A pareto optimization approach of a Gaussian process ensemble for short-term load forecasting

Accurate prediction of load demand remains a challenge for efficient power distribution and becomes critical in the context of smart grid management when the presence of stochastic sources adds to the stochasticity of demand. Short-term load forecasting involving demand prediction in the range of hours or days is of special interest to generators and power customers. A number of methods has been developed for fast and accurate electric power forecasting. Among others, Gaussian process (GP) regression has been used for prediction in the nonlinear problems with promising results. On that direction, an ensemble of Gaussian process regressors modeled as kernel machines is proposed for load forecasting. The use of different kernels accommodates the construction of a group composed of different predictors and its evolution using genetic algorithms. The proposed approach takes the form of a multiobjective problem in which the objectives consist of a set of criteria. In order to optimize all the criteria it needs to use Pareto optimality to identify an accepted solution. The results obtained show that the ensemble of GP predictors outperforms each individual forecaster.