Semantic Scholar Open Access 2004 730 sitasi

Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001

Bo-Juen Chen Ming-Wei Chang Chih-Jen Lin

Lihat Sumber DOI

Abstrak

Load forecasting is usually made by constructing models on relative information, such as climate and previous load demand data. In 2001, EUNITE network organized a competition aiming at mid-term load forecasting (predicting daily maximum load of the next 31 days). During the competition we proposed a support vector machine (SVM) model, which was the winning entry, to solve the problem. In this paper, we discuss in detail how SVM, a new learning technique, is successfully applied to load forecasting. In addition, motivated by the competition results and the approaches by other participants, more experiments and deeper analyses are conducted and presented here. Some important conclusions from the results are that temperature (or other types of climate information) might not be useful in such a mid-term load forecasting problem and that the introduction of time-series concept may improve the forecasting.

Topik & Kata Kunci

Computer Science

Penulis (3)

Bo-Juen Chen

Ming-Wei Chang

Chih-Jen Lin

Format Sitasi

APA MLA BibTeX

Chen, B., Chang, M., Lin, C. (2004). Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001. https://doi.org/10.1109/TPWRS.2004.835679

Akses Cepat

Lihat di Sumber doi.org/10.1109/TPWRS.2004.835679

Informasi Jurnal

Tahun Terbit: 2004
Bahasa: en
Total Sitasi: 730×
Sumber Database: Semantic Scholar
DOI: 10.1109/TPWRS.2004.835679
Akses: Open Access ✓