NDLI: Short-Term Load Forecasting With Seasonal Decomposition Using Evolution for Parameter Tuning

Content Provider	IEEE Xplore Digital Library
Author	Hoverstad, B.A. Tidemann, A. Langseth, H. Ozturk, P.
Copyright Year	2010
Abstract	This paper studies data-driven short-term load forecasting, where historic data are used to predict the expected load for the next 24 h. Our focus is to simplify and automate the estimation and analysis of various forecasting models. We propose a three-stage approach to load forecasting, consisting of preprocessing, forecasting, and postprocessing, where the forecasting stage uses evolution to automatically set the parameters for each model. In our implementation, the preprocessing stage includes removal of daily and weekly seasonality by a nonparametric method. This seasonal pattern is added in the postprocessing stage. The system allows for easy exploration of several forecasting models, without the need to have in-depth knowledge of how to obtain the best performance for each model. We apply the method to several forecasting algorithms and on three datasets: (1) distribution substation; (2) GEFCom 2012; and (3) a transmission level dataset. We find that the forecasting algorithms considered produce significantly more accurate forecasts when combined with our proposed preprocessing stage compared with applying the same algorithms directly on the raw data. We also find that the parameter values chosen by evolution often provide insights into the interplay between the different datasets and forecast models. Software is available online.
Starting Page	1904
Ending Page	1913
Page Count	10
File Size	1291950
File Format	PDF
ISSN	19493053
Volume Number	6
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load modeling Predictive models Forecasting Time series analysis Load forecasting Autoregressive processes Meteorology load forecasting Artificial intelligence genetic algorithms
Content Type	Text
Resource Type	Article
Subject	Computer Science

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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	Dr. B. Sutradhar bsutra@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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