Forecasting day-ahead natural gas demand in Denmark

Karabiber, Orhan Altuğ; Xydis, George

doi:10.1016/j.jngse.2020.103193

Cited by 25 publications

(7 citation statements)

References 24 publications

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“…Other factors that might influence natural gas prices through the influence on consumption are temperature (Karabiber & Xydis, 2020) and storage levels (Chaton et al, 2008). Both are directly related to the seasonal demand pattern.…”

Section: Market Data and Stylized Factsmentioning

confidence: 99%

“…They combined autoregressive (AR) models with convolutional ANNs to reduce overfitting on the one hand but allow nonlinear effects on the other hand. Karabiber and Xydis (2020) also considered ANNs alongside ARIMA models as well as the TBATS model (De Livera et al, 2011). They considered many exogenous regressors such as Fourier terms, temperature, the day type (workdays, weekends, holidays), wind speed, solar radiation, electricity prices, gas prices, and biogas production.…”

Section: Introductionmentioning

confidence: 99%

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Distributional modeling and forecasting of natural gas prices

Berrisch

Ziel

2022

Journal of Forecasting

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We examine the problem of modeling and forecasting European day‐ahead and month‐ahead natural gas prices. For this, we propose two distinct probabilistic models that can be utilized in risk and portfolio management. We use daily pricing data ranging from 2011 to 2020. Extensive descriptive data analysis shows that both time series feature heavy tails and conditional heteroscedasticity and show asymmetric behavior in their differences. We propose state‐space time series models under skewed, heavy‐tailed distributions to capture all stylized facts of the data. They include the impact of autocorrelation, seasonality, risk premia, temperature, storage levels, the price of European Emission Allowances, and related fuel prices of oil, coal, and electricity. We provide rigorous model diagnostics and interpret all model components in detail. Additionally, we conduct a probabilistic forecasting study with significance tests and compare the predictive performance against literature benchmarks. The proposed day‐ahead (month‐ahead) model leads to a 13% (9%) reduction in out‐of‐sample continuous ranked probability score (CRPS) compared with the best performing benchmark model, mainly due to adequate modeling of the volatility and heavy tails.

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Section: Market Data and Stylized Factsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributional modeling and forecasting of natural gas prices

Berrisch

Ziel

2022

Journal of Forecasting

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“…Reliable gasoline demand forecasting is essential for petroleum supply chain planning. Many studies of various scopes have been conducted on energy demand forecasting, including energy as a whole (De Vita et al, 2006;Sözen and Arcaklioglu, 2007;Lee and Tong, 2012;Barak and Sadegh, 2016;Rehman et al, 2017;Ozturk and Ozturk, 2018;Wang et al, 2018;Wang et al, 2019;Li and Zhang, 2019), electricity (González-Romera et al, 2008;Maçaira et al, 2015;Hussain et al, 2016;Ryu et al, 2017;Oliveira and Oliveira, 2018;McNeil et al, 2019;Jiang et al, 2020), petroleum (Houri and Baratimalayeri, 2008;Sa'ad, 2009;Azadeh et al, 2010;Ma et al, 2012;Melikoglu, 2013;Barde, 2014;Chai et al, 2016;Akhmad and Amir, 2018;Sapnken et al, 2018;Oliskevych et al, 2018), natural gas (Szoplik, 2015;Akpinar and Yumusak, 2016;Karabiber and Xydis, 2020), or solar and wind energy (Alsaedi et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Gasoline Demand in Indonesia Using Time Series

Mardiana

Saragih

Huseini

2020

IJEEP

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Fuel is an essential commodity in both the economy and society. Indonesian fuel demand continues to increase annually, whereas fuel production has decreased. Gasoline accounts for more than 50% of fuel consumption for transportation. A reliable gasoline product demand forecast is required to plan the gasoline supply. The objective of this study is to forecast the demand for total gasoline and its three components, which are gasoline 88, gasoline 90, and gasoline 92. This study compared the Holt-Winters additive model and autoregressive integrated moving average for the time-series data for the 2017-2019 period. Because the Holt-Winters additive model generates more accurate results, it was applied to predict the total demand for gasoline during 2020-2022. The results of the combination of the Holt-Winters model and a neural network to forecast gasoline 92 demand had lower errors than the individual Holt-Winters method. The forecast results show that total gasoline demand is forecasted to increase, but the components indicate a different trend. Gasoline 92 and gasoline 88 decreased, but gasoline 90 increased.

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“…In daily studies, NG demand was predicted. A successful daily NG forecasting will lead to determining temperature, pipeline pressures, the viscosity of the gas [8,20], and exogenous variables [21].…”

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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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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Forecasting day-ahead natural gas demand in Denmark

Cited by 25 publications

References 24 publications

Distributional modeling and forecasting of natural gas prices

Distributional modeling and forecasting of natural gas prices

Forecasting Gasoline Demand in Indonesia Using Time Series

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

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