Temporal convolutional networks for transient simulation of high-speed channels

Goay, Chan Hong; Ahmad, Nur Syazreen; Goh, Patrick

doi:10.1016/j.aej.2023.05.059

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…Stride and the padding also have an impact in the process (16). Stride refers to the number of moves from the feature detector to the input vector, and padding corresponds to the addition of an outer edge of zeros to the input vector [78,79].…”

Section: Temporal Convolutional Networkmentioning

confidence: 99%

Load Forecasting with Machine Learning and Deep Learning Methods

et al. 2023

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Characterizing the electric energy curve can improve the energy efficiency of existing buildings without any structural change and is the basis for controlling and optimizing building performance. Artificial Intelligence (AI) techniques show much potential due to their accuracy and malleability in the field of pattern recognition, and using these models it is possible to adjust the building services in real time. Thus, the objective of this paper is to determine the AI technique that best forecasts electrical loads. The suggested techniques are random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), long short-term memory (LSTM), and temporal convolutional network (Conv-1D). The conducted research applies a methodology that considers the bias and variance of the models, enhancing the robustness of the most suitable AI techniques for modeling and forecasting the electricity consumption in buildings. These techniques are evaluated in a single-family dwelling located in the United States. The performance comparison is obtained by analyzing their bias and variance by using a 10-fold cross-validation technique. By means of the evaluation of the models in different sets, i.e., validation and test sets, their capacity to reproduce the results and the ability to properly forecast on future occasions is also evaluated. The results show that the model with less dispersion, both in the validation set and test set, is LSTM. It presents errors of −0.02% of nMBE and 2.76% of nRMSE in the validation set and −0.54% of nMBE and 4.74% of nRMSE in the test set.

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Section: Temporal Convolutional Networkmentioning

confidence: 99%

Load Forecasting with Machine Learning and Deep Learning Methods

et al. 2023

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“…While GRU and CNN models offer high forecasting accuracy, they suffer from a notable drawback: high memory requirements during training. However, in 2018, Bai et al introduced a solution to this issue with their Temporal Convolutional Network (TCN), which features shared filters across a layer, resulting in reduced memory requirements [18]. As a result, TCN holds promise for accurate water level forecasting.…”

Section: Introductionmentioning

confidence: 99%

Water Level Time Series Forecasting Using TCN Study Case in Surabaya

Saepudin,

Egi Shidqi Rabbani,

Dio Navialdy

et al. 2024

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Climate change is causing water levels to rise, leading to detrimental effects like tidal flooding in coastal areas. Surabaya, the capital of East Java Province in Indonesia, is particularly vulnerable due to its low-lying location. According to the Meteorological, Climatological, and Geophysical Agency (BMKG), tidal flooding occurs annually in Surabaya as a result of rising water levels, highlighting the urgent need for water level forecasting models to mitigate these impacts. In this study, we employ the Temporal Convolutional Network (TCN) machine learning model for water level forecasting using data from a sea level station monitoring facility in Surabaya. We divided the training data into three scenarios: 3, 6, and 8 months to train TCN models for 14-day forecasts. The 8-month training scenario yielded the best results. Subsequently, we used the 8-month training data to forecast 1, 3, 7, and 14 days using TCN, Transformers, and the Recurrent Neural Network (RNN) models. TCN consistently outperformed other models, particularly excelling in 1-day forecasting with coefficient of determination () and RMSE values of 0.9950 and 0.0487, respectively.

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River flood prediction through flow level modeling using multi-attention encoder-decoder-based TCN with filter-wrapper feature selection

Selva Jeba,

Chitra

2024

Earth Sci Inform

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Temporal convolutional networks for transient simulation of high-speed channels

Cited by 7 publications

References 23 publications

Load Forecasting with Machine Learning and Deep Learning Methods

Load Forecasting with Machine Learning and Deep Learning Methods

Water Level Time Series Forecasting Using TCN Study Case in Surabaya

River flood prediction through flow level modeling using multi-attention encoder-decoder-based TCN with filter-wrapper feature selection

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