A deep asymmetric Laplace neural network for deterministic and probabilistic wind power forecasting. (August 2022)

• Record Type: Journal Article
• Authors: Wang, Yun; Xu, Houhua; Zou, Runmin; Zhang, Lingjun; Zhang, Fan
• Is Part Of: Renewable energy. Volume 196(2022)
• Page Start: 497
• Page End: 517
• Publication Date: 2022-08
• Subjects: Probabilistic wind power forecasting -- Deterministic wind power forecasting -- Multi-scale feature fusion -- Asymmetric Laplace distribution -- Multi-convolution neural network -- Bidirectional long short-term memory; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2022.07.009
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Accurate forecasting of wind power faces two challenges: 1) extracting more effective information on power fluctuations from limited input features, and 2) constructing a suitable loss function for model training. This paper proposes a novel deep asymmetric Laplace neural network named AL-MCNN-BiLSTM for wind power forecasting. The maximal information coefficient, which can describe the linear and nonlinear relationships between targeted and historical wind power data, is employed to determine the optimal inputs. Then, a novel multi-convolutional neural network (MCNN) is designed with a multi-scale information fusion block, which helps make full use of the multi-scale information in different convolutional layers. The MCNN extracts local information from inputs, then bidirectional long-short-term memory (BiLSTM) is employed to extract temporal information. An asymmetric Laplace distribution is assumed to characterize the uncertainty in wind power forecasts, such that an asymmetric Laplace-based loss function can be used in the model. The forecasting results on four datasets demonstrate that AL-MCNN-BiLSTM not only generates more precise deterministic wind power forecasts with a maximum coefficient of determination of 0.9803, but also produces more reliable prediction intervals at 85%, 90%, 95%, and 99% confidence levels with minimum values of pinball loss reaching 6.8948, 5.1895, 3.0189, and 0.7645, respectively.