258
Views
0
CrossRef citations to date
0
Altmetric
Research Article

An electricity big data application to reveal the chronological linkages between industries

ORCID Icon, ORCID Icon, , & ORCID Icon
Received 13 Feb 2023, Published online: 12 Jun 2024

References

  • Ashraf, Z., Javid, A. Y., & Javid, M. (2013). Electricity consumption and economic growth: Evidence from Pakistan. Economics and Business Letters, 2(1), 21–32. https://doi.org/10.17811/ebl.2.1.2013.21-32
  • Aslan, A., Apergis, N., & Yildirim, S. (2014). Causality between energy consumption and GDP in the U.S.: evidence from wavelet analysis. Frontiers in Energy, 8(1), 1–8. https://doi.org/10.1007/s11708-013-0290-6
  • Avelino, A. F. T. (2017). Disaggregating input–output tables in time: The temporal input–output framework. Economic Systems Research, 29(3), 313–334. https://doi.org/10.1080/09535314.2017.1290587
  • Crown, W. H. (2019). Real-world evidence, causal inference, and machine learning. Value in Health, 22(5), 587–592. https://doi.org/10.1016/j.jval.2019.03.001
  • Dong, X. S., Qian, L. J., Huang, L., & IEEE. (2017). A CNN based bagging learning approach to short-term load forecasting in smart grid. 2017 IEEE smartworld, ubiquitous intelligence & computing, advanced & trusted computed, scalable computing & communications, cloud & big data computing, internet of people and smart city innovation (Smartworld/Scalcom/Uic/Atc/Cbdcom/Iop/Sci).
  • Einav, L., & Levin, J. (2014). Economics in the age of big data. Science, 346(6210), 1243089. https://doi.org/10.1126/science.1243089
  • Fezzi, C., & Fanghella, V. (2020). Real-time estimation of the short-run impact of COVID-19 on economic activity using electricity market data. Environmental and Resource Economics, 76(4), 885–900. https://doi.org/10.1007/s10640-020-00467-4
  • Hamermesh, D. S. (2013). Six decades of top economics publishing: Who and How? Journal of Economic Literature, 51(1), 162–172. https://doi.org/10.1257/jel.51.1.162
  • Harding, M. C., & Lamarche, C. (2021). Small steps with Big Data: Using machine learning in energy and environmental economics. Annual Review of Resource Economics, 13(1), 469–488. https://doi.org/10.1146/annurev-resource-100920-034117
  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction.
  • He, K., Mi, Z., Coffman, D. M., & Guan, D. (2022). Using a linear regression approach to sequential interindustry model for time-lagged economic impact analysis. Structural Change and Economic Dynamics, 62, 399–406. https://doi.org/10.1016/j.strueco.2022.03.017
  • Janzen, B., & Radulescu, D. (2020). Electricity use as a real-time indicator of the economic burden of the COVID-19-related lockdown: Evidence from Switzerland. CESifo Economic Studies, 66(4), 303–321. https://doi.org/10.1093/cesifo/ifaa010
  • Kim, Y. S. (2015). Electricity consumption and economic development: Are countries converging to a common trend? Energy Economics, 49, 192–202. https://doi.org/10.1016/j.eneco.2015.02.001
  • Leontief, W. W. (1953). Studies in the structure of the American economy. Oxford University Press.
  • Levine, S. H., & Romanoff, E. (1989). Economic impact dynamics of complex engineering project scheduling. IEEE Transactions on Systems, Man, and Cybernetics, 19(2), 232–240. https://doi.org/10.1109/21.31029
  • López Prol, J., & O, S. (2020). Impact of COVID-19 measures on short-term electricity consumption in the most affected EU countries and USA states. iScience, 23(10), 101639. https://doi.org/10.1016/j.isci.2020.101639
  • Ludwig, N., Feuerriegel, S., & Neumann, D. (2015). Putting Big Data analytics to work: Feature selection for forecasting electricity prices using the LASSO and random forests. Journal of Decision Systems, 24(1), 19–36. https://doi.org/10.1080/12460125.2015.994290
  • Mellander, C., Lobo, J., Stolarick, K., & Matheson, Z. (2015). Night-time light data: A good proxy measure for economic activity? PLoS One, 10(10), e0139779. https://doi.org/10.1371/journal.pone.0139779
  • Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87–106. https://doi.org/10.1257/jep.31.2.87
  • Naimur Rahman, M., Esmailpour, A., & Zhao, J. (2016). Machine learning with Big Data An efficient electricity generation forecasting system. Big Data Research, 5, 9–15. https://doi.org/10.1016/j.bdr.2016.02.002
  • Novan, K., Smith, A., & Zhou, T. (2020). Residential building codes do save energy: Evidence from hourly smart-meter data. The Review of Economics and Statistics, 104(3), 483–500. https://doi.org/10.1162/rest_a_00967
  • Okuyama, Y., Hewings, G. J. D., & Sonis, M. (2004). Measuring economic impacts of disasters: Interregional input-output analysis using sequential interindustry model. In Y. Okuyama & S. E. Chang (Eds.), Modeling spatial and economic impacts of disasters (pp. 77–101). Springer Berlin Heidelberg.
  • Okuyama, Y., Hewings, G. J., & Sonis, M. (2000). Sequential interindustry model (SIM) and impact analysis: application for measuring economic impact of unscheduled events. 47th north American meetings of the regional science association international, Chicago, IL.
  • Perez-Chacon, R., Asencio-Cortes, G., Martinez-Alvarez, F., & Troncoso, A. (2020). Big data time series forecasting based on pattern sequence similarity and its application to the electricity demand. Information Sciences, 540, 160–174. https://doi.org/10.1016/j.ins.2020.06.014
  • Qu, H. N., Ling, P., & Wu, L. B. (2015). . Electricity consumption analysis and applications based on smart grid Big Data. IEEE 12th Int Conf Ubiquitous Intelligence & Comp/IEEE 12th Int Conf Adv & Trusted Comp/Ieee 15th Int Conf Scalable Comp & Commun/IEEE Int Conf Cloud & Big Data Comp/IEEE Int Conf Internet People and Associated Symposia/Workshops, 923–928.
  • Romanoff, E., & Levine, S. H. (1977). Interregional sequential interindustry modeling: A preliminary analysis of regional growth and decline in a two region case. Northeast Regional Science Review, 7, 87–101.
  • Romanoff, E., & Levine, S. H. (1981). Anticipatory and responsive sequential interindustry models. IEEE Transactions on Systems, Man, and Cybernetics, 11(3), 181–186. https://doi.org/10.1109/TSMC.1981.4308650
  • Romanoff, E., & Levine, S. H. (1990). Combined regional impact dynamics of several construction megaprojects. Regional Science Review, 17, 85–93.
  • Šćepanović, S., Mishkovski, I., Hui, P., Nurminen, J. K., & Ylä-Jääski, A. (2015). Mobile phone call data as a regional socio-economic proxy indicator. PLoS One, 10, e0124160.
  • Tuzlukov, V. (2018). Signal processing noise. CRC Press.
  • UN. (2008). International Standard Industrial Classification of all Economic Activities (ISIC - Rev. 4). Retrieved February 19, 2017, from https://unstats.un.org/unsd/cr/registry/isic-4.asp
  • Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3–28. https://doi.org/10.1257/jep.28.2.3
  • Viloria, A., Sierra, D. M., Camargo, J. F., Zea, K. B., Fuentes, J. P., Hernandez-Palma, H., & Kamatkar, S. J. (2020). Demand in the electricity market: Analysis using Big Data. Intelligent Computing, Information and Control Systems, Iciccs 2019, 1039, 316–325. https://doi.org/10.1007/978-3-030-30465-2_36
  • Wang, Z. Y., Ye, X. Y., Lee, J., Chang, X. M., Liu, H. M., & Li, Q. Q. (2018). A spatial econometric modeling of online social interactions using microblogs. Computers, Environment and Urban Systems, 70, 53–58. https://doi.org/10.1016/j.compenvurbsys.2018.02.001
  • Yuan, Y. H., Tsao, S. H., Chyou, J. T., & Tsai, S. B. (2020). An empirical study on effects of electronic word-of-mouth and Internet risk avoidance on purchase intention: From the perspective of big data. Soft Computing, 24(8), 5713–5728. https://doi.org/10.1007/s00500-019-04300-z
  • Zeng, A. R., Liu, S., & Yu, Y. (2019). Comparative study of data driven methods in building electricity use prediction. Energy and Buildings, 194, 289–300. https://doi.org/10.1016/j.enbuild.2019.04.029
  • Zhang, C., Zhou, K., Yang, S., & Shao, Z. (2017). On electricity consumption and economic growth in China. Renewable and Sustainable Energy Reviews, 76, 353–368. https://doi.org/10.1016/j.rser.2017.03.071
  • Zhou, K., Yang, C., & Shen, J. (2017). Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China. Utilities Policy, 44, 73–84. https://doi.org/10.1016/j.jup.2017.01.004