Price forecasting is one of the tasks executed to planning and operation of electrical markets. According to the literature Artificial Neural Networks are the most used techniques to forecast as well as associated with other classical methods or other artificial intelligence forming the hybrid methods. This work has two parts: use the IEEE 24 RTS to run a DC OPF and compute the LMP random varying the load to obtain different scenarios and after realizing the price prediction with Neural Networks (GRNN). The DC OPF will be done by MATPOWER and the predictions by Neural Network toolbox of MATLAB.
News published in Agência FAPESP Newsletter about the scholarship: