NDLI: Hybrid forecasting model of power demand based on three-stage synthesis and stochastically self-adapting mechanism

Content Provider	IEEE Xplore Digital Library
Author	Shuping Dang Jiahong Ju Baker, L. Gholamzadeh, A. Yizhi Li
Copyright Year	2014
Description	Author affiliation: Sch. of Electr. & Electron. Eng., Univ. of Manchester, Manchester, UK (Shuping Dang; Jiahong Ju; Baker, L.; Gholamzadeh, A.; Yizhi Li)
Abstract	The power demand over the electrical power system and smart grid is a random function in the time domain which is affected by a larger number of stochastic factors, for example weather, date and economy as well as a series of unpredictable human factors. Therefore, the most convenient and efficient methodology to forecast the power demand is a stochastic model based on statistics and fuzzy mathematics, because it can merge all complex factors which are difficult or even impossible to be modelled mathematically into an appropriate correction variable. In this paper, we will introduce a hybrid forecasting model of power demand which separates the forecasting process into three stages, i.e. long-term, middle-term and short-term analysis. Most of the long-term factors will be combined in a comprehensive correction factor for the middle-term stage. In the middle-term stage the forecasting mechanism integrates several different forecasting principles and methods to produce a combined forecasting result and dynamically adjusts its forecasting scheme by different weights for different forecasting methods by measuring and comparing the forecasting result and its corresponding practical measurement. By this self-adapting algorithm, the forecasting model is able to forecast the next 24-hour power demand via using the historical data obtained in its database. In the short-term stage, a fine adjustment mechanism will be involved to enhance the reliability and robustness of the holistic forecasting mechanism.
Sponsorship	IEEE Croatia Sect.
Starting Page	467
Ending Page	472
File Size	269263
Page Count	6
File Format	PDF
ISBN	9781479924493
DOI	10.1109/ENERGYCON.2014.6850468
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-13
Publisher Place	Croatia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Power Demand Forecasting Power demand Heuristic algorithms Predictive models Mathematical model Forecasting Equations Self-adapting Meteorology Stochastic Properties
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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