Short-term load forecasting (STLF)
We have developed a generic strategy for short-term load forecasting (STLF) based on the support vector regression machines (SVR). Two important improvements to the SVR based load forecasting method are introduced, i.e. procedure for generation of model inputs and subsequent model input selection using feature selection algorithms. One of the objectives of the proposed strategy is to reduce the operator interaction in the model-building procedure. The proposed use of feature selection algorithms for automatic model input selection and the use of the particle swarm global optimization based technique for the optimization of SVR hyper-parameters reduces the operator interaction. To confirm the effectiveness of the proposed modeling strategy, the model has been trained and tested on two publicly available and well-known load forecasting data sets and compared to the state-of-the-art STLF algorithms yielding improved accuracy.
Collaboration between University of Zagreb (FER) and HEP d.d. (public utility company in Croatia).
Energy price forecasting
This part of my research is related to the recent collaboration with Manuele Monti (IKBrokers, Italy). Also I have received NumeriX E-Quant grant (Italy, 2014) for the work in this area.
E. Ceperic, V. Ceperic, and A. Baric. “A strategy for short-term load forecasting by support vector regression machines”. In: IEEE Transactions on Power Systems (2013). Accepted for publication. Corresponding author. Journal impact factor : 2.921.