Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Qualitative use of forecast variables in hybrid load forecasting techniques

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Shrestha, G. ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore ; Lie, T.T.

Hybrid forecasting is a blend of various forecasting techniques. One such forecasting technique was presented by Rahman and Shrestha (1991) which utilized the attractive features of both the statistical and expert system based methods. The priority vector based load forecasting technique uses pairwise comparisons to extract relationships from pre-sorted historical hourly load and weather records for up to two years. The pre-sorting is done to identify seasonal boundaries and to categorize the day types (weekdays, weekends, holidays, etc.). The technique is adaptive in the sense that it internally generates the coefficients for relative influence of relevant variables (i.e., weather parameters) on the load. As these relationships change over time, such coefficients are automatically updated. This paper extends the technique by investigating qualitative use of continuous variable (e.g. temperature) as a means to overcome some of the difficulties confronted during the implementation when record high or record low values of these variables are encountered. This technique has been applied to forecast the hourly loads for a week, in summer when record high temperatures were observed, using 168-hour lead time. Results obtained by implementing the technique by using temperature as both (i) continuous variable, and (ii) qualitative variable, using the same set of historical data from a utility company are presented. Most, forecast errors are below 5% and many of the large errors are reduced by qualitative treatment of the variable temperature

Published in:

Advances in Power System Control, Operation and Management, 1993. APSCOM-93., 2nd International Conference on

Date of Conference:

7-10 Dec 1993