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

• Published in: IEEE transactions on sustainable energy 2021-07, Vol.12 (3), p.1561-1570
• Main Authors: Long, Huan, Zhang, Chen, Geng, Runhao, Wu, Zaijun, Gu, Wei
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Auto encoder; Coders; Computational geometry; convex optimization; Convexity; Cost function; Engines; extreme learning machine; Extreme learning machines; Feature extraction; Heuristic methods; interval prediction; Learning algorithms; Lower bounds; Machine learning; Optimization; Optimization techniques; Prediction models; Predictions; Predictive models; Solar energy; Solar power; solar power prediction; Stochasticity; Uncertainty; Upper bounds; Weight
• ISSN: 1949-3029; 1949-3037
• DOI: 10.1109/TSTE.2021.3054125

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 efficiently quantify the solar power prediction uncertainty. 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 predict the interval boundary. The output weight matrix of ELM can be solved 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. In the upper-level problem, the optimal combination of the lower and upper prediction engines is determined by evaluating the interval prediction performance. Comprehensive experiments based on public data set are conducted to validate the superiority of the proposed interval prediction model.