Multi-objective prediction intervals for wind power forecast based on deep neural networks

Wind power forecast is playing a significant role in the operation and dispatch of modern power systems. Compared with traditional point forecast methods, interval forecast is able to quantify uncertainties effectively. Unfortunately, the stochastic and intermittent nature of wind power has brought significant challenges to get high quality prediction intervals (PIs). In this paper, a novel interval forecast model based on long short-term memory neural networks (LSTM) is proposed to construct PIs with the lower and upper bound estimation method. Besides, an improved PI evaluation criterion is proposed by considering the estimation error of PIs. Moreover, a multi-objective optimization framework is proposed to investigate the relationship between PI estimation error and average width. To tune the parameters of LSTM, the widely used non-dominated fast sort genetic algorithm is further improved by introducing the competitive learning mechanism. The effectiveness of the proposed model and algorithm is demonstrated by a series of experiments based on a real world wind power dataset. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

Wind power forecast plays a critical role in modern power systems, and this paper proposes a novel interval forecast model based on LSTM to construct prediction intervals effectively. An improved PI evaluation criterion and a multi-objective optimization framework are introduced to investigate the relationship between PI estimation error and average width. The proposed model and algorithm's effectiveness is demonstrated through experiments on real world wind power dataset.