TCNFormer: Temporal Convolutional Network Former for Short-Term Wind Speed Forecasting
Global environmental challenges and rising energy demands have led to extensive exploration of wind energy technologies. Accurate wind speed forecasting (WSF) is crucial for optimizing wind energy capture and ensuring system stability. However, predicting wind speed remains challenging due to its inherent randomness, fluctuation, and unpredictability. This study proposes the Temporal Convolutional Network Former (TCNFormer) for short-term (12-hour) wind speed forecasting. The TCNFormer integrates the Temporal Convolutional Network (TCN) and transformer encoder to capture the spatio-temporal features of wind speed. The transformer encoder consists of two distinct attention mechanisms: causal temporal multi-head self-attention (CT-MSA) and temporal external attention (TEA). CT-MSA ensures that the output of a step derives only from previous steps, i.e., causality. Locality is also introduced to improve efficiency. TEA explores potential relationships between different sample sequences in wind speed data. This study utilizes wind speed data from the NASA Prediction of Worldwide Energy Resources (NASA POWER) of Patenga Sea Beach, Chittagong, Bangladesh (latitude 22.2352{\deg} N, longitude 91.7914{\deg} E) over a year (six seasons). The findings indicate that the TCNFormer outperforms state-of-the-art models in prediction accuracy. The proposed TCNFormer presents a promising method for spatio-temporal WSF and may achieve desirable performance in real-world applications of wind power systems.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
PTCT: Patches with 3D-Temporal Convolutional Transformer Network for Precipitation Nowcasting
Precipitation nowcasting is to predict the future rainfall intensity over a short period of time, which mainly relies on the prediction of radar echo sequences. Though convolutional neural network (CNN) and recurrent neu…
Inductive BiasAttention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting
Traffic forecasting is one of the most fundamental problems in transportation science and artificial intelligence. The key challenge is to effectively model complex spatial-temporal dependencies and correlations in moder…
EmMixformer: Mix transformer for eye movement recognition
Eye movement (EM) is a new highly secure biometric behavioral modality that has received increasing attention in recent years. Although deep neural networks, such as convolutional neural network (CNN), have recently achi…
Financial Time Series Forecasting using CNN and Transformer
Time series forecasting is important across various domains for decision-making. In particular, financial time series such as stock prices can be hard to predict as it is difficult to model short-term and long-term tempo…
Decision MakingTime SeriesTime Series ForecastingMotion-Guided Global-Local Aggregation Transformer Network for Precipitation Nowcasting
Nowadays deep learning-based weather radar echo extrapolation methods have competently improved nowcasting quality. Current pure convolutional or convolutional recurrent neural network-based extrapolation pipelines inher…
Optical Flow Estimation