Natural Gas Consumption Research Articles

The Importance of Energy Management in Public University Campuses

Energy consumption has gained great attention in recent years, particularly due to the changes in the geopolitics situation and the difficulty in ensuring security in energy sources supply. Several national and international governments set new and more stringent targets to reduce fossil fuel consumption, also proposing specific short-term and mid-term actions. For instance, the Italian Government emanates the Italian National Plan to Reduce Natural Gas consumption in 2022, particularly focused on residential, commercial and public buildings. University of L’Aquila takes inspiration from this planning, to revise its energy management and promote technical actions and a communication campaign aimed at reducing energy consumption. The short-term actions are related to the HVAC plants management of its building: a) shortening the heating days and reducing the daily hours HVAC switching on; b) slight reduction of the indoor comfort temperature; c) reduction of lighting time and intensity. Moreover, virtuous behavior has been encouraged with a communication campaign addressed to employees and students. Results obtained in only one year are very exciting: the natural gas consumption has been reduced more than 20% in academic year 2022/2023, with respect to average values of previous years, with an estimated GHG emissions avoided close to 140 tCO2 for the specific faculty considered. These results are very positive in the GreenMetric perspective, and they boost the importance also of a quite simple but effective energy management strategy and diffusion of awareness on energy and environmental issues.

Forecast of consumption of natural gas in U.S. based on time series analysis and ARIMA model

Natural gas is a widely used fossil fuel, playing a vital role in both industrial and economic sectors. As a widely used fossil fuel, the prediction of natural gas consumption is crucial for industrial and economics and hence many scholars have been researched on the topic. Due to the weather variation and the sensitivity of industrial demand, a precise consumption forecast is difficult. In the article, ARIMA model, a widely used model in time series analysis and forecasts would be considered in the prediction for consumption for future one year. It turns out that, in the next year, the consumption of natural gas in the coming year is stable and will stay at a still level and the overall trend of the series is increasing, with no significant short-term fluctuations. Therefore, the article suggested that long-term investment in industries related to natural gas is recommended. Besides, short-term investment is not suggested compared to the long-term due to the still level of the consumption within one year.

Stochastic Decision-Making Optimization Model for Large Electricity Self-Producers Using Natural Gas in Industrial Processes: An Approach Considering a Regret Cost Function

In the context of high energy costs and energy transition, the optimal use of energy resources for industrial consumption is of fundamental importance. This paper presents a decision-making structure for large consumers with flexibility to manage electricity or natural gas consumption to satisfy the demands of industrial processes. The proposed modelling energy system structure relates monthly medium and hourly short-term decisions to which these agents are subjected, represented by two connected optimization models. In the medium term, the decision occurs under uncertain conditions of energy and natural gas market prices, as well as hydropower generation (self-production). The monthly decision is represented by a risk-constrained optimization model. In the short term, hourly optimization considers the operational flexibility of energy and/or natural gas consumption, subject to the strategy defined in the medium term and mathematically connected by a regret cost function. The model application of a real case of a Brazilian aluminum producer indicates a measured energy cost reduction of USD 3.98 millions over a six-month analysis period.

The European Energy Crisis and the Consequences for the Global Natural Gas Market

The Russian invasion of Ukraine in February 2022 led to severe disruptions in the European gas market with significant repercussions on a global scale. The conflict caused a surge in energy prices, a major reshuffling of global natural gas flows, and a shift in the policy-makers’ agendas toward energy supply security. This paper provides a comprehensive review of the developments in the global gas market in the aftermath of the war in Ukraine, focusing in particular on the European gas market and on global LNG trade flows. We first review the characteristics of the gas market in terms of both pricing benchmarks and contractual terms. Next, we analyze the changes to LNG and natural gas production, consumption, and trade flows throughout the European energy crisis. Finally, we review the main policy response to the energy crisis and present some considerations on the gas market outlook. JEL Classification: L95, P28, Q35

Natural Gas Consumption Forecasting Based on Homoheterogeneous Stacking Ensemble Learning

Natural gas consumption is an important indicator of energy utilization and demand, and its scientific and high-accuracy prediction plays a key role in energy policy formulation. With the development of deep neural networks and ensemble learning, a homoheterogeneous stacking ensemble learning method is proposed for natural gas consumption forecasting. Firstly, to obtain the potential data characteristics, a nonlinear concave and convex transformation-based data dimension enhancement method is designed. Then, with the aid of a stacking ensemble learning framework, the multiscale autoregressive integrated moving average (ARIMA) and high-order fuzzy cognitive map (HFCM) methods are chosen as the base learner models, while the meta learner model is constructed via a well-designed deep neural network with long short-term memory (LSTM) cells. Finally, with the natural gas energy consumption data of national and 30 provinces (where the data of Xizang are unavailable) of China from 2000 to 2019, the numerical results show the proposed algorithm has a better forecasting performance in accuracy, robustness to noise, and sensitivity to data variations than the seven compared traditional and ensemble methods, and the corresponding model applicability rate could achieve more than 90%.

Towards sustainable hydrogen production: Integrating electrified and convective steam reformer with carbon capture and storage

This work reports the design of a process for hydrogen production based on electrified steam methane reforming (e-SMR) coupled with a convective reforming (convective SMR) and carbon capture and storage (CCS) as an alternative to conventional fuel-fired reforming to reduce natural gas (NG) consumption as well as carbon dioxide emissions. The energy required by the reforming reaction is supplied by direct electric heating instead of burning fossil fuel in the radiant section of a furnace, saving 35 % NG and reducing CO2 emission by 29 %. Implementing convective SMR reduces the electric load of the main e-SMR reactor and ensures a slightly higher thermal efficiency (80.2 %) compared to conventional fuel-fired reforming (78.9 %). Further CO2 emissions (85 %) and NG consumption reduction (50 %) are possible by adopting amine-based CO2 capture. If coupled with an energy integration scheme, it is possible to capture 75 % of the CO2 produced, preserving high energy efficiency (79.4 %). This requires only a 14 % increase in capital costs, which is strongly beneficial compared to applying CO2 capture to flue gases of the fuel-fired reforming (69.8 % efficiency and 80 % more capital costs).The process based on e-SMR coupled with convective SMR and CO2 capture ensures a levelized cost of hydrogen (LCOH) of 0.281 € Nm−3 H2, which is much lower than the conventional fuel-fired reforming with CO2 capture applied to flue gases (0.309 € Nm−3 H2). Moreover, it has comparable CO2 emissions (1.59 vs 0.99 kg CO2 emitted kg−1 H2) but produces lower CO2 (6.39 vs 9.88 CO2 produced kg−1 H2) compared to fuel-fired reforming due to using renewable electricity as energy source for the SMR. Compared to conventional fuel-fired reforming, the same process provides similar LCOH (0.283 vs 0.282 € Nm−3 H2) but with drastically lower CO2 emissions (1.59 vs 8.99 kg CO2 emitted kg−1 H2).

ANALYSIS OF SELECTED COUNTRIES ACCORDING TO THEIR ENERGY CONSUMPTION BY CLUSTER ANALYSIS K-MEANS METHOD

In this study, countries (Australia, Austria, Azerbaijan, Belgium, Brazil, Bulgaria, Canada, China, Chile, Colombia, Denmark, Egypt, Finland, France, Germany, Greece, Hungary, India, Indonesia, Iran, Iraq, Italy, Japan, Kazakhstan, Luxembourg, Mexico, New Zealand, the Netherlands, Norway, Pakistan, Poland, Portugal, Russia, Spain, Sweden, Switzerland, Thailand, Turiye, the United Kingdom, the United States, and Uzbekistan) were clustered based on their energy consumption for the years 2000 and 2021. In line with the objectives of the study, data on nuclear energy, coal consumption, oil consumption, natural gas consumption, hydropower consumption, and renewable energy consumption were used to represent energy consumption. The clustering analysis revealed differences between countries in the clusters formed between 2000 and 2021. The transition of countries such as Iran, the Netherlands, Mexico, and Luxembourg from Cluster 1 in 2000 to Cluster 2 in 2021 illustrates the complexity of changes in energy consumption patterns. Factors underlying these changes include changes in energy policies, economic conditions, international relations, and technological advances. Similarly, the transition of countries such as Canada, Germany, Italy, Spain, and the United Kingdom from Cluster 2 in 2000 to Cluster 1 in 2021 can be attributed to various factors such as changes in energy policies, economic growth or stagnation, technological progress, shifts in international trade relations, and environmental considerations.

Home energy management system for residential fuel cell-based combined heat and power system

The goal of this paper is to propose an optimization scheme for enhancing power dispatch and load scheduling for residential fuel cell-based combined heat and power systems (FC-CHPS) using mixed integer linear programming (MILP), considering the lifetime degradation of both the fuel cell system (FCS) and battery energy storage systems (BESS). The scheme is applied in the home energy management system that oversees the electric and thermal power of residential FC-CHPS. First, the nonlinearity in the relationship between the natural gas consumption and output electric power, and between the residual thermal power and output electric power of the fuel cell system (FCS) in the FC-CHPS is approximated using piecewise linearization. Then, the piecewise linearization is integrated with MILP to derive optimal power dispatch and load scheduling solutions, while accounting for the nonlinearity of the FCS. Next, cost functions representing the lifetime degradation of FCS and BESS are formulated. These cost functions result in nonlinear optimization constraints. Therefore, innovative methods are proposed to linearize these constraints, allowing the continued use of MILP as the optimization method and factoring in the lifetime degradation of FCS and BESS. Compared to the mixed-integer non-linear programming (MINLP) method that does not linearize the non-linearity in the FCS or the lifetime degradation of the FCS and BESS, the proposed MILP scheme achieves the identical objective function value. Furthermore, the computation time for the proposed MILP scheme is 99.94 % lower than that required by the MINLP method. The proposed scheme provides accurate results in short computation times.

Analysis of the Rebound Effect and Induced Effect of Non-residential Natural Gas Consumption: Empirical Evidence from China

Following the Paris Agreement, economic development demands energy services while reducing emissions. This study focuses on non-residential natural gas consumption, which plays a crucial role in transforming the energy sector; however, market-based mechanisms are still in the early stages and have not yet been explored. Based on the analysis of data collected from 2000 to 2020 in China, this study constructs a simultaneous equation model to analyze the rebound and induced effects of non-residential natural gas consumption. This paper finds that industrial economic growth has a significant promoting effect on non-residential gas consumption. In addition, implementing and promoting natural gas utilization policies and increases in natural gas prices can significantly reduce gas consumption intensity. This article innovatively calculates the rebound and induced effects of non-residential gas consumption, providing a decision-making reference for accelerating the realization of the dual-carbon goal.

Techno-Economic Optimization of Microgrid Operation with Integration of Renewable Energy, Hydrogen Storage, and Micro Gas Turbine

Microgrids are integral to modern energy systems, yet they face substantial challenges in integrating diverse components, managing complex dynamics, and ensuring stability amid renewable energy variability. This study investigates the integration of wind turbines, an electrolyzer, and a hydrogen-compatible micro gas turbine (MGT), with a focus on enhancing operational efficiency and maintaining dynamic equilibrium within the microgrid. The synergy between the electrolyzer and MGT provides a robust energy storage solution, improving both system stability and performance.Using advanced machine learning and real operational data, this research generates highly accurate, rapid models with greater precision and detail than conventional methods. The intelligent management system developed combines real-time optimization and adaptive fine-tuning, using forecasts of weather, electricity prices, and energy demand to devise optimized operational strategies. The study systematically analyzes various configurations, including grid-connected and island modes, and evaluates the impact of hydrogen storage in each scenario.The results demonstrate that the optimization approach substantially enhances economic returns. However, this can also lead to increased natural gas consumption and emissions, revealing a trade-off between financial gains and environmental sustainability. This highlights the necessity for strategies that reconcile economic incentives with sustainability objectives, offering valuable insights for improved microgrid management.

Disaggregated Impact of Non-Renewable Energy Consumption on the Environmental Sustainability of the United States: A Novel Dynamic ARDL Approach

While there is a vast body of literature on environmental sustainability, the disaggregated impact of major non-renewable energy (NRE) consumption on the environmental sustainability of the United States (U.S.) is understudied, particularly in terms of using a load capacity factor (LCF) perspective. In this study, the above research gap is addressed using a dynamic autoregressive distributed lag (DYNARDL) model to analyze the heterogeneous impact of NRE consumption on the environmental sustainability of the U.S. from 1961 to 2022. Given the U.S.’s heavy reliance on energy consumption from NRE sources, this analysis provides an in-depth examination of the long-term effects of this energy consumption on the environment. Based on the analysis of the DYNARDL model, it is found that an increase of one unit of coal, natural gas, and petroleum energy consumption reduces environmental sustainability by 0.007, 0.006, and 0.008 units in the short-run and 0.006, 0.004, and 0.005 units in the long-run, respectively. However, one unit of nuclear energy consumption increases environmental sustainability by 0.007 units in the long-run. The kernel-based regularized system (KRLS) result reveals that coal and petroleum energy consumption have a significantly negative causal link with environmental sustainability, while nuclear energy consumption demonstrates a significant positive causal relationship. The research suggests the expansion of the use of nuclear energy by gradually reducing the utilization of coal and petroleum-based forms of energy, then natural gas, to improve environmental sustainability in the U.S., while considering the social and economic implications of efforts aimed at shifting away from the use of fossil fuels.

A natural gas consumption forecasting system for continual learning scenarios based on Hoeffding trees with change point detection mechanism

Forecasting natural gas consumption, considering seasonality and trends, is crucial in planning its supply and consumption and optimizing the cost of obtaining it, mainly by industrial entities. However, in times of threats to its supply, it is also a critical element that guarantees the supply of this raw material to meet individual consumers’ needs, ensuring society’s energy security. This article introduces a novel multistep forecasting of natural gas consumption with change point detection integration for model collection selection with continual learning capabilities using data stream processing. The performance of the forecasting models based on the proposed approach is evaluated in a complex real-world use case of natural gas consumption forecasting. Furthermore, the methodology generability was verified in an electricity load forecasting task. We employed Hoeffding tree predictors as forecasting models and the Pruned Exact Linear Time (PELT) algorithm for the change point detection procedure. The change point detection integration enables the selection of a different model collection for successive time frames. Thus, three model collection selection procedures are defined and evaluated for forecasting scenarios with various densities of detected change points. These models were compared with change point agnostic baseline approaches and deep learning models. Our experiments show that the proposed approach provides superior results to deep learning models for both datasets and that fewer change points result in a lower forecasting error regardless of the model collection selection procedure employed.

Analysis of in-bin low-temperature and high-temperature grain drying in Alberta: Specific energy, carbon costs and policy implications

Optimizing grain drying process is pivotal for both economic viability and environmental sustainability. In cold countries, supplemental heating is paramount but instigates the issue of carbon release at the same time. To curve the emission, a carbon levy has been imposed that varies with fuel types. However, there is limited knowledge of the impact of field-adaptable technologies utilizing commercialized fuels on the optimal execution of grain drying. To explore this, a comprehensive three-year on-farm grain drying study was conducted in the province of Alberta, Canada. This comparative analysis centered on three critical aspects, i.e., specific energy consumption, greenhouse gas emissions (GHGs), and operating costs. Furthermore, the research extended its projections into the year 2030, accounting for the impact of gradual carbon levies on natural gas, propane, and diesel fuel systems. Resultsshowed that indirect heaters in in-bin systems were 38 % more energy-efficient than direct heaters due to better airflow and higher initial grain temperatures, while indirect heaters with diesel burners exhibited 27.8 % lower burner turndown ratios and 35.4 % higher energy consumption than natural gas counterparts. In high-temperature drying, natural gas consumption decreased as supply air temperatures rose, with double-flow dryer showing the lowest energy consumption (6.3±1.9 GJ/t) in comparison to mixed-flow and cross-flow dryer due to waste heat recovery. Heaters and Dryers operated at varying load capacities, indicated 126–135 % increase in natural gas cost with carbon levy by 2030. Achieving lower specific energy consumption in grain drying requires precise burner and process control, along with maintenance, offering valuable guidance for producers.

Progressing environmental sustainability in hydroponic greenhouse systems: Embracing circular bioeconomy through compost and biochar pathways

The global population growth and awareness about the important role of vitamins in human health drive demand for fresh fruits and vegetables. In line with this need, greenhouse cultivation addresses year-round availability, but chemicals pose challenges. Hydroponic systems control pests, boost yields, and decrease chemicals while faced with significant waste generation. Waste generated by hydroponic systems holds potential for repurposing into value-added products, which aligns with the principles of the circular bioeconomy. However, before embracing widespread adoption, it is crucial to assess its environmental compatibility. This study employs a life cycle assessment approach to compare hydroponic tomato production in greenhouses under conventional and circular bioeconomy systems. The first scenario (Sc-1) encompasses hydroponic tomato production in a greenhouse system under a conventional system. The second scenario (Sc-2) involves the production of tomatoes, along with the conversion of residue of tomato production into compost. The third scenario (Sc-3) closely resembled Sc-2, while valorizing residue of tomato production into biochar. The outcomes indicate that per kg of tomato production under Sc-1 leads to a damage of 4.32 × 10−6 DALY to human health, 4.33E−06 species.yr to ecosystems, and 1.82 × 10−1 USD2013 to resources. The results of weighting of environmental damages also shows a total environmental damage of 75.36 mPt per kg of tomato production under Sc-1 which is mainly based on the production and consumption of natural gas. The findings demonstrate that Sc-2 and Sc-3 exhibit a diminished potential on damage to human health, ecosystems, and resource compared to Sc-1, respectively. Accordingly, in comparison to Sc-1, Sc-2 leads to an approximately 11% reduction in total weighted environmental impacts, while Sc-3 results in an approximately 4% reduction in total weighted environmental impacts. Based on these outcomes, integrating compost and biochar production into hydroponic tomato systems holds significant potential for environmental benefits. Accordingly, further research and development efforts should focus on optimizing the efficiency and scalability of composting and biochar production technologies to maximize their impact on sustainability in hydroponic tomato production.

Forecast of Commodity Energy Consumption and Structure of Rural Households in Sichuan Province

With the growth in energy consumption, problems such as environmental pollution and resource depletion are becoming more and more prominent, and the sustainable use of energy resources and sustainable economic and social development can be achieved only through scientific forecasting and effective management. Forecasting the changing trend of energy consumption structure can guide the government and society to take corresponding measures for environmental protection and sustainable development, and reduce the negative impact of energy consumption on the environment. Based on the current development situation of commodity energy in rural households in Sichuan Province and related consumption data, this paper uses the grey prediction model and Markov model to predict the consumption and consumption structure of commodity energy in rural households in Sichuan Province. It is found that among the four types of commodity energy consumed by rural households in Sichuan Province, the consumption of electricity shows exponential and growth, and it is expected that the consumption of electricity by rural households in Sichuan Province from 2022 to 2025 will be 3,316,166, 3,582,203, 3,886,622, and 4,216,240 tonnes of standard coal, respectively. Meanwhile, using Markov chain to predict the consumption structure, it is found that under the no-planning constraint scenario, the share of energy consumption of electricity and natural gas is increasing, while the share of energy consumption of coal and liquefied petroleum gas (LPG) will be decreasing, and the shares of energy consumption of coal, electricity, natural gas, and LPG will be 1.97 per cent, 75.82 per cent, 26.36 per cent, and 0.02 per cent, respectively, by 2030.

Short-term natural gas load forecasting based on EL-VMD-Transformer-ResLSTM

Due to changes in urban residents’ consumption habits and lifestyles, accurately predicting natural gas consumption has become increasingly important. To address this issue, this paper proposes a forecasting model that combines Ensemble Learning (EL), Variational Mode Decomposition (VMD), Transformer, and LSTM. First, XGBoost, CatBoost, and LightGBM are used as base learners in the ensemble learning framework, with the predictions generated by the ensemble model integrated into the original dataset. Next, the VMD method is employed to decompose the natural gas load sequence into several intrinsic mode functions (IMFs), effectively extracting the inherent features of the natural gas load sequence. Finally, the data is input into the Transformer-ResLSTM network for prediction. This network replaces the original Transformer decoder structure with an LSTM network and fully connected layers, creating a new decoder structure. Additionally, a residual connection mechanism is introduced in both the encoder of the Transformer network and the new decoder structure. Experimental results show that, compared to traditional models such as ARIMA, Transformer, GRU, and LSTM, the proposed hybrid model significantly improves prediction accuracy, reducing MSE by 92–98% and MAE by 74–83%. In summary, this method demonstrates significant potential and practical value in enhancing the accuracy of natural gas load forecasting.

Forecasting natural gas consumption in Turkey using fractional non-linear grey Bernoulli model optimized by grey wolf optimization (GWO) algorithm

Forecasting natural gas consumption in Turkey using fractional non-linear grey Bernoulli model optimized by grey wolf optimization (GWO) algorithm

Industrial equipment optimization for combustion performance enhancement: a real-world case study

To address the issue of high NOx emission from the combustion chamber, this work optimized the industrial machine structure to enhance the combustion performance. The analysis results indicated that the flue gas recirculation (FGR) could effectively reduce the combustion temperature and the distribution of high-temperature regions in the machine chamber, thereby suppressing NOx formation without affecting the gas velocity inside the chamber. Based on the simulation analysis, the FGR technology was applied to modifying the machine structure and evaluated the modification effect in real-world application. It is found that after adding FGR, the oxygen content at the furnace outlet decreased from 13.8% to 10.5%, the NOx emission from the furnace decreased from 80 mg/m3 to 18 mg/m3, and the natural gas consumption decreased by more than 17%. These results demonstrate a significant impact on energy saving and emission reduction after optimizing the machine structure, which can provide a reference basis for subsequent researchers in this field.

Pricing electricity from blue hydrogen to mitigate the energy rebound effect: A case study in agriculture and livestock

As the world's second largest producer of natural gas, Iran relies heavily on natural gas for 70% of its energy needs. There are, however, challenges associated with energy supply in remote regions attached to the gas pipeline network, particularly during the cold season when fuel oil is required for heating and energy provisioning. To address this issue, this research proposes the use of alternative energy sources, such as hydrogen, for non-residential energy consumption, including agriculture and livestock. A key objective of this study is to develop an optimization model for the generation of electricity from hydrogen resources, as well as formulate a robust strategy to assist electricity producers in maximizing their profits. Using this model, producers are able to set prices in accordance with their risk tolerance and present them to the market, thus mitigating price fluctuations. In the targeted sectors, the results indicate that appropriate pricing models can prevent rebound effects. Specifically, the implementation of this model can reduce natural gas consumption by up to 20% and fuel oil usage by 15% during peak seasons. Additionally, the optimal pricing strategy can lead to a 10% reduction in greenhouse gas emissions. The results of this research provide stable and reliable solutions to the energy supply challenges in these regions, thus making the energy market more resilient and environmentally friendly.

Extraction of arsenic from fine dust of copper smelters by reduction roasting with natural gas

One of the current trends in the complex processing of fine dust from copper smelters is their direct leaching with sulfuric acid. As practical results show, high reliable technological parameters are not achieved due to the high content of arsenic in dust. During sulfuric acid leaching of dust, arsenic is distributed between the lead cake and the solution at a ratio of 40 and 60%, respectively. The redistribution of arsenic between leaching products significantly reduces the technological performance and leads to the accumulation of arsenic in the technological scheme. The paper presents the results of comprehensive studies of the elemental and phase composition of fine dust from one of the copper smelters in Kazakhstan. In the initial dust, along with the main phases presented in the form of lead and zinc sulfate, the following typical components were found: oxides of copper, lead, zinc and copper and zinc ferrites. Arsenic is found in two forms—As(III) and As(V). The laboratory installation and technique for conducting reduction roasting of dust with natural gas are presented. The influence of roasting duration, temperature and natural gas consumption on the extraction of arsenic from dust was studied. It has been established that almost complete, up to 99%, extraction of arsenic from dust is achieved with optimal technological roasting parameters: duration τ = 40 min.; natural gas consumption is 1.5 times higher than the stoichiometrically required amount for the reduction of As2O5, and temperature 500 °C.