A Combination Interval Prediction Model based on Biased Convex Cost Function and Auto Encoder in Solar Power Prediction

Due to the intermittent and stochastic nature of solar power, solar power interval prediction is of great importance for grid management and power dispatching. A combination interval prediction model based on the lower and upper bound estimation (LUBE) is proposed to quantify the solar power prediction uncertainty efficiently. In the proposed model, the upper and lower bounds are separately predicted by two prediction engines. The Extreme Learning Machine (ELM) is selected as the basic prediction engine. The auto-encoder technique is used to initialize the input weight matrix of ELM for efficient feature learning. A novel biased convex cost function is developed for ELM to train the output weight matrix via the convex optimization technique instead of the conventional heuristic algorithm. The proposed interval prediction model can be formulated as a bi-level optimization problem. In the lower-level problem, the lower and upper ELMs are trained under different candidate hyper-parameters of the biased cost function. The optimal combination of the lower and upper prediction engines is determined by evaluating the interval prediction performance in the upper-level problem. Comprehensive experiments based on public data set are conducted to validate the superiority of the proposed interval prediction model.