Forecasting daily natural gas consumption with regression, time series and machine learning based methods

Yücesan, Melih; Pekel, Engin; Çelik, Erkan; Gül, Muhammet; Seri̇n, Faruk

doi:10.1080/15567036.2021.1875082

Cited by 11 publications

(8 citation statements)

References 56 publications

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“…However, our studies are natural gas demand for the long-term data were applied in the tested set. The fourth study was [34]. In this study, the researchers achieved a prediction of best performance with around 0.357% MAPE measure.…”

Section: Application Of Sarima and Sarimax Models On Natural Gas Production And Consumptionmentioning

confidence: 75%

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Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models

et al. 2021

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Research on forecasting the seasonality and growth trend of natural gas (NG) production and consumption will help organize an analysis base for NG inspection and development, social issues, and allow industrials elements to operate effectively and reduce economic issues. In this situation, we handle a comparison structure on the application of different models in monthly NG production and consumption forecasting using the cross-correlation function and then analyze the association between exogenous variables. Moreover, the SARIMA-X model is tested for US monthly NG production and consumption prediction via the proposed method for the first time in the literature review in this study. The performance of that model has been compared with SARIMA (p, d, q) * (P, D, Q)s. The results from RMSE and MAPE indicate that the superiority of the best model. By applying this method, the US monthly NG production and consumption is forecast until 2025. The success of the proposed method allows the use of seasonality patterns. If this seasonal approach continues, the United States’ NG production (16%) and consumption (24%) are expected to increase by 2025. The results of this study provide effective information for decision-makers on NG production and consumption to be credible and to determine energy planning and future sustainable energy policies.

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Section: Application Of Sarima and Sarimax Models On Natural Gas Production And Consumptionmentioning

confidence: 75%

“…Time-series methods can be determined as natural relationships. They are widely used in natural gas consumption forecasts [10,[30][31][32][33][34]. Although machine learning models can be determined, their relationships are usually non-natural.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models

et al. 2021

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“…In the transportation sector, models have been developed using time series for calculating the transportation times of vehicles, especially in metropolitans that have transportation problems due to traffic density, for planning urban transportation vehicles [5] [6]. There are lots of studies on changes and fluctuations on time series data sets, especially in areas such as seasonal electricity consumption [4], [7], natural gas consumption [8], [9], [10] economy [11] and food [12]. Box-Jenkins (ARIMA-Autoregressive Integrated Moving Average) models (AR-Autoregression, MA-Moving average, ARMA-Autoregressive moving average) [13] have been used in many fields such as furniture [14], finance [15], energy [16], food [17] for discrete and linear time series datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

Time Series Cleaning Methods for Hospital Emergency Admissions

ALİŞAN

Olcay

2022

Journal of Soft Computing and Artificial Intelligence

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Due to the nature of hospital emergency services, density cannot be easily estimated. It is one of the important issues that should be planned for emergency service managers to have sufficient resources continuously in services that develop suddenly, and emergency interventions are made for human life. Effective and efficient management and planning of limited resources are important not only for hospital administrators but also for people who will receive service from emergency services. In this situation, estimating the number of people who will request service in the emergency service with the least error is of great importance in terms of resource management and the operations carried out in the emergency services. The density of patients coming to the emergency department may vary according to the season, special dates, and even time zones during the day. The aim of the study is to show that more successful results will be obtained because of processing the time series by considering the country and area-specific features instead of the traditional approach. In this paper, the patient admission dataset of the public hospital emergency service in Turkey was used. Data cleaning and arranging operations were carried out by considering the official and religious special days of Turkey and the time periods during the day. The data set is first handled holistically, and its performances are measured by making predictions with the LSTM (Long Short Term Memory) model. Then, to examine the effect of time zones, performance values were calculated separately by dividing each day into 3 equal time zones. Finally, to investigate the effect of triage areas on the total density, the model performance was measured by dividing the data forming each time zone into 3 different triage areas in 3 equal time periods. Three stages were applied both on the raw data set and on the data created by extracting the official, religious holidays, and weekend data specific to Turkey. According to the MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error) results, more successful results are obtained thanks to the cleaning and editing processes. Thanks to the study, it is thought that the data sets used for demand forecasting studies in the health sector will produce results closer to reality by determining and standardizing the purification criteria in this way.

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“…The values of the output signals depend on both the input signals and the historical values of output signals in a dynamic system [ 125 ]. Thus, NARX can provide more effective results than traditional neural networks [ 126 ]. NARX model for the time sequence

is given in Equation (20) .…”

Section: Comparative Analysismentioning

confidence: 99%

Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain

Ayyıldız

Erdoğan

Gümüş

2021

Computers in Biology and Medicine

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Forecasting daily natural gas consumption with regression, time series and machine learning based methods

Cited by 11 publications

References 56 publications

Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models

Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models

Time Series Cleaning Methods for Hospital Emergency Admissions

Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain

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