Data-Driven Building Energy Consumption Prediction Model Based on VMD-SA-DBN

Qin, Yongrui; Zhao, Meng; Lin, Qingcheng; Li, Xuefeng; Ji, Jing

doi:10.3390/math10173058

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…The proposed model decomposition and central frequency analysis results, presented in Figure 6, demonstrate effective noise reduction, leading to more stable and smoother IMF values than the original water temperature data. Consequently, these improved IMF components contribute to enhanced model prediction [34]. Additionally, they have found applications in analyzing time-series data, such as stock prices [35], wind speed [29], and power load [36].…”

Section: B Variational Mode Decomposition (Vmd)mentioning

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: B Variational Mode Decomposition (Vmd)mentioning

confidence: 99%

An Ensemble Model for Water Temperature Prediction in Intensive Aquaculture

Wang,

Xu,

Cao

et al. 2023

IEEE Access

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“…Because of its computational efficiency and capacity to characterize an unknown system with a small quantity of data, it is one of the most frequently used prediction models in the literature [22]. The ANN forecasting models have achieved acceptable results in various fields, such as ecology [23], economy [24], industry [25,26], communication [27], and medicine [28].…”

Section: Introductionmentioning

confidence: 99%

Optimal Neural Network Model for Short-Term Prediction of Confirmed Cases in the COVID-19 Pandemic

2022

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COVID-19 is one of the largest issues that humanity still has to cope with and has an impact on the daily lives of billions of people. Researchers from all around the world have made various attempts to establish accurate mathematical models of COVID-19 spread. In many branches of science, it is difficult to make accurate predictions about short time series with extremely irregular behavior. Artificial neural networks (ANNs) have lately been extensively used for such applications. Although ANNs may mimic the nonlinear behavior of short time series, they frequently struggle to handle all turbulences. Alternative methods must be used as a result. In order to reduce errors and boost forecasting confidence, a novel methodology that combines Time Delay Neural Networks is suggested in this work. Six separate datasets are used for its validation showing the number of confirmed daily COVID-19 infections in 2021 for six world countries. It is demonstrated that the method may greatly improve the individual networks’ forecasting accuracy independent of their topologies, which broadens the applicability of the approach. A series of additional predictive experiments involving state-of-the-art Extreme Learning Machine modeling were performed to quantitatively compare the accuracy of the proposed methodology with that of similar methodologies. It is shown that the forecasting accuracy of the system outperforms ELM modeling and is in the range of other state-of-the art solutions.

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