Legendre Memory Unit with A Multi-Slice Compensation Model for Short-Term Wind Speed Forecasting Based on Wind Farm Cluster Data

2026-02-04

AI Summary

This study addresses the challenges of low accuracy and insufficient robustness in short-term wind speed forecasting for wind farm clusters by proposing a novel WMF-CPK-MSLMU model. The approach first applies weighted mean filtering (WMF) to preprocess individual farm data and, for the first time, integrates Legendre memory units (LMUs) into wind farm cluster prediction, enhanced with a multi-slice architecture to effectively fuse spatiotemporal information. A compensation parameter (CPK), derived from Kendall’s rank correlation coefficient, is introduced to adaptively activate hidden nodes and impute missing data, replacing conventional random initialization to strengthen model expressiveness. Experimental results demonstrate that the proposed model significantly outperforms existing methods across multiple wind farm clusters, achieving high prediction accuracy, rapid response, and strong robustness.

Technology Category

• Machine Learning: Ensemble Methods
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

With more wind farms clustered for integration, the short-term wind speed prediction of such wind farm clusters is critical for normal operation of power systems. This paper focuses on achieving accurate, fast, and robust wind speed prediction by full use of cluster data with spatial-temporal correlation. First, weighted mean filtering (WMF) is applied to denoise wind speed data at the single-farm level. The Legendre memory unit (LMU) is then innovatively applied for the wind speed prediction, in combination with the Compensating Parameter based on Kendall rank correlation coefficient (CPK) of wind farm cluster data, to construct the multi-slice LMU (MSLMU). Finally, an innovative ensemble model WMF-CPK-MSLMU is proposed herein, with three key blocks: data pre-processing, forecasting, and multi-slice compensation. Advantages include: 1) LMU jointly models linear and nonlinear dependencies among farms to capture spatial-temporal correlations through backpropagation; 2) MSLMU enhances forecasting by using CPK-derived weights instead of random initialization, allowing spatial correlations to fully activate hidden nodes across clustered wind farms.; 3) CPK adaptively weights the compensation model in MSLMU and complements missing data spatially, to facilitate the whole model highly accurate and robust. Test results on different wind farm clusters indicate the effectiveness and superiority of proposed ensemble model WMF-CPK-MSLMU in the short-term prediction of wind farm clusters compared to the existing models.

Problem

• short-term wind speed forecasting
• wind farm cluster
• spatial-temporal correlation
• data compensation
• robust prediction

Innovation

• Legendre Memory Unit
• Multi-Slice Compensation
• Kendall Rank Correlation
• Wind Farm Cluster
• Short-Term Wind Speed Forecasting