An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting

Electric load forecasting is an important task in the daily operations of a power utility associated with energy transfer scheduling, unit commitment and load dispatch. Inspired by the various non-linearity of electric load data and the strong learning capacity of support vector regression (SVR) for small sample and balanced data, this paper presents an adaptive fuzzy combination model based on the self-organizing map (SOM), the SVR and the fuzzy inference method. The adaptive fuzzy combination model can effectively count for electric load forecasting with good accuracy and interpretability at the same time. The key idea behind the combination is to build a human-understandable knowledge base by constructing a fuzzy membership function for each homogeneous sub-population. The comparison of different mathematical models and the effectiveness of the presented model are shown by the real data of New South Wales electricity market. The obtained results confirm the validity of the developed model.

► We present an adaptive fuzzy combination model for electric load forecasting.
► The proposed model considers both forecast accuracy and result interpretability.
► The proposed model can tolerate more redundant information compared with SVR model.
► It takes significantly less CPU running time to train the proposed model.
► The obtained results confirm the validity of the developed model.