Channel-correlation aware photovoltaic power forecasting framework based on multi-perspective modeling DOI
Dezhi Liu, Jiaming Zhu, Xuan Lin

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 128171 - 128171

Опубликована: Май 1, 2025

Язык: Английский

A coupled framework for power load forecasting with Gaussian implicit spatio temporal block and attention mechanisms network DOI
Dezhi Liu, Xuan Lin,

Hanyang Liu

и другие.

Computers & Electrical Engineering, Год журнала: 2025, Номер 123, С. 110263 - 110263

Опубликована: Март 20, 2025

Язык: Английский

Процитировано

1
Comprehensive Scale Fusion Networks with High Spatiotemporal Feature Correlation for Air Quality Prediction DOI Creative Commons

Chen-Yi Wu,

Zhengliang Lai,

Yuanrong Xu

и другие.

Atmosphere, Год журнала: 2025, Номер 16(4), С. 429 - 429

Опубликована: Апрель 8, 2025

The escalation of industrialization has worsened air quality, underscoring the essential need for accurate forecasting to inform policies and protect public health. Current research primarily emphasized individual spatiotemporal features prediction, neglecting interconnections between these features. To address this, we proposed generative Comprehensive Scale Spatiotemporal Fusion Air Quality Predictor (CSST-AQP). novel dual-branch architecture combines multi-scale spatial correlation analysis with adaptive temporal modeling capture complex interactions in pollutant dispersion enhanced pollution forecasting. Initially, a fusion preprocessing module based on localized high-correlation encodes multidimensional quality indicators geospatial data into unified Then, core employs collaborative framework: processing branch extracts at varying granularities, an enhancement concurrently models local periodicities global evolutionary trends. feature engine hierarchically integrates spatiotemporally relevant regional scales while aggregating from related sites. In experimental results across 14 Chinese regions, CSST-AQP achieves state-of-the-art performance compared LSTM-based networks RMSE 6.11–9.13 μg/m3 R2 0.91–0.93, demonstrating highly robust 60 h capabilities diverse pollutants.

Язык: Английский

Процитировано

0
Research on Prediction Method of Coal Spontaneous Combustion Temperature Based on Spatio-Temporal Graph Attention Mechanism with Time-Frequency Domain Lag Feature Fusion DOI
Ningke Xu, Shuang Li

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
An interpretable interval-valued wind power prediction system based on multi-objective feature extraction and base model selection with dynamic ensemble DOI

Jujie Wang,

Yuxuan Lu, Qian Li

и другие.

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 96, С. 101977 - 101977

Опубликована: Май 9, 2025

Язык: Английский

Процитировано

0
Channel-correlation aware photovoltaic power forecasting framework based on multi-perspective modeling DOI
Dezhi Liu, Jiaming Zhu, Xuan Lin

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 128171 - 128171

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0