Temperature Prediction of Chinese Cities Based on GCN-BiLSTM

Miao, Lizhi; Yu, Dingyu; Pang, Yueyong; Zhai, Yuehao

doi:10.3390/app122211833

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…To overcome these limitations, bidirectional LSTM models have been applied, which can mitigate the gradient disappearance problem in RNNs and consider both past and future data to make predictions. This bidirectional LSTM model has proven successful in various prediction tasks, including traffic [23], wind speed [24], and temperature [25] forecasting.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Model for Water Temperature Prediction in Intensive Aquaculture

Wang,

Xu,

Cao

et al. 2023

IEEE Access

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In intensive aquaculture systems, accurate water temperature prediction is crucial for aquaculture efficiency. Traditional prediction models often have limitations in dealing with strongly coupled, nonlinear, and time-varying water temperature data. A novel hybrid model for temperature prediction is proposed to improve prediction generalization ability and robustness. The model integrates advanced data processing and prediction techniques. Firstly, the VMD method is utilized to achieve effective data decomposition and noise reduction. Secondly, the CNN algorithm is applied to achieve feature extraction of the data. Finally, the bi-directional LSTM and self-concerned combination are used to obtain the final prediction results. The experimental results show that the MAE, RMSE, MSE, MAPE, and R 2 of the VMD-CNN-BILSTM-SA combination prediction model proposed in this paper are 0.016, 0.143, 0.020, 0.035, and 0.978, respectively. Compared with other deep learning models, the BiLSTM model presented in this paper improves the R 2 by 13.2% compared with LSTM and 13.7% over the GRU model. This study can be applied in fishery farming, which can reduce the risk of farming and promote the modernization of fishery.

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Section: Introductionmentioning

confidence: 99%

An Ensemble Model for Water Temperature Prediction in Intensive Aquaculture

Wang,

Xu,

Cao

et al. 2023

IEEE Access

View full text Add to dashboard Cite

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Friction performance prediction of automotive pads under operating conditions using attention-based CNN-BiLSTM deep learning framework

Yin,

Zhang,

Zhang

et al. 2024

J Mech Sci Technol

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Data Enrichment as a Method of Data Preprocessing to Enhance Short-Term Wind Power Forecasting

Zhou

et al. 2023

Energies

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Wind power forecasting involves data preprocessing and modeling. In pursuit of better forecasting performance, most previous studies focused on creating various wind power forecasting models, but few studies have been published with an emphasis on new types of data preprocessing methods. Effective data preprocessing techniques and the fusion with the physical nature of the wind have been called upon as potential future research directions in recent reviews in this area. Data enrichment as a method of data preprocessing has been widely applied to forecasting problems in the consumer data universe but has not seen application in the wind power forecasting area. This study proposes data enrichment as a new addition to the existing library of data preprocessing methods to improve wind power forecasting performance. A methodological framework of data enrichment is developed with four executable steps: add error features of weather prediction sources, add features of weather prediction at neighboring nodes, add time series features of weather prediction sources, and add complementary weather prediction sources. The proposed data enrichment method takes full advantage of multiple commercially available weather prediction sources and the physical continuity nature of wind. It can cooperate with any existing forecasting models that have weather prediction data as inputs. The controlled experiments on three actual individual wind farms have verified the effectiveness of the proposed data enrichment method: The normalized root mean square error (NRMSE) of the day-ahead wind power forecast of XGBoost and LSTM with data enrichment is 11% to 27% lower than that of XGBoost and LSTM without data enrichment. In the future, variations on the data enrichment methods can be further explored as a promising direction of enhancing short-term wind power forecasting performance.

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Temperature Prediction of Chinese Cities Based on GCN-BiLSTM

Cited by 3 publications

References 24 publications

An Ensemble Model for Water Temperature Prediction in Intensive Aquaculture

An Ensemble Model for Water Temperature Prediction in Intensive Aquaculture

Friction performance prediction of automotive pads under operating conditions using attention-based CNN-BiLSTM deep learning framework

Data Enrichment as a Method of Data Preprocessing to Enhance Short-Term Wind Power Forecasting

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