Saturated load forecasting could effectively estimate future direction and final scale of the regional power grid

providing guidance for planning and mid/long-term transactions of the power market. Firstly

a probabilistic forecasting model based on Gaussian process regression(GPR)is adopted for saturated load forecasting

aiming at its characteristic of strong uncertainty and large time span. Secondly

the optimal solution of model hyper-parameters with the objective of minimizing the sum of squares due to errors(SSE)is realized by a modified chaotic particle swarm optimization(MCPSO)presented. In consideration of the randomness of the factors influencing the saturated load

a saturated load forecasting model based on modified chaotic particle swarm optimization-Gaussian process regression is proposed. Thirdly

in multi-scenarios using the above model while taking saturation criterion into account could forecast the saturated load and obtain multi-scenario scale and time-point. Finally

case studies show that this model not only has high precision

but also enhances the elasticity of forecasting results.