DocumentCode :
3376639
Title :
Day ahead load forecasting models for holidays in Indian context
Author :
Fernandes, Royden S S ; Bichpuriya, Yogesh K. ; Rao, M.S.S. ; Soman, S.A.
Author_Institution :
Dept. of Electr. Eng., Indian Inst. of Technol. Bombay, Mumbai, India
fYear :
2011
fDate :
22-24 Dec. 2011
Firstpage :
1
Lastpage :
5
Abstract :
Accurate Short Term Load Forecasting (STLF) is critical for efficient functioning of electricity distribution company. High forecast error may result in non-optimal system operations and financial risk in short term power markets. Load profiles on holidays are very different from that on normal days. In India, holidays can be categorized as Sunday, Public holidays (e.g., Independence day, Republic day etc.) and Festival days (e.g., Diwali, Eid, Christmas etc.). Apart from these holidays, there are a few regional holidays e.g., Ganesh Chaturthi and Maharashtra day in the state of Maharashtra. Sunday is a repeated holiday having weekly frequency while other holidays come once in a year. Also, some of these holidays follows lunar calender and some follows Gregorian calender. Each holiday, excluding Sunday and Public holidays, has different characteristics in terms of activities, lighting load and the number of peoples celebrating the holiday. In such a scenario, predicting the accurate load profile for the holidays is a difficult task. This paper proposes two different models for Sunday and other holidays. Sunday model is used for forecasting load profile on Sundays and Holiday model is used for all other public holidays and festival days. The proposed models have been tested on load data of an urban distribution utilities and the results are illustrated.
Keywords :
load forecasting; power distribution; power markets; Indian context; electricity distribution company; festival days; financial risk; ganesh chaturthi; holidays; lighting load; lunar calender; maharashtra; nonoptimal system operation; short term load forecasting model; short term power markets; urban distribution utility; Biological system modeling; Data models; Forecasting; Load forecasting; Load modeling; Predictive models;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Systems (ICPS), 2011 International Conference on
Conference_Location :
Chennai
Print_ISBN :
INAVLID ISBN
Type :
conf
DOI :
10.1109/ICPES.2011.6156652
Filename :
6156652
Link To Document :
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