Journal
ELECTRICAL ENGINEERING
Volume 99, Issue 3, Pages 881-898Publisher
SPRINGER
DOI: 10.1007/s00202-016-0424-z
Keywords
Load forecasting; Special days; Mean shift and pattern discovery
Categories
Funding
- ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalization - COMPETE [POCI-01-0145-FEDER-006961]
- FCT (Portuguese Foundation for Science and Technology) [UID/EEA/50014/2013]
Ask authors/readers for more resources
Short-term load forecasting plays an important role to the operation of electric systems, as a key parameter for planning maintenances and to support the decision making process on the purchase and sale of electric power. A particular case in this respect is the consumption forecasting on special days, which can be a complex task as it presents unusual load behavior, when compared to regular working days. Moreover, its reduced number of samples makes it hard to properly train and validate more complex and nonlinear prediction algorithms. This paper tackles this problem by proposing a new approach to improve the accuracy of the predictions amidst existing special days, employing an Information Theoretic Learning Mean Shift algorithm for pattern discovery, classifying and densifying the available scarce consumption data. The paper describes how this methodology was applied to an electrical load forecasting problem in the northern region of Brazil, improving the previously obtained accuracy held by the power company.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available