Solar radiation forecasting under asymmetric cost functions

Grid operators are tasked to balance the electric grid such that generation equals load. In recent years renewable energy sources have become more popular since they are both clean and sustainable. Because of intermittency of renewable energy sources like wind and solar, the operators are required to predict renewable generation and allocate some operating reserves to mitigate errors. If they overestimate the renewable generation during scheduling, they do not have enough generation available during operation. So overestimation of resources create a more serious problem than underestimation. However, many researchers who study the solar radiation forecasting problem evaluate their methods using symmetric criteria like root mean square error (RMSE) or mean absolute error (MAE). In this paper, we investigate solar radiation forecasting under LinLin and LinEx which are asymmetric cost functions that are better fitted to the grid operator problem. We formulate the problem as an optimization problem and we use linear programming and steepest descent algorithm to find the solution. Simulation results show substantial cost saving using these methods.