Forecasting yearly natural gas consumption using Artificial Neural Network for the Algerian market

Laib, Oussama; Khadir, Mohamed Tarek; Chouireb, Lakhdar

doi:10.1109/ceit.2016.7929041

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…[14] used several MLP to predict the gas demand in the Polish city of Szczecin in any hour or day of the year taking into consideration weather and calendar inputs. Laib et al [15] employed multiple MLPs to predict the yearly gas consumption in Algeria, where each MLP was used to predict the consumption in a specific area before summing all the results to get the total consumption. Jetcheva et al in [16] developed several ANNs to forecast the next 24h electricity load and divided the dataset into subsets, where each subset was used to train a different ANN.…”

Section: Related Workmentioning

confidence: 99%

Modeling renewable energy production and CO2 emissions in the region of Adrar in Algeria using LSTM neural networks

Bouziane¹,

Dugdale²,

Khadir³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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This paper addresses the slow-onset crisis of global warming caused by CO2 emissions. Although electrical load is a major influence in a country's growth and development, it is also one of largest sources of greenhouse gases (GHG), CO2 in particular. Therefore, switching to cleaner energy sources is a clear objective and forecasting electricity load and its environmental cost is a necessary task for electrical energy planning and management. This paper addresses short-term load forecasting of renewable energy (RE) production in the region of Adrar in Algeria with Adrar's photovoltaic (PV) farm and Kabertene's wind farm. The forecast is compared to the overall load demand, and the reduced amount of CO2 resulting from using renewable energy instead of fossil fuels is calculated. The forecasting models use Long short-term memory (LSTM) neural networks, which were trained and validated using real data provided by the national state-owned company SONALGAZ. The results show good performance for the forecasting models with PV and wind models achieving a Mean-absolute-error (MAE) of 0.024 and 0.1 respectively. This RE can help to reduce CO2 emissions by up to 25% per hour.

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Section: Related Workmentioning

confidence: 99%

Modeling renewable energy production and CO2 emissions in the region of Adrar in Algeria using LSTM neural networks

Bouziane¹,

Dugdale²,

Khadir³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

Self Cite

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“…Compared with traditional forecasting models, machine learning models have stronger nonlinear fitting ability (Niu and Dai 2017) and higher forecasting accuracy. Laib et al (2016) predicted the annual NGC in Algeria using the artificial neural network (ANN). Similarly, Szoplik (2015) used the ANN to forecast hourly natural gas demand in Szczecin (Poland).…”

Section: Ngc Forecastingmentioning

confidence: 99%

“…Among them, PLS is one of the commonly used regression models, which can eliminate the multicollinearity between influence factors and avoid spurious regression. ANN is one of the most commonly used models in machine learning models, and it is also applied to predict annual natural gas consumption in the literature (Laib et al 2016). The reason for choosing SVM is to show the importance of parameter optimization for forecasting performance of SVM.…”

Section: Benchmark Models and Parameter Settingsmentioning

confidence: 99%

Forecasting annual natural gas consumption via the application of a novel hybrid model

Gao

Shao

2021

Environ Sci Pollut Res

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Accurate prediction of natural gas consumption (NGC) can offer effective information for energy planning and policy-making. In this study, a novel hybrid forecasting model based on support vector machine (SVM) and improved artificial fish swarm algorithm (IAFSA) is proposed to predict annual NGC. An adaptive learning strategy based on sigmoid function is introduced to improve the performance of traditional artificial fish swarm algorithm (AFSA), which provides a dynamic adjustment for parameter moving step step and visual scope visual. IAFSA is used to obtain the optimal parameters of SVM. In addition, the annual NGC data of China is selected as an example to evaluate the prediction performance of the proposed model. Experimental results reveal that the proposed model in this study outperforms the benchmark models such as artificial neural network (ANN) and partial least squares regression (PLS). The mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE) values are as low as 0.512, 1.4958, and 1.0940. Finally, the proposed model is employed to predict NGC in China from 2020 to 2025.

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Predictive Agents for the Forecast of CO2 Emissions Issued from Electrical Energy Production and Gas Consumption

Bouziane

Khadir

2020

Embedded Systems and Artificial Intelligence

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Forecasting yearly natural gas consumption using Artificial Neural Network for the Algerian market

Cited by 4 publications

References 9 publications

Modeling renewable energy production and CO2 emissions in the region of Adrar in Algeria using LSTM neural networks

Modeling renewable energy production and CO2 emissions in the region of Adrar in Algeria using LSTM neural networks

Forecasting annual natural gas consumption via the application of a novel hybrid model

Predictive Agents for the Forecast of CO2 Emissions Issued from Electrical Energy Production and Gas Consumption

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