Forecasting annual natural gas consumption in USA: Application of machine learning techniques- ANN and SVM

Singh, Sanjeet; Bansal, Pooja; Hosen, Mosharrof; Bansal, Sanjeev K.

doi:10.1016/j.resourpol.2022.103159

Cited by 25 publications

(12 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main prediction methods for small samples are grey theory (Zhao et al 2020), time series(Walery 2019), support vector machines (Singh et al 2023), and shallow neural networks (Liang et al 2020). The advantages and disadvantages of each algorithm are summarized in Table 8.…”

Section: Machine Learning Prediction Methodsmentioning

confidence: 99%

Carbon emission prediction of construction industry in Sichuan Province based on the GA-BP model

Peng,

Tan,

2024

Environ Sci Pollut Res

View full text Add to dashboard Cite

The reduction of the carbon emissions of construction industry is urgent. Therefore, it is essential to accurately predict the carbon emissions of the provincial construction industry, which can support differentiation emission reduction policies in China. This paper proposes a carbon emission prediction model that optimizes the back propagation (BP) neural network by genetic algorithm (GA) to predict carbon emission of construction industry, or "GA-BP". To begin with, the operational carbon emissions of construction industry in Sichuan Province from 2000 to 2020 are calculated by the emission factor method. Further, the electricity correction factor is introduced to eliminate the regional difference in electricity carbon emission coefficient. Finally, four factors are selected by the grey correlation analysis method to predict the operational carbon emission of construction industry in Sichuan Province from 2021 to 2025. The results show that the operational carbon emissions of construction industry in Sichuan Province have been trending up in the past two decades, with an average increase rate of 10.51%. The GA-BP model is a high precision prediction model to predict carbon emissions of construction industry. The mean absolute 2 percentage error (MAPE) of the model is only 6.303%, and its coefficient of determination is 0.853. Moreover, the operational carbon emissions of construction industry in Sichuan Province will reach 8,891.97 million tons of CO2 in 2025. The GA-BP model can effectively predict the future operational carbon emissions of construction industry in Sichuan Province, which provides a new idea for the green and sustainable development of construction industry in Sichuan Province.

show abstract

Section: Machine Learning Prediction Methodsmentioning

confidence: 99%

Carbon emission prediction of construction industry in Sichuan Province based on the GA-BP model

Peng,

Tan,

2024

Environ Sci Pollut Res

View full text Add to dashboard Cite

show abstract

“…The experiments utilize the NTHU-DDD dataset, which includes data from 36 individuals displaying various behaviors indicative of drowsiness, such as yawning, slow blinking, dozing off, and the wearing of glasses or sunglasses under varied lighting conditions both during the day and at night. The dataset categorizes instances into drowsy and The research study [13] proposes a method to address the significant issue of drowsiness. In this experiment, an eye image dataset is utilized to detect the driver's eyes within a specific range.…”

Section: Literature Analysismentioning

confidence: 99%

Novel Transfer Learning Approach for Driver Drowsiness Detection Using Eye Movement Behavior

Madni,

Raza,

Sehar

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Driver drowsiness detection is a critical field of research within automotive safety, aimed at identifying signs of fatigue in drivers to prevent accidents. Drowsiness impairs a driver's reaction time, decision-making ability, and overall alertness, significantly increasing the risk of collisions. Nowadays, the challenge is to detect drowsiness using physiological signals, which often require direct contact with the driver's body. This can be uncomfortable and distracting. This study aimed at detecting driver drowsiness through eye movement behavior imagery of the driver. We utilized a standard image dataset based on the eye movement behavior of drivers to conduct this research experiment. We proposed a novel transfer learning-based features generation which combined the strengths of the Visual Geometry Group (VGG-16) and Light Gradient-Boosting Machine (LGBM) methods. The proposed VGLG (VGG16-LGBM) approach first extracts spatial features from input eye image data and then generates salient transfer features using LGBM. Experimental evaluations reveal that the k-neighbors classifier outperformed the state-of-the-art approach with a high-performance accuracy of 99%. The computational complexity analysis shows that the proposed approach detects driver drowsiness in 0.00829 seconds. We have enhanced the performance through hyperparameter tuning and validations using k-fold validation. This research has the potential to revolutionize driver drowsiness detection, aiming to prevent road accidents and save precious lives.

show abstract

“…It can also deal with nonlinear problems and is widely used in various industries. Currently, ANNs have been used in many fields to make predictions, such as predicting the annual consumption of natural gas [16], the wear rates of Al-MnO2 nanocomposites [17], the shear strengths of beams [18], the energy consumption of heating stations [19], the underground temperature [20], and the energy consumption of typical households [21]. Additionally, there are various applications related to forecasting water usage.…”

Section: Introductionmentioning

confidence: 99%

ANN-LSTM-A Water Consumption Prediction Based on Attention Mechanism Enhancement

Zhou,

Meng,

2024

Energies

View full text Add to dashboard Cite

To reduce the energy consumption of domestic hot water (DHW) production, it is necessary to reasonably select a water supply plan through early predictions of DHW consumption to optimize energy consumption. However, the fluctuations and intermittence of DHW consumption bring great challenges to the prediction of water consumption. In this paper, an ANN-LSTM-A water quantity prediction model based on attention mechanism (AM) enhancement is improved. The model includes an input layer, an AM layer, a hidden layer, and an output layer. Based on the combination of artificial neural network (ANN) and long short-term memory (LSTM) models, an AM is incorporated to address the issue of the traditional ANN model having difficulty capturing the long-term dependencies, such as lags and trends in time series, to improve the accuracy of the DHW consumption prediction. Through comparative experiments, it was found that the root mean square error of the ANN-LSTM-A model was 15.4%, 13.2%, and 13.2% lower than those of the ANN, LSTM, and ANN-LSTM models, respectively. The corresponding mean absolute error was 17.9%, 11.5%, and 8% lower than those of the ANN, LSTM, and ANN-LSTM models, respectively. The results showed that the proposed ANN-LSTM-A model yielded better performances in predicting DHW consumption than the ANN, LSTM, and ANN-LSTM models. This work provides an effective reference for the reasonable selection of the water supply plan and optimization of energy consumption.

show abstract

Forecasting annual natural gas consumption in USA: Application of machine learning techniques- ANN and SVM

Cited by 25 publications

References 32 publications

Carbon emission prediction of construction industry in Sichuan Province based on the GA-BP model

Carbon emission prediction of construction industry in Sichuan Province based on the GA-BP model

Novel Transfer Learning Approach for Driver Drowsiness Detection Using Eye Movement Behavior

ANN-LSTM-A Water Consumption Prediction Based on Attention Mechanism Enhancement

Contact Info

Product

Resources

About