On Normality Assumption in Residual Simulation for Probabilistic Load Forecasting

被引:71
作者
Xie, Jingrui [1 ]
Hong, Tao [1 ]
Laing, Thomas [2 ]
Kang, Chongqing [3 ]
机构
[1] Univ North Carolina Charlotte, Energy Prod & Infrastruct Ctr, Charlotte, NC 28223 USA
[2] North Carolina Assoc Elect Cooperat, Raleigh, NC 27616 USA
[3] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
关键词
Electric load forecasting; normal distribution; pinball loss function; residual simulation; RELIABILITY;
D O I
10.1109/TSG.2015.2447007
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Grid modernization has brought in various types of active demand, and intermittent and distributed generation resources to challenge the traditional power system planning and operation practices. As a result, more and more decision making processes rely on probabilistic forecasts as an input. While residual simulation has been recognized as one way to generate probabilistic load forecasts, the research on the application side of probabilistic load forecasting has been heavily relying on unverified distributions of load forecasting residuals, such as normal distribution. In this paper, we study the normality assumption from a different angle. Instead of trying to prove or disprove its validity via hypothesis tests, we attempt to understand whether applying the normality assumption helps improve the quality of probabilistic load forecasts. We apply a proper scoring rule, the pinball loss function, to evaluate a set of probabilistic load forecasts developed from different underlying linear and nonlinear models. To ensure the solidity of our conclusion, we conduct two case studies, one based on data from a large generation and transmission cooperative in the U.S., and the other based on data from the probabilistic load forecasting track of the Global Energy Forecasting Competition 2014.
引用
收藏
页码:1046 / 1053
页数:8
相关论文
共 23 条
[1]   PROBABILISTIC ANALYSIS OF POWER FLOWS [J].
ALLAN, RN ;
BORKOWSKA, B ;
GRIGG, CH .
PROCEEDINGS OF THE INSTITUTION OF ELECTRICAL ENGINEERS-LONDON, 1974, 121 (12) :1551-1556
[2]   EVALUATION METHODS AND ACCURACY IN PROBABILISTIC LOAD FLOW SOLUTIONS [J].
ALLAN, RN ;
DASILVA, AML ;
BURCHETT, RC .
IEEE TRANSACTIONS ON POWER APPARATUS AND SYSTEMS, 1981, 100 (05) :2539-2546
[3]  
[Anonymous], 2010, DISSERTATION
[4]   Effects of load forecast uncertainty on bulk electric system reliability evaluation [J].
Billinton, Roy ;
Huang, Dange .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2008, 23 (02) :418-425
[5]   PROBABILISTIC LOAD FLOW [J].
BORKOWSKA, B .
IEEE TRANSACTIONS ON POWER APPARATUS AND SYSTEMS, 1974, PA93 (03) :752-759
[6]   Load forecasting using support vector machines: A study on EUNITE competition 2001 [J].
Chen, BJ ;
Chang, MW ;
Lin, CJ .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2004, 19 (04) :1821-1830
[7]   Probabilistic load flow: A review [J].
Chen, P. ;
Chen, Z. ;
Bak-Jensen, B. .
2008 THIRD INTERNATIONAL CONFERENCE ON ELECTRIC UTILITY DEREGULATION AND RESTRUCTURING AND POWER TECHNOLOGIES, VOLS 1-6, 2008, :1586-1591
[8]   Probabilistic assessment of interconnection assistance between power systems [J].
Hamoud, G .
IEEE TRANSACTIONS ON POWER SYSTEMS, 1998, 13 (02) :535-540
[9]   Neural networks for short-term load forecasting: A review and evaluation [J].
Hippert, HS ;
Pedreira, CE ;
Souza, RC .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2001, 16 (01) :44-55
[10]   RELIABILITY AND PRODUCTION COST CALCULATION WITH PEAK LOAD FORECAST UNCERTAINTY [J].
HOFFER, J ;
DORFNER, P .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 1991, 13 (04) :223-229