Hybrid Advanced Techniques for Forecasting in Energy Sector

Hybrid Advanced Techniques for Forecasting in Energy Sector

Hybrid Advanced Techniques

Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents a SVR model hybridized with the empirical mode decomposition (EMD) method and auto regression (AR) for electric load forecasting. The electric load data of the New South Wales (Australia) market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously
provide forecasting with good accuracy and interpretability.

Wei-Chiang Hong (Ed.)

https://www.mdpi.com/books/pdfview/book/841

MDPI - Multidisciplinary Digital Publishing Institute

2018

Baihaqi

Creative Commons

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english

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