295
Views
1
CrossRef citations to date
0
Altmetric
Research Article

The Detection of Unaccounted for Gas in Residential Natural Gas Customers Using Particle Swarm Optimization-based Neural Networks

, &

References

  • Akpinar, M., M. F. Adak, and N. Yumusak. 2016. Forecasting natural gas consumption with hybrid neural networks—artificial bee colony. In 2016 2nd International Conference on Intelligent Energy and Power Systems (IEPS) (pp. 1–21). IEEE. June
  • Akpinar, M., and N. Yumuşak. 2017. Naive forecasting of household natural gas consumption with sliding window approach. Turkish Journal of Electrical Engineering & Computer Sciences 25 (1):30–45. doi:10.3906/elk-1404-378.
  • Aras, H., and N. Aras. 2004. Forecasting residential natural gas demand. Energy Sources 26 (5):463–72. doi:10.1080/00908310490429740.
  • Bangyal, W. H., J. Ahmed, and H. T. Rauf. 2020. A modified bat algorithm with torus walk for solving global optimisation problems. International Journal of Bio-Inspired Computation 15 (1):1–13. doi:10.1504/IJBIC.2020.105861.
  • Barrera, J., O. Álvarez-Bajo, J. J. Flores, and C. A. CoelloCoello. 2016. Limiting the velocity in the particle swarm optimization algorithm. Computacióny Sistemas 20 (4):635–45. doi:10.13053/cys-20-4-2505.
  • Botev, L., and P. Johnson. 2020. Applications of statistical process control in the management of unaccounted for gas. Journal of Natural Gas Science and Engineering 76:103194. doi:10.1016/j.jngse.2020.103194.
  • Brabec, M., O. Konár, E. Pelikán, and M. Malý. 2008. A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers. International Journal of Forecasting 24 (4):659–78. doi:10.1016/j.ijforecast.2008.08.005.
  • Brkovic, M., L. Radovanovic, E. Desnica, J. Pekez, and Z. Adamovic. 2015. Analysis of loss reduction in natural gas transportation and distribution. Energy Sources, Part B: Economics, Planning, and Policy 10 (2):214–22. doi:10.1080/15567249.2010.551824.
  • Fan, H., I. F. MacGill, and A. B. Sproul. 2015. Statistical analysis of driving factors of residential energy demand in the greater Sydney region, Australia. Energy and Buildings 105:9–25. doi:10.1016/j.enbuild.2015.07.030.
  • Gorucu, F. B. 2004. Evaluation and forecasting of gas consumption by statistical analysis. Energy Sources 26 (3):267–76. doi:10.1080/00908310490256617.
  • Haydell, M., and R. Energy–entex. 2011. Unaccounted-for gas. Proceedings of the American School of Gas Measurement Technology, pp. 148–153. http://asgmt.com/wp-content/uploads/pdf-docs/2001/1/34.pdf.
  • Jafari, F. D., and R. Sadigh. 2019. Modeling and forecasting residential natural gas demand in Iran. Revista Gestão & Tecnologia 19 (4):33–57. doi:10.20397/2177-6652/2019.v19i4.1669.
  • Kim, S., and H. Kim. 2016. A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting 32 (3):669–79. doi:10.1016/j.ijforecast.2015.12.003.
  • Laib, O., M. T. Khadir, and L. Mihaylova. 2019. Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks. Energy 177:530–42. doi:10.1016/j.energy.2019.04.075.
  • Levy, M., D. Raviv, and J. Baker. 2019. Data Center predictions using MATLAB machine learning toolbox. In 2019 IEEE 9th annual computing and communication workshop and conference (CCWC), Las Vegas, NV, USA, IEEE , January, pp. 0458–64.
  • Makridakis, S., E. Spiliotis, and V. Assimakopoulos. 2020. The M4 Competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting 36 (1):54–74. doi:10.1016/j.ijforecast.2019.04.014.
  • Maroufmashat, A., and S. Sattari. 2016. Estimation of natural gas optimum allocation to consuming sectors in year 2025 in Iran. Energy Sources, Part B: Economics, Planning, and Policy 11 (7):587–96. doi:10.1080/15567249.2011.598898.
  • Office of Planning and Macroeconomic of Electricity and Energy (OPMEE). 2018. Energy Balance Sheet of the Ministry of Energy 2018. Tehran, Iran: Ministry of Energy. https://irandataportal.syr.edu/energy-environment.
  • Özmen, A., Y. Yılmaz, and G. W. Weber. 2018. Natural gas consumption forecast with MARS and CMARS models for residential users. Energy Economics 70:357–81. doi:10.1016/j.eneco.2018.01.022.
  • Poli, R., J. Kennedy, and T. Blackwell. 2007. Particle swarm optimization. Swarm Intelligence 1 (1):33–57. doi:10.1007/s11721-007-0002-0.
  • Potočnik, P., J. Šilc, and G. Papa. 2019. A comparison of models for forecasting the residential natural gas demand of an urban area. Energy 167:511–22. doi:10.1016/j.energy.2018.10.175.
  • Qiao, W., K. Huang, M. Azimi, and S. Han. 2019. A novel hybrid prediction model for hourly gas consumption in supply side based on improved whale optimization algorithm and relevance vector machine. IEEE Access 7:88218–30. doi:10.1109/ACCESS.2019.2918156.
  • Qiao, W., H. Moayedi, and L. K. Foong. 2020a. Nature-inspired hybrid techniques of IWO, DA, ES, GA, and ICA, validated through a k-fold validation process predicting monthly natural gas consumption. Energy and Buildings 217:110023. doi:10.1016/j.enbuild.2020.110023.
  • Qiao, W., Z. Yang, Z. Kang, and Z. Pan. 2020b. Short-term natural gas consumption prediction based on Volterra adaptive filter and improved whale optimization algorithm. Engineering Applications of Artificial Intelligence 87:103323. doi:10.1016/j.engappai.2019.103323.
  • Soltanisarvestani, A., and A. A. Safavi. 2021. Modeling unaccounted-for gas among residential natural gas consumers using a comprehensive fuzzy cognitive map. Utilities Policy 72:101251. doi:10.1016/j.jup.2021.101251.
  • Spoladore, A., D. Borelli, F. Devia, F. Mora, and C. Schenone. 2016. Model for forecasting residential heat demand based on natural gas consumption and energy performance indicators. Applied Energy 182:488–99. doi:10.1016/j.apenergy.2016.08.122.
  • Tonković, Z., M. Zekić-Sušac, and M. Somolanji. 2009. Predicting natural gas consumption by neural networks. Tehničkivjesnik 16 (3):51–61.
  • Yilmaz, A. C., and K. Aydin. 2016. Impact Velocity Prediction in a Traffic Accident. In MATEC Web of Conferences, Lucerne, Switzerland, EDP Sciences, Vol. 81, p. 02003.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.