.
    Obikoya, G. D.
    INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH IN AFRICA, 2019, 44 : 150 - 181
  • [45] Decomposition-based wind power forecasting models and their boundary issue: An in-depth review and comprehensive discussion on potential solutions
    Chen, Yinsong
    Yu, Samson
    Islam, Shama
    Lim, Chee Peng
    Muyeen, S. M.
    ENERGY REPORTS, 2022, 8 : 8805 - 8820
  • [46] Daily Average Wind Power Interval Forecasts Based on an Optimal Adaptive-Network-Based Fuzzy Inference System and Singular Spectrum Analysis
    Zhang, Zhongrong
    Song, Yiliao
    Liu, Feng
    Liu, Jinpeng
    SUSTAINABILITY, 2016, 8 (02)
  • [47] Spatio-temporal analysis and modeling of short-term wind power forecast errors
    Tastu, Julija
    Pinson, Pierre
    Kotwa, Ewelina
    Madsen, Henrik
    Nielsen, Henrik Aa.
    WIND ENERGY, 2011, 14 (01) : 43 - 60
  • [48] Statistical analysis of wind characteristics based on Weibull methods for estimation of power generation in Lithuania
    Katinas, Vladislovas
    Marciukaitis, Mantas
    Gecevicius, Giedrius
    Markevicius, Antanas
    RENEWABLE ENERGY, 2017, 113 : 190 - 201
  • [49] Meter Placement in Power System NetworkA Comprehensive Review, Analysis and Methodology
    Lekshmana, Ramesh
    Padmanaban, Sanjeevikumar
    Mahajan, Sagar Bhaskar
    Ramachandaramurthy, Vigna K.
    Holm-Nielsen, Jens Bo
    ELECTRONICS, 2018, 7 (11):
  • [50] Enhancing Algorithm Selection through Comprehensive Performance Evaluation: Statistical Analysis of Stochastic Algorithms
    Amin, Azad Arif Hama
    Aladdin, Aso M.
    Hasan, Dler O.
    Mohammed-Taha, Soran R.
    Rashid, Tarik A.
    COMPUTATION, 2023, 11 (11)