The Use of Probabilistic Forecasts Applying Them in Theory and Practice

被引：39

作者：

Haupt, Sue Ellen ^{[1
]}

Garcia Casado, Mayte ^{[2
]}

Davidson, Michael ^{[3
]}

Dobschinski, Jan ^{[4
]}

Du, Pengwei ^{[5
]}

Lange, Matthias ^{[6
]}

Miller, Timothy ^{[7
]}

Mohrlen, Corinna ^{[8
]}

Motley, Amber ^{[9
]}

Pestana, Rui ^{[10
]}

Zack, John ^{[11
]}

机构：

[1] Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA

[2] Red Elect Espana, Madrid, Spain

[3] Australian Energy Market Operator, Adelaide, SA, Australia

[4] Fraunhofer IEE, Kassel, Germany

[5] ERCOT, Austin, TX USA

[6] Energy & Meteo Syst, Oldenburg, Germany

[7] Southwest Power Pool, Little Rock, AR USA

[8] WEPROG, Assens, Denmark

[9] CAISO, Folsom, CA USA

[10] R&D Nester, Sacavem, Portugal

[11] UL AWS Truepower, Albany, NY USA

来源：

IEEE POWER & ENERGY MAGAZINE | 2019年 / 17卷 / 06期

关键词：

D O I：

10.1109/MPE.2019.2932639

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：

Much of the electric system is weather dependent; thus, our ability to forecast the weather contributes to its efficient and economical operation. Climatological forecasts of meteorological variables are used for long-term planning, capturing changing frequencies of extreme events, such as cold and hot periods, and identifying suitable locations for deploying new resources. Planning for fuel delivery and maintenance relies on subseasonal to seasonal forecasts. On shorter timescales of days, the weather affects both energy demand and supply. Electrical load depends critically on weather because electricity is used for heating and cooling. As more renewable energy is deployed, it becomes increasingly important to understand how these energy sources vary with atmospheric conditions; thus, predictions are necessary for planning unit commitments. On the scales of minutes to hours, shortterm nowcasts aid in the real-time grid integration of these variable energy resources (VERs). © 2003-2012 IEEE.

引用

页码：46 / 57

页数：12

共 7 条

[1]

Bessa R.J., Et al., Towards improved understanding of the applicability of uncertainty forecasts in the electric power industry, Energies, 10, 9, (2017)

[2]

Bouallegue Z.B., Heppelmann T., Theis S.E., Pinson P., Generation of scenarios from calibrated ensemble forecasts with a dual-ensemble copula-coupling approach, Monthly Weather Rev., 144, pp. 4737-4750, (2016)

[3]

Dobschinski J., Et al., Uncertainty forecasting in a nutshell, IEEE Power Energy Mag., 15, 6, pp. 40-49, (2017)

[4]

Giebel G., Et al., IEA Wind Task 36 Forecasting, (2017)

[5]

Joslyn S.L., Nadav-Greenberg L., Taing M.U., Nichols R.M., The effects of wording on the understanding and use of uncertainty information in a threshold forecasting decision, Appl. Cognit. Psychol., 23, 1, pp. 55-72, (2009)

[6]

Joslyn S.J., LeClerc J.E., Uncertainty forecasts improve weather-related decisions and attenuate the effects of forecast error, J. Exp. Psychol. Appl., 18, 1, pp. 126-140, (2012)

[7]

Wende-Von Berg S., Et al., SysDL2.0: System services from area distribution networks: Methods and applications, (in German), in Proc. 14th Symp. Energy Innovation, (2016)

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