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Short Term Peak Timing Forecasting /

Shukla, Shreyashi,

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Short Term Peak Timing Forecasting /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Short Term Peak Timing Forecasting // Shreyashi Shukla.
Author:	Shukla, Shreyashi,
Description:	1 electronic resource (143 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
Contained By:	Dissertations Abstracts International85-05B.
Subject:	Industrial engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30694257
ISBN:	9798380856058

Short Term Peak Timing Forecasting /
Shukla, Shreyashi,

Short Term Peak Timing Forecasting /Shreyashi Shukla. - 1 electronic resource (143 pages)

Source: Dissertations Abstracts International, Volume: 85-05, Section: B.

Peak load forecasting is crucial for reliable and effective grid operation. The day-today operation of the power grid requires load scheduling and dispatches of different energy resources including Energy Storage System and Demand Side Management programs. An effective implementation of these peak-shaving strategies relies heavily on when the peak demand occurs. Hence, forecasting the timing of peak load is as important as forecasting its magnitude.A review of relevant literature indicates that there is no inclusive study on the topic of peak timing forecasting. This research aims to bridge the gap between industry requirements and academic research by addressing some key questions. First, the study defines the different forms of peak timing problems that can benefit grid operation. Next, we investigate the problem of how we measure the peak timing forecast errors. The research critically reviews error measures used in the literature for peak timing forecasting. Based on the findings five application-specific error measures are proposed. The research then focuses on one of the manifestations of the peak timing problem, that is, forecasting daily peak hours.We analyzed the accuracy of peak hour forecasts from a state-of-the-art hourly load forecasting model and set it as the benchmark. The model selection process using different peak timing errors and load shape errors is investigated. Furthermore, two novel peak-hour forecasting frameworks are developed and their effectiveness is empirically demonstrated in two case studies. The first case study comes from an anonymous medium-sized utility in the U.S., while the second one is publicly accessible data from ISO New England. The proposed models demonstrate improved forecast results on the benchmark model by 12-16% in the test years of the two case studies. Additionally, when the models are only evaluated on the critical days with very high demands, they outperform the benchmark by 25-53%. Findings from this study emphasize the importance of developing explicit models for peak hour forecasting by analyzing the key determinants that vary with geographical location and regional factors.

English

ISBN: 9798380856058Subjects--Topical Terms:

526216
Industrial engineering.
Subjects--Index Terms:

Electricity demand

Short Term Peak Timing Forecasting /
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245 1 0 $a Short Term Peak Timing Forecasting / $c Shreyashi Shukla.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 1 electronic resource (143 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
500 $a Advisors: Hong, Tao Committee members: Lim, Churlzu; Wang, Pu; Wu, Jay.
502 $b Ph.D. $c The University of North Carolina at Charlotte $d 2023.
520 $a Peak load forecasting is crucial for reliable and effective grid operation. The day-today operation of the power grid requires load scheduling and dispatches of different energy resources including Energy Storage System and Demand Side Management programs. An effective implementation of these peak-shaving strategies relies heavily on when the peak demand occurs. Hence, forecasting the timing of peak load is as important as forecasting its magnitude.A review of relevant literature indicates that there is no inclusive study on the topic of peak timing forecasting. This research aims to bridge the gap between industry requirements and academic research by addressing some key questions. First, the study defines the different forms of peak timing problems that can benefit grid operation. Next, we investigate the problem of how we measure the peak timing forecast errors. The research critically reviews error measures used in the literature for peak timing forecasting. Based on the findings five application-specific error measures are proposed. The research then focuses on one of the manifestations of the peak timing problem, that is, forecasting daily peak hours.We analyzed the accuracy of peak hour forecasts from a state-of-the-art hourly load forecasting model and set it as the benchmark. The model selection process using different peak timing errors and load shape errors is investigated. Furthermore, two novel peak-hour forecasting frameworks are developed and their effectiveness is empirically demonstrated in two case studies. The first case study comes from an anonymous medium-sized utility in the U.S., while the second one is publicly accessible data from ISO New England. The proposed models demonstrate improved forecast results on the benchmark model by 12-16% in the test years of the two case studies. Additionally, when the models are only evaluated on the critical days with very high demands, they outperform the benchmark by 25-53%. Findings from this study emphasize the importance of developing explicit models for peak hour forecasting by analyzing the key determinants that vary with geographical location and regional factors.
546 $a English
590 $a School code: 0694
650 4 $a Industrial engineering. $3 526216
650 4 $a Environmental geology. $3 535218
653 $a Electricity demand
653 $a Forecasting
653 $a Peak timing
653 $a Short term forecasting
653 $a Geographical location
690 $a 0546
690 $a 0407
710 2 $a The University of North Carolina at Charlotte. $b Infrastructure & Environmental Systems. $e degree granting institution. $3 3817101
720 1 $a Hong, Tao $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-05B.
790 $a 0694
791 $a Ph.D.
792 $a 2023
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30694257