Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector

Javanmard, Majid Emami; Tang, Yidan; Wang, Z.; Tontiwachwuthikul, P.

doi:10.1016/j.apenergy.2023.120830

Cited by 44 publications

(10 citation statements)

References 64 publications

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“…Figure. 5 Fossil primary power saving between the studied plants A positive value means that CCHP-BESS system requires less fossil primary power to meet user's power demand compared to the CHP-BESS max BESS plant configuration. However, the APC system does not give an energetic improvement before the hour 2920 and after the hour 7300.…”

Section: Resultsmentioning

confidence: 99%

“…These technologies, such as combined, heat and power (CHP) engines, gas turbines, absorption chillers, heat pumps, and renewable energy sources, significantly minimize greenhouse gas emissions and combat climate change [3,4]. The extraction, transportation, and combustion of fossil fuels, the traditional energy sources for power plants, have detrimental environmental impacts, including air pollution and habitat destruction [5]. By adopting energy-efficient measures, power plants can mitigate their reliance on fossil fuels, decrease their environmental footprint, and preserve natural resources [6].…”

Section: Introductionmentioning

confidence: 99%

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Battery–integrated combined cooling, heating and power plant (CCHP) through NH₃ – H₂O absorption system in a hospital facility

Simone,

Alfredo,

Raffaele

et al. 2023

J. Phys.: Conf. Ser.

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Combined heat and power (CHP) systems have been employed in various applications for years. They are gaining increasing attention in the residential and small industrial sectors for their primary energy saving potential and CO 2 emissions reduction. Additionally, in specific applications such as the hospital sector, CHP plants can play a critical role in replacing fossil-fueled electric generators to supply electricity during local grid outages, thus enhancing hospital facilities’ energy efficiency, while also securing reliability and efficiency of operation. Hospitals have unique energy demand profiles, with high and constant demand (particularly heating and cooling demands), making them an ideal use-case for trigeneration systems. Combined, cooling, heating and power plants (CCHP) are capable to provide not only heating and electricity, but also cooling through the efficient exploitation of a single energy source, reducing Hospitals’ reliance on the local grid. In this context, this work aims at evaluating the potential of a battery-integrated CCHP plant through an innovative ammonia-water absorption system, whose energy analysis is based on data acquired from an Italian Hospital facility. The potential of integrating an experimental combined cooling and power production ammonia-water absorption system has been investigated starting from an optimized battery energy storage system (BESS) CHP plant configuration, studied for the same facility. A proper control strategy has been developed to maximize the cooling production when required from the end-user through CHP plant’s exhaust gases waste heat recovery. The energy analysis demonstrated the advantages of the CCHP-BESS plant over the optimal CHP-BESS configuration, with an 11.22% increase in primary energy saving, a 9.85% reduction in CO 2 emissions, and a 5.03% decrease in electric peak power demand.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Battery–integrated combined cooling, heating and power plant (CCHP) through NH₃ – H₂O absorption system in a hospital facility

Simone,

Alfredo,

Raffaele

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

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“…Javanmard et al [13] utilized a hybrid approach integrating a multi-objective mathematical model with machine learning algorithms to predict energy demand and carbon emissions in the transport sector of Canada. The authors predicted that energy demand and emissions would rise by 34.72% and 50.02%, respectively, from 2019 to 2048 [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating the Energy Demand and Carbon Emission Reduction Potential of Singapore’s Future Road Transport Sector

Devihosur,

Chidire,

Massier

et al. 2024

Sustainability

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About 20% of the world’s CO2 emissions originate from transport. Many countries are committed to decarbonizing their transport sector. Singapore pledged to electrify a whole host of its land transportation fleet, which includes private cars, public buses, ride-hail vehicles, and motorcycles. This paper proposes a simple empirical framework to estimate the future energy demand after 100% electrification has been realized for nine selected road transport vehicle sub-classes and to calculate the carbon emission reduction potential based on various scenarios. The present energy demand for each vehicle sub-class is first calculated based on parameters like petrol and diesel consumption, heat value and density of petrol and diesel, population of vehicle type, and average mileage per vehicle sub-class. Several scenarios are presented, and an analysis is carried out to derive a range of emission factors which are used to estimate the carbon emission reduction potential. Relative to the present day, the future energy demand estimates reveal an overall reduction of 73.60%. Full electrification and a “clean” power generation mix could lead to an emission reduction as high as 93.64% across all vehicles sub-classes, with private cars having the highest reduction potential.

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“…Its versatility allows for application across various topics, including the intricate and volatile financial market (Gajamannage et al, 2023 ), and specific cases such as asset pricing in the Chinese stock market (Pan et al, 2023 ), due to its ability to mitigate error propagation during iterations. Neural network also finds usefulness in predicting energy demand and CO 2 emissions (Javanmard et al, 2023 ). In this study, a fusion of a multiobjective mathematical model with data-driven machine learning algorithms enhances the accuracy of energy demand and CO 2 emissions forecasts in the transportation sector.…”

Section: Introductionmentioning

confidence: 99%

Hybridization of long short-term memory neural network in fractional time series modeling of inflation

Arif,

Herlinawati,

Devianto

et al. 2024

Front. Big Data

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Inflation is capable of significantly impacting monetary policy, thereby emphasizing the need for accurate forecasts to guide decisions aimed at stabilizing inflation rates. Given the significant relationship between inflation and monetary, it becomes feasible to detect long-memory patterns within the data. To capture these long-memory patterns, Autoregressive Fractionally Moving Average (ARFIMA) was developed as a valuable tool in data mining. Due to the challenges posed in residual assumptions, time series model has to be developed to address heteroscedasticity. Consequently, the implementation of a suitable model was imperative to rectify this effect within the residual ARFIMA. In this context, a novel hybrid model was proposed, with Generalized Autoregressive Conditional Heteroscedasticity (GARCH) being replaced by Long Short-Term Memory (LSTM) neural network. The network was used as iterative model to address this issue and achieve optimal parameters. Through a sensitivity analysis using mean absolute percentage error (MAPE), mean squared error (MSE), and mean absolute error (MAE), the performance of ARFIMA, ARFIMA-GARCH, and ARFIMA-LSTM models was assessed. The results showed that ARFIMA-LSTM excelled in simulating the inflation rate. This provided further evidence that inflation data showed characteristics of long memory, and the accuracy of the model was improved by integrating LSTM neural network.

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Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector

Cited by 44 publications

References 64 publications

Battery–integrated combined cooling, heating and power plant (CCHP) through NH₃ – H₂O absorption system in a hospital facility

Battery–integrated combined cooling, heating and power plant (CCHP) through NH₃ – H₂O absorption system in a hospital facility

Estimating the Energy Demand and Carbon Emission Reduction Potential of Singapore’s Future Road Transport Sector

Hybridization of long short-term memory neural network in fractional time series modeling of inflation

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Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector

Cited by 44 publications

References 64 publications

Battery–integrated combined cooling, heating and power plant (CCHP) through NH3 – H2O absorption system in a hospital facility

Battery–integrated combined cooling, heating and power plant (CCHP) through NH3 – H2O absorption system in a hospital facility

Estimating the Energy Demand and Carbon Emission Reduction Potential of Singapore’s Future Road Transport Sector

Hybridization of long short-term memory neural network in fractional time series modeling of inflation

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Product

Resources

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Battery–integrated combined cooling, heating and power plant (CCHP) through NH₃ – H₂O absorption system in a hospital facility

Battery–integrated combined cooling, heating and power plant (CCHP) through NH₃ – H₂O absorption system in a hospital facility