Short-Term Wind Speed Forecasting Based on a Novel Multiresolution Neural Network

IEEE Transactions on Instrumentation and Measurement, vol. 75, pp. 1–11

Abstract

As the penetration of wind power grows at an unprecedented pace, short-term wind speed forecasting has become indispensable. However, in practical power system applications, the temporal resolution of wind speed measurements often exceeds that required for system dispatch. To leverage these high-resolution measurements for generating lower‐resolution wind speed prediction compatible with dispatch requirements, a novel multi-resolution neural network is first proposed. This method consists of three stages: (1) Multi-resolution time series are constructed from the original wind speed series. (2) A low-resolution feature extraction module based on an improved scalar long short-term memory (sLSTM) network is employed to capture the trend patterns in the low-resolution data, while a high-resolution feature extraction module based on an improved modern temporal convolutional network (modern TCN) is designed to extract fluctuation features from the high-resolution data. (3) Both high-resolution and low-resolution features are fused through a sparse cross-attention mechanism. In the test case, three datasets are used for effectiveness verification, while the main test results indicate that the proposed model well improves the short-term wind speed forecasting accuracy.

Authors 6

1. Yilin Zhang

   Chongqing University

   Affiliation as printed

   State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing, China

2. M Zhang

   University of Southern California

   Affiliation as printed

   Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, USA

3. Junjie Tang

   Chongqing University

   Affiliation as printed

   State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing, China

4. Jie Zhang

   Yunnan Power Grid Co., Ltd. (China)

   Affiliation as printed

   System Operation Department, Yunnan Power Grid Company Ltd., Kunming, Yunnan, China

5. Ferdinanda Ponci Aachen Institute for Automation of Complex Power Systems E.ON Energy Research Center

   RWTH Aachen University

   Affiliation as printed

   Institute for Automation of Complex Power Systems, E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany

6. Antonello Monti Aachen Institute for Automation of Complex Power Systems E.ON Energy Research Center

   RWTH Aachen University

   Affiliation as printed

   Institute for Automation of Complex Power Systems, E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany