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... gas consumption per-consumer is plotted against daily average temperatures in Figure 1. It can be observed that approximately below 18 • Celsius degrees the relationship between natural gas consumption and temperature is linear and natural gas consumption responds to temperature changes. ...
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Data from literature and a stoichiometric material balance model were employed to estimate associated emissions with flaring of sweet gas in Nigerian oil and gas companies. Emission factors were obtained using AP 42 formula. Results showed that thousands of tonnes, ranging from 6500 to 22,000 tonnes of natural gas were flared from 1997 to 2016. At...
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... They suggested that more accurate seasonal weather forecasts increase the predictability of natural gas consumption. In their study dealing with the Istanbul (Turkey) case, Goncu et al. (2013) concluded that the HDD is the main determinant of the demand for natural gas. Hu et al. (2014) stated that significant temperature shocks affect both the conditional averages and the variability of natural gas prices. ...
... According to our study, changes in climate (HDD) affect residential natural gas prices significantly. This result is also supported by the results obtained in the studies of Gunnarshaug and Ellerman (1998), Timmer and Lamb (2007), Goncu et al. (2013), Hartley and Medlock III (2014), Nick and Thoenes (2014), Harold et al. (2015). In addition, our study concluded that there is no correlation between oil prices and residential natural gas prices. ...
This study examines the effects of climate and oil prices on residential natural gas prices in selected 11 OECD countries by using panel data for the period 1992-2016. After applying the panel unit root tests, the parameters are estimated using Common Correlated Effects Pooled (CCEP) method. Moreover, Emirmahmutoglu-Kose (2011) test is used to test the panel causality between the variables. The results revealed that in the long run, the heating degree days have a statistically significant and negative effect on natural gas prices used in the residential sector in selected OECD countries, while there is an insignificant relationship between oil prices and natural gas prices used in the residential sector in these countries. It is also found to be a causality of heating degree days to natural gas prices.
... Besides, Kim and Lee (2019) examined the impact of extreme cold events on heating energy demand in Korea. Goncu, Karahan, and Kuzubas (2019) proposed a model to forecast residential natural gas consumption and temperature for Istanbul. ...
Recent evidence points to global warming and climate change as the biggest issues of the century; thus, the analysis of the weather-commodity futures prices relationship has crucial importance. This paper considers the relationship between weather anomalies, proxied by the Global Historical Surface Temperature Anomalies (HadCRUT4), and futures prices of agricultural products, energy commodities, industrial, and precious metals. Analyzing the monthly data between December 1982 and November 2020, the outcomes of the novel Granger causality test suggest unidirectional causality from the temperature anomalies to commodity futures prices. The findings imply that global temperature anomalies impact the expectations about the agricultural- and energy-related economic activities, including the use of commercial and organic fertilizers and fossil fuel combustion, respectively.
... It has been applied to multiple case studies as, e.g. (Goncu et al., 2013;Sarak and Satman, 2003) for gas demand estimation or (Berger and Worlitschek, 2018) for heat demand estimation. We add to the literature by proposing a new approach on how to estimate country-wise comfort temperatures and heating seasons by using historical weather and consumption data. ...
Space heating in buildings is becoming a key element of sector-coupled energy system research. Data availability limits efforts to model the buildings sector, because heat consumption is not directly metered in most countries. Space heating is often related to weather through the proxy of heating degree-days using a specific heating threshold temperature, but methods vary between studies. This study estimates country-specific heating threshold temperatures using widely and publicly available consumption and weather data. This allows for national climate and culture-specific human behaviour to be captured in energy systems modelling. National electricity and gas consumption data are related to degree-days through linear models, and Akaike’s Information Criteria is used to define the summer season in each country, when space heating is not required. We find that the heating threshold temperatures computed using daily, weekly and monthly aggregated consumption data are statistically indifferent. In general, threshold temperatures for gas heating centre around 15.0 ± 1.7 ∘C (daily averaged temperature), while heating by electricity averages to 13.4 ± 2.4 ∘C. We find no evidence of space heating during June, July and August, even if heating degree-days are present.
... It has been applied to multiple case studies as, e.g. (Goncu et al., 2013;Sarak and Satman, 2003) for gas demand estimation or (Berger and Worlitschek, 2018) for heat demand estimation. We add to the literature by proposing a new approach on how to estimate country-wise comfort temperatures and heating seasons by using historical weather and consumption data. ...
Space heating in buildings is becoming a key element of sector-coupled energy system research. Data availability limits efforts to model the buildings sector, because heat consumption is not directly metered in most countries. Space heating is often related to weather through the proxy of heating degree-days using a specific heating threshold temperature, but methods vary between studies. This study estimates country-specific heating threshold temperatures using widely and publicly available consumption and weather data. This allows for national climate and culture-specific human behaviour to be captured in energy systems modelling. National electricity and gas consumption data are related to degree-days through linear models, and Akaike's Information Criteria is used to define the summer season in each country, when space heating is not required. We find that the heating threshold temperatures computed using daily, weekly and monthly aggregated consumption data are statistically indifferent. In general, threshold temperatures for gas heating centre around 15.0 +/- 1.7 degree C (daily averaged temperature), while heating by electricity averages to 13.4 +/- 2.4 degree C. We find no evidence of space heating during June, July and August, even if heating degree-days are present.
... the scale and time-scale of the model itself. Some models address global and national levels, such as [3][4][5][6] with timescales of years or decades; many models are devoted to directly predicting natural gas consumption at the hour and city level [4] , while others address the yearly and national scale [5] or daily predictions at the town level [6]. ...
... the scale and time-scale of the model itself. Some models address global and national levels, such as [3][4][5][6] with timescales of years or decades; many models are devoted to directly predicting natural gas consumption at the hour and city level [4] , while others address the yearly and national scale [5] or daily predictions at the town level [6]. ...
The forecasting of energy and natural gas consumption is a topic that spans different temporal and spatial scales and addresses scenarios that vary significantly in consistency and extension. Therefore, although forecasting models share common aims, the specific scale at which each model has been developed strongly impacts its features and the parameters that are to be considered or neglected. There are models designed to handle time scales, such as decades, years, and months, down to daily or hourly models of consumption. Similarly, there are patterns of forecasted consumption that range from continents or groups of nations down to the most limited targets of single individual users, passing through all intermediate levels. This paper describes a model that is able to provide a short-term profile of the hourly heat demand of end-users of a District Heating Network (DHN). The simulator uses the hourly natural gas consumptions of large groups of users and their correlation with the outside air temperature. Next, a procedure based on standards for estimating the energy performance of buildings is defined to scale results down to single-user consumption. The main objective of this work is to provide a simple and fast tool that can be used as a component of wider models of DHNs to improve the control strategies and the management of load variations. The novelty of this work lies in the development of a plain algebraic model for predicting hourly heat demand based only on average daily temperature and historical data of natural gas consumption. Whereas aggregated data of natural gas consumption for groups of end users are measured hourly or even more frequently, the thermal demand is typically evaluated over a significantly longer time horizon, such as a month or more. Therefore, the hourly profile of a single user’s thermal demand is commonly unknown, and only long-term averaged values are available and predictable. With this model, used in conjunction with common weather forecasting services that reliably provide the average temperature of the following day, it is possible to predict the expected hourly heat demand one day in advance and day-by-day.
In this paper, the monthly gas consumption of households in Isfahan province has been modeled using SARIMA models. The best model has been selected bases on AIC criterion and the residuals has been investigated for any notable departure of model assumptions.
This paper presents an alternative to forecasting natural gas consumption in Medellín (Antioquia), the second largest city in Colombia. The data for this study was collected from the SUI (Sistema Único de Información), which is the official database for gas consumption rates and prices in Colombia. Dataset covered the first quarter 2003 up to the second quarter 2014, using ARIMA and Support Vector Machine models predictions, forecast performance was estimated. Results indicate that the ARIMA model presents a better fit than the SVM model in terms of RSME and MAE.
Power-to-gas (PtG) energy storage converts electricity to hydrogen or synthetic natural gas. The gas produced is stored and converted back to electricity at a later time; or it is directly used to supply a gas load and/or sell in the gas market. In the first case, due to double energy conversion in a relatively less efficient process, a large portion of the energy is wasted. The latter case is examined in this paper, where PtG storage is optimally scheduled to convert waste/inexpensive electricity to synthetic natural gas for some useful operations at appropriate time periods. To that end, a new model is proposed for optimal day-ahead scheduling of PtG storage and gas load management in electricity and gas markets to minimize the cost of gas consumption for the gas load. A gas demand (GD) forecasting algorithm is integrated into the scheduling model. Reserve provision is formulated as part of the optimization problem to optimally manage the gas load in case of an outage in the gas grid. The application of the proposed model to a test case is examined, and the results are studied.
