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arxiv: 2605.16232 · v1 · pith:JTJB3GT3new · submitted 2026-05-15 · 💻 cs.CL · cs.AI· cs.ET· cs.LG· cs.SY· eess.SY

A Unified Generative-AI Framework for Smart Energy Infrastructure: Intelligent Gas Distribution, Utility Billing, Carbon Analytics, and Quantum-Inspired Optimisation

Pavan Manjunath , Thomas pruefer This is my paper

Pith reviewed 2026-05-20 18:17 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.ETcs.LGcs.SYeess.SY
keywords generative AIsmart energy infrastructuregas distributionutility billingcarbon analyticsquantum-inspired optimisationsmart metering
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The pith

A unified generative-AI framework integrates smart metering, quantum-inspired optimisation, and carbon analytics to reshape energy utility operations for gas distribution and billing.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes a single framework that merges generative artificial intelligence with smart metering and quantum-inspired combinatorial optimisation. This combination is presented as a way for energy utilities to manage physical infrastructure, customer billing, and environmental reporting in one system. A sympathetic reader would care because the approach promises more automated gas distribution, accurate billing, and reliable carbon tracking without separate tools for each task. The work frames this as a new application domain where these technologies converge to improve overall accountability and efficiency.

Core claim

The accelerating convergence of smart metering, generative artificial intelligence, and quantum-inspired combinatorial optimisation is reshaping how energy utilities manage physical infrastructure, customer engagement, and environmental accountability through a unified framework applied to intelligent gas distribution, utility billing, and carbon analytics.

What carries the argument

The unified generative-AI framework that combines generative models with quantum-inspired combinatorial optimisation to address gas distribution, billing, and carbon analytics tasks in one system.

Load-bearing premise

Generative AI and quantum-inspired methods can be practically unified into a single deployable framework for gas distribution and carbon analytics without major integration barriers or the need for domain-specific validation data.

What would settle it

A controlled pilot in an operating gas utility that measures whether the framework delivers measurable gains in distribution efficiency or carbon reporting accuracy without requiring large amounts of new domain-specific data would support or refute the central claim.

Figures

Figures reproduced from arXiv: 2605.16232 by Pavan Manjunath, Thomas pruefer.

Figure 2
Figure 2. Figure 2: Five-phase simulation pipeline of the unified framework. Thick navy arrows trace the main pipeline flow; thin grey arrows indicate convergence of the four data sources into the preprocessing stage. The beige pills inside each Phase 3 card make the cross-component data dependencies explicit (consumes / produces). Every label corresponds to a quantity or component stated in Sections 4.1–4.3. 5. Results and A… view at source ↗
read the original abstract

The accelerating convergence of smart metering, generative artificial intelligence, and quantum-inspired combinatorial optimisation is reshaping how energy utilities manage physical infrastructure, customer engagement, and environmental accountability

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper claims that the accelerating convergence of smart metering, generative artificial intelligence, and quantum-inspired combinatorial optimisation is reshaping energy utilities' management of physical infrastructure, customer engagement, and environmental accountability, and proposes a unified generative-AI framework for intelligent gas distribution, utility billing, carbon analytics, and quantum-inspired optimisation.

Significance. A substantiated unified framework combining generative models with quantum-inspired optimisation for energy systems could offer practical advances in infrastructure management and carbon tracking if accompanied by architectures, interfaces, and validation. The current manuscript, however, consists only of a high-level claim without any supporting derivations, experiments, or component specifications, so its significance cannot be assessed.

major comments (2)
  1. The manuscript provides no generative model architecture, quantum-inspired objective function, data exchange mechanisms between components, or validation protocol, leaving the central claim of a deployable unified framework unsupported.
  2. Abstract: the statement that this convergence 'is reshaping' utilities is presented without any data, case studies, or benchmarks, rendering the claim unevaluable.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We appreciate the referee's detailed feedback on our manuscript. We have carefully considered each comment and provide point-by-point responses below. Where appropriate, we indicate that revisions will be incorporated into the revised manuscript to address the concerns raised.

read point-by-point responses

  1. Referee: The manuscript provides no generative model architecture, quantum-inspired objective function, data exchange mechanisms between components, or validation protocol, leaving the central claim of a deployable unified framework unsupported.

    Authors: We agree that the manuscript, in its current form, presents the framework at a conceptual level without detailed specifications for the generative model architecture, the quantum-inspired objective function, data exchange mechanisms, or a validation protocol. This high-level presentation was intended to introduce the unified approach. To strengthen the paper, we will revise it by adding a dedicated section outlining the proposed generative AI model architecture, including a description of the quantum-inspired combinatorial optimization objective function, interfaces for data exchange between smart metering, billing, and carbon analytics components, and a high-level validation protocol using benchmark datasets. These additions will provide the necessary support for the framework's claims. revision: yes

  2. Referee: Abstract: the statement that this convergence 'is reshaping' utilities is presented without any data, case studies, or benchmarks, rendering the claim unevaluable.

    Authors: The claim regarding the convergence reshaping utilities is drawn from broader industry trends and recent advancements in smart metering and AI adoption. However, we acknowledge that the abstract would benefit from more concrete support. In the revised manuscript, we will modify the abstract to include references to relevant industry reports and studies demonstrating the impact of these technologies, and provide brief examples or case study summaries to substantiate the statement. revision: yes

Circularity Check

0 steps flagged

No significant circularity: high-level descriptive framework with no equations or derivations

full rationale

The manuscript is a high-level conceptual proposal describing the convergence of smart metering, generative AI, and quantum-inspired optimization for energy utilities. No equations, specific architectures, objective functions, integration protocols, or derivation steps are presented in the provided text. Without any load-bearing mathematical claims or predictions that could reduce to fitted inputs or self-citations by construction, the derivation chain is empty and the content remains self-contained as descriptive overview rather than a derived result.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract does not detail any free parameters, axioms, or invented entities; the proposal appears to rest on the unstated assumption that the listed technologies can be combined without additional foundational elements.

pith-pipeline@v0.9.0 · 5565 in / 1104 out tokens · 66023 ms · 2026-05-20T18:17:52.866978+00:00 · methodology

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Works this paper leans on

60 extracted references · 60 canonical work pages

  1. [1]

    World Energy Outlook 2023

    IEA. World Energy Outlook 2023. International Energy Agency, Paris (2023)

    work page 2023

  2. [2]

    Fit for 55 Package: Revised Energy Efficiency Directive (2023/1791/EU)

    European Commission. Fit for 55 Package: Revised Energy Efficiency Directive (2023/1791/EU) . Official Journal of the European Union (2023)

    work page 2023

  3. [3]

    Securities and Exchange Commission

    U.S. Securities and Exchange Commission. The Enhancement and Standardization of Climate -Related Disclosures for Investors: Final Rule. SEC Release No. 33-11275 (2024)

    work page 2024

  4. [4]

    Proceedings of the National Academy of Sciences 109(17), 6435–6440 (2012)

    Alvarez, R.A., Pacala, S.W., Winebrake, J.J., Chameides, W.L., Hamburg, S.P.: Greater focus needed on methane leakage from natural gas infrastructure. Proceedings of the National Academy of Sciences 109(17), 6435–6440 (2012). https://doi.org/10.1073/pnas.1202407109

    work page doi:10.1073/pnas.1202407109 2012

  5. [5]

    Environmental Protection Agency

    U.S. Environmental Protection Agency. Greenhouse Gas Reporting Program —Subpart W: Petroleum and Natural Gas Systems. EPA-430-R-22-003 (2022)

    work page 2022

  6. [6]

    AR6 Climate Change 2021 —The Physical Science Basis, Chapter 7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity

    IPCC. AR6 Climate Change 2021 —The Physical Science Basis, Chapter 7: The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity. Cambridge University Press (2021)

    work page 2021

  7. [7]

    In: Climate Change 2013: The Physical Science Basis

    Myhre, G., Shindell, D., et al.: Anthropogenic and natural radiative forcing. In: Climate Change 2013: The Physical Science Basis. Cambridge University Press (2013)

    work page 2013

  8. [8]

    Nature 627, 328–334 (2024)

    Sherwin, E.D., Rutherford, J.S., Zhang, Z., et al.: US oil and gas system emissions from nearly one million aerial site measurements. Nature 627, 328–334 (2024). https://doi.org/10.1038/s41586-024-07117-5

    work page doi:10.1038/s41586-024-07117-5 2024

  9. [9]

    Science Advances 9(46), eadh2391 (2023)

    Thorpe, A.K., Green, R.O., Thompson, D.R., et al.: Attribution of individual methane and carbon dioxide emission sources using EMIT observations from space. Science Advances 9(46), eadh2391 (2023). https://doi.org/10.1126/sciadv.adh2391

    work page doi:10.1126/sciadv.adh2391 2023

  10. [10]

    Applied Energy 341, 121082 (2023)

    Jiang, Z., Zheng, X., Liu, X.: Carbon -aware compressor scheduling in gas distribution networks using grid carbon-intensity feeds. Applied Energy 341, 121082 (2023). https://doi.org/10.1016/j.apenergy.2023.121082

    work page doi:10.1016/j.apenergy.2023.121082 2023

  11. [11]

    Carbon Intensity API: Methodology and Data Documentation

    National Grid ESO. Carbon Intensity API: Methodology and Data Documentation . National Grid ESO, London (2023)

    work page 2023

  12. [12]

    IEEE Transactions on Smart Grid 10(3), 3125 –3148 (2019)

    Wang, Y., Chen, Q., Hong, T., Kang, C.: Review of smart meter data analytics: Applications, methodologies, and challenges. IEEE Transactions on Smart Grid 10(3), 3125 –3148 (2019). https://doi.org/10.1109/TSG.2018.2818167

    work page doi:10.1109/tsg.2018.2818167 2019

  13. [13]

    IEEE Transactions on Smart Grid 6(2), 786–794 (2015)

    Eibl, G., Engel, D.: A systematic analysis of privacy in smart metering. IEEE Transactions on Smart Grid 6(2), 786–794 (2015). https://doi.org/10.1109/TSG.2014.2356700

    work page doi:10.1109/tsg.2014.2356700 2015

  14. [14]

    Energy Efficiency Directive (EU) 2023/1791

    European Parliament. Energy Efficiency Directive (EU) 2023/1791 . Article 22: Individual metering and billing. Official Journal of the European Union (2023)

    work page 2023

  15. [15]

    Smart Meter National Programme: Technical Specifications and Data Standards

    Ministry of Power, Government of India. Smart Meter National Programme: Technical Specifications and Data Standards. MoP, New Delhi (2022)

    work page 2022

  16. [16]

    Department of Energy

    U.S. Department of Energy. Grid Modernization Initiative: Data Access and Privacy Framework. DOE/GO- 102021-5558 (2021)

    work page 2021

  17. [17]

    In: Advances in Neural Information Processing Systems, vol

    Brown, T.B., Mann, B., Ryder, N., et al.: Language models are few -shot learners. In: Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901. Curran Associates (2020)

    work page 1901

  18. [18]

    In: Advances in Neural Information Processing Systems , vol

    Ouyang, L., Wu, J., Jiang, X., et al.: Training language models to follow instructions with human feedback. In: Advances in Neural Information Processing Systems , vol. 35, pp. 27730–27744. Curran Associates (2022)

    work page 2022

  19. [19]

    In: Advances in Neural Information Processing Systems, vol

    Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6840–6851. Curran Associates (2020)

    work page 2020

  20. [20]

    In: Proceedings of the International Conference on Learning Representations (ICLR) (2021)

    Song, Y., Ermon, S.: Score -based generative modeling through stochastic differential equations. In: Proceedings of the International Conference on Learning Representations (ICLR) (2021)

    work page 2021

  21. [21]

    In: Proceedings of the AAAI Conference on Artificial Intelligence , vol

    Zhou, H., Zhang, S., Peng, J., et al.: Informer: Beyond efficient transformer for long sequence time -series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, pp. 11106–11115 (2021)

    work page 2021

  22. [22]

    In: Proceedings of the International Conference on Learning Representations (ICLR) (2023)

    Nie, Y., Nguyen, N.H., Sinthong, P., Kalagnanam, J.: A time series is worth 64 words: Long-term forecasting with transformers. In: Proceedings of the International Conference on Learning Representations (ICLR) (2023)

    work page 2023

  23. [23]

    International Journal of Forecasting 37(4), 1748 –1764 (2021)

    Lim, B., Arık, S.Ö., Loeff, N., Pfister, T.: Temporal fusion transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting 37(4), 1748 –1764 (2021). https://doi.org/10.1016/j.ijforecast.2021.03.012

    work page doi:10.1016/j.ijforecast.2021.03.012 2021

  24. [24]

    Science Advances 5(4), eaav2372 (2019)

    Goto, H., Tatsumura, K., Dixon, A.R.: Combinatorial optimisation by simulating adiabatic bifurcations in nonlinear Hamiltonian systems. Science Advances 5(4), eaav2372 (2019). https://doi.org/10.1126/sciadv.aav2372

    work page doi:10.1126/sciadv.aav2372 2019

  25. [25]

    Science Advances 7(6), eabe7953 (2021)

    Goto, H., Endo, K., Suzuki, M., et al.: High -performance combinatorial optimisation based on classical mechanics. Science Advances 7(6), eabe7953 (2021). https://doi.org/10.1126/sciadv.abe7953

    work page doi:10.1126/sciadv.abe7953 2021

  26. [26]

    In: Advances in Neural Information Processing Systems, vol

    Goodfellow, I., Pouget -Abadie, J., Mirza, M., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27, pp. 2672–2680. Curran Associates (2014)

    work page 2014

  27. [27]

    In: Proceedings of the International Conference on Learning Representations (ICLR) (2014)

    Kingma, D.P., Welling, M.: Auto -encoding variational Bayes. In: Proceedings of the International Conference on Learning Representations (ICLR) (2014)

    work page 2014

  28. [28]

    IEEE Transactions on Power Systems 37(4), 3174–3184 (2022)

    Liao, Z., Zhao, Z., Luo, Z., et al.: Deep generative models for distribution system state estimation. IEEE Transactions on Power Systems 37(4), 3174–3184 (2022). https://doi.org/10.1109/TPWRS.2021.3128667

    work page doi:10.1109/tpwrs.2021.3128667 2022

  29. [29]

    Energies 13(1), 130 (2020)

    Fekri, M.N., Ghosh, A.M., Grolinger, K.: Generating energy data for machine learning with recurrent generative adversarial networks. Energies 13(1), 130 (2020). https://doi.org/10.3390/en13010130

    work page doi:10.3390/en13010130 2020

  30. [30]

    Energy and Buildings 298, 113561 (2023)

    Yilmaz, S., Büyükşahin, Ü.Ç., Ertekin, Ş.: Diffusion -based synthetic residential electricity consumption generation. Energy and Buildings 298, 113561 (2023). https://doi.org/10.1016/j.enbuild.2023.113561

    work page doi:10.1016/j.enbuild.2023.113561 2023

  31. [31]

    IEEE Access 7, 63868 –63880 (2019)

    Koochali, A., Schichtel, P., Dengel, A., Ahmed, S.: Probabilistic forecasting of sensory data with generative adversarial networks —ForGAN. IEEE Access 7, 63868 –63880 (2019). https://doi.org/10.1109/ACCESS.2019.2915544

    work page doi:10.1109/access.2019.2915544 2019

  32. [32]

    Energy 134, 681–698 (2017)

    Chaczykowski, M., Zarodkiewicz, P.: Simulation of natural gas quality in transmission and distribution systems. Energy 134, 681–698 (2017). https://doi.org/10.1016/j.energy.2017.06.052

    work page doi:10.1016/j.energy.2017.06.052 2017

  33. [33]

    Chemical Engineering Journal 81(1–3), 41–51 (2001)

    Osiadacz, A.J., Chaczykowski, M.: Comparison of isothermal and non-isothermal pipeline gas flow models. Chemical Engineering Journal 81(1–3), 41–51 (2001). https://doi.org/10.1016/S1385-8947(00)00194-7

    work page doi:10.1016/s1385-8947(00)00194-7 2001

  34. [34]

    International Journal of Forecasting 24(4), 659 –678 (2008)

    Brabec, M., Konár, O., Pelikán, E., Malý, M.: A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers. International Journal of Forecasting 24(4), 659 –678 (2008). https://doi.org/10.1016/j.ijforecast.2008.08.005

    work page doi:10.1016/j.ijforecast.2008.08.005 2008

  35. [35]

    Applied Energy 92, 26 –37 (2012)

    Soldo, B.: Forecasting natural gas consumption. Applied Energy 92, 26 –37 (2012). https://doi.org/10.1016/j.apenergy.2011.11.003

    work page doi:10.1016/j.apenergy.2011.11.003 2012

  36. [36]

    Computational Statistics and Data Analysis 55(9), 2579 –2589 (2011)

    Hyndman, R.J., Ahmed, R.A., Athanasopoulos, G., Shang, H.L.: Optimal combination forecasts for hierarchical time series. Computational Statistics and Data Analysis 55(9), 2579 –2589 (2011). https://doi.org/10.1016/j.csda.2011.03.006

    work page doi:10.1016/j.csda.2011.03.006 2011

  37. [37]

    Journal of Renewable and Sustainable Energy 13(4), 043703 (2021)

    Haben, S., Arora, S., Giasemidis, G., Voss, M., Vukadinovic Greetham, D.: Review of low voltage load forecasting with the focus on distributed energy resources. Journal of Renewable and Sustainable Energy 13(4), 043703 (2021). https://doi.org/10.1063/5.0052879

    work page doi:10.1063/5.0052879 2021

  38. [38]

    IEEE Transactions on Smart Grid 10(1), 841 –851 (2019)

    Kong, W., Dong, Z.Y., Jia, Y., Hill, D.J., Xu, Y., Zhang, Y.: Short -term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions on Smart Grid 10(1), 841 –851 (2019). https://doi.org/10.1109/TSG.2017.2753802

    work page doi:10.1109/tsg.2017.2753802 2019

  39. [39]

    Journal of Loss Prevention in the Process Industries 41, 97–106 (2016)

    Datta, S., Sarkar, S.: A review on different pipeline fault detection methods. Journal of Loss Prevention in the Process Industries 41, 97–106 (2016). https://doi.org/10.1016/j.jlp.2016.03.010

    work page doi:10.1016/j.jlp.2016.03.010 2016

  40. [40]

    Optics and Laser Technology 117, 245–260 (2019)

    Ross-Pinnock, D., Pierson, M., Hartog, A.: Distributed acoustic sensing for pipeline monitoring —a review. Optics and Laser Technology 117, 245–260 (2019). https://doi.org/10.1016/j.optlastec.2019.04.027

    work page doi:10.1016/j.optlastec.2019.04.027 2019

  41. [41]

    Journal of Computing in Civil Engineering 34(2), 04020001 (2020)

    Cody, R.A., Tolson, B.A., Orchard, J.: Detecting leaks in water distribution pipes using a deep autoencoder and hydroacoustic spectrograms. Journal of Computing in Civil Engineering 34(2), 04020001 (2020). https://doi.org/10.1061/(ASCE)CP.1943-5487.0000876

    work page doi:10.1061/(asce)cp.1943-5487.0000876 2020

  42. [42]

    IEEE Transactions on Industrial Informatics 18(11), 7891 –7900 (2022)

    Ravula, S., Hu, X., Karam, L.J.: Graph -neural-network-based pipeline leakage localisation using pressure and flow sensors. IEEE Transactions on Industrial Informatics 18(11), 7891 –7900 (2022). https://doi.org/10.1109/TII.2022.3148302

    work page doi:10.1109/tii.2022.3148302 2022

  43. [43]

    IEEE Transactions on Industrial Informatics 20(3), 3210 –3221 (2024)

    Chen, T., Wang, S., Yu, J., Li, R.: Graph-attention autoencoder for multi-modal pipeline anomaly detection. IEEE Transactions on Industrial Informatics 20(3), 3210 –3221 (2024). https://doi.org/10.1109/TII.2023.3301234

    work page doi:10.1109/tii.2023.3301234 2024

  44. [44]

    Quantum annealing in the transverse Ising model

    Kadowaki, T., Nishimori, H.: Quantum annealing in the transverse Ising model. Physical Review E 58(5), 5355–5363 (1998). https://doi.org/10.1103/PhysRevE.58.5355

    work page doi:10.1103/physreve.58.5355 1998

  45. [45]

    Ising formulations of many NP problems

    Lucas, A.: Ising formulations of many NP problems. Frontiers in Physics 2, 5 (2014). https://doi.org/10.3389/fphy.2014.00005

    work page doi:10.3389/fphy.2014.00005 2014

  46. [46]

    Energy 179, 76–89 (2019)

    Ajagekar, A., You, F.: Quantum computing for energy systems optimisation —challenges and opportunities. Energy 179, 76–89 (2019). https://doi.org/10.1016/j.energy.2019.04.186

    work page doi:10.1016/j.energy.2019.04.186 2019

  47. [47]

    -M., Schüler, T.: Quantum -inspired heuristics for pump scheduling in drinking - water networks

    Volk, M., Strothmann, A. -M., Schüler, T.: Quantum -inspired heuristics for pump scheduling in drinking - water networks. Procedia CIRP 105, 1098–1103 (2022). https://doi.org/10.1016/j.procir.2022.02.183

    work page doi:10.1016/j.procir.2022.02.183 2022

  48. [48]

    Azure OpenAI Service to Help Customers Make Sense of Their Energy Bills

    Microsoft. Azure OpenAI Service to Help Customers Make Sense of Their Energy Bills. Microsoft Technical Case Study (2023). https://azure.microsoft.com/en-us/blog/azure-openai-service-energy-billing

    work page 2023

  49. [49]

    Customer Story: Enel Uses Vertex AI to Personalize Energy Communications

    Google Cloud. Customer Story: Enel Uses Vertex AI to Personalize Energy Communications. Google Cloud Case Studies (2024). https://cloud.google.com/customers/enel

    work page 2024

  50. [50]

    In: Proceedings of the ACM International Conference on Future Energy Systems (e -Energy), pp

    Chen, Y., Tan, Y., Chen, X.: LLM -augmented customer -facing energy reporting: An Italian pilot. In: Proceedings of the ACM International Conference on Future Energy Systems (e -Energy), pp. 234–241 (2024). https://doi.org/10.1145/3632775.3661981

    work page doi:10.1145/3632775.3661981 2024

  51. [51]

    Energy Strategy Reviews 26, 100367 (2019)

    Tranberg, B., Corradi, O., Lajoie, B., et al.: Real-time carbon accounting method for the European electricity markets. Energy Strategy Reviews 26, 100367 (2019). https://doi.org/10.1016/j.esr.2019.100367

    work page doi:10.1016/j.esr.2019.100367 2019

  52. [52]

    Energies 13(15), 3939 (2020)

    Bokde, N.D., Tranberg, B., Andresen, G.B.: A graphical approach to carbon -aware computing using electricityMap data. Energies 13(15), 3939 (2020). https://doi.org/10.3390/en13153939

    work page doi:10.3390/en13153939 2020

  53. [53]

    Journal of the American Statistical Association 114(526), 804 –819 (2019)

    Wickramasuriya, S.L., Athanasopoulos, G., Hyndman, R.J.: Optimal forecast reconciliation using unbiased estimating equations. Journal of the American Statistical Association 114(526), 804 –819 (2019). https://doi.org/10.1080/01621459.2018.1448825

    work page doi:10.1080/01621459.2018.1448825 2019

  54. [54]

    Gulf Professional Publishing, Oxford (2018)

    Speight, J.G.: Natural Gas: A Basic Handbook, 2nd edn. Gulf Professional Publishing, Oxford (2018)

    work page 2018

  55. [55]

    Reliability Engineering and System Safety 167, 496–513 (2017)

    Pambour, K.A., Erdener, B.C., Bolado -Lavin, R., Dijkema, G.P.J.: SAInt —a novel qualitative and quantitative risk assessment methodology for analysing the consequences of natural gas pipeline incidents. Reliability Engineering and System Safety 167, 496–513 (2017). https://doi.org/10.1016/j.ress.2017.06.026

    work page doi:10.1016/j.ress.2017.06.026 2017

  56. [56]

    In: Proceedings of the International Gas Union Research Conference (2014)

    Verde, C., Molina, A., Torres, L.: Statistical residual -based leak detection in gas distribution networks. In: Proceedings of the International Gas Union Research Conference (2014)

    work page 2014

  57. [57]

    Water Research 218, 118506 (2022)

    Bohorquez, J., Lambert, M.F., Alexander, B., Simpson, A.R., Clemens, A.: Pipeline burst detection combining physics -based models and machine learning. Water Research 218, 118506 (2022). https://doi.org/10.1016/j.watres.2022.118506

    work page doi:10.1016/j.watres.2022.118506 2022

  58. [58]

    2006 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 2 —Energy

    IPCC. 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 2 —Energy. IGES, Japan (2006)

    work page 2006

  59. [59]

    Energy Policy 38(10), 5977–5987 (2010)

    Hawkes, A.D.: Estimating marginal CO₂ emission rates for national electricity systems. Energy Policy 38(10), 5977–5987 (2010). https://doi.org/10.1016/j.enpol.2010.05.053

    work page doi:10.1016/j.enpol.2010.05.053 2010

  60. [60]

    Advanced Science 8(12), 2100707 (2021)

    Lannelongue, L., Grealey, J., Inouye, M.: Green algorithms —quantifying the carbon footprint of computation. Advanced Science 8(12), 2100707 (2021). https://doi.org/10.1002/advs.202100707

    work page doi:10.1002/advs.202100707 2021