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Machine Learning and GIS Approach for Electrical Load Assessment to Increase Distribution Networks Resilience

Author

Listed:
  • Alessandro Bosisio

    (Department of Energy, Politecnico di Milano, 20156 Milano, Italy)

  • Matteo Moncecchi

    (Department of Energy, Politecnico di Milano, 20156 Milano, Italy)

  • Andrea Morotti

    (Planning Department, Unareti S.p.A., 20138 Milano, Italy)

  • Marco Merlo

    (Department of Energy, Politecnico di Milano, 20156 Milano, Italy)

Abstract

Currently, distribution system operators (DSOs) are asked to operate distribution grids, managing the rise of the distributed generators (DGs), the rise of the load correlated to heat pump and e-mobility, etc. Nevertheless, they are asked to minimize investments in new sensors and telecommunication links and, consequently, several nodes of the grid are still not monitored and tele-controlled. At the same time, DSOs are asked to improve the network’s resilience, looking for a reduction in the frequency and impact of power outages caused by extreme weather events. The paper presents a machine learning GIS-based approach to estimate a secondary substation’s load profiles, even in those cases where monitoring sensors are not deployed. For this purpose, a large amount of data from different sources has been collected and integrated to describe secondary substation load profiles adequately. Based on real measurements of some secondary substations (medium-voltage to low-voltage interface) given by Unareti, the DSO of Milan, and georeferenced data gathered from open-source databases, unknown secondary substations load profiles are estimated. Three types of machine learning algorithms, regression tree, boosting, and random forest, as well as geographic information system (GIS) information, such as secondary substation locations, building area, types of occupants, etc., are considered to find the most effective approach.

Suggested Citation

  • Alessandro Bosisio & Matteo Moncecchi & Andrea Morotti & Marco Merlo, 2021. "Machine Learning and GIS Approach for Electrical Load Assessment to Increase Distribution Networks Resilience," Energies, MDPI, vol. 14(14), pages 1-23, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4133-:d:591057
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    References listed on IDEAS

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    1. Lindberg, K.B. & Seljom, P. & Madsen, H. & Fischer, D. & Korpås, M., 2019. "Long-term electricity load forecasting: Current and future trends," Utilities Policy, Elsevier, vol. 58(C), pages 102-119.
    2. Yee Yan, Yuk, 1998. "Climate and residential electricity consumption in Hong Kong," Energy, Elsevier, vol. 23(1), pages 17-20.
    3. Ismail Shah & Hasnain Iftikhar & Sajid Ali, 2020. "Modeling and Forecasting Medium-Term Electricity Consumption Using Component Estimation Technique," Forecasting, MDPI, vol. 2(2), pages 1-17, May.
    4. Shiju Wang & Zhiying Lu & Shaoyun Ge & Chengshan Wang, 2014. "An Improved Substation Locating and Sizing Method Based on the Weighted Voronoi Diagram and the Transportation Model," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-9, May.
    5. Hekkenberg, M. & Benders, R.M.J. & Moll, H.C. & Schoot Uiterkamp, A.J.M., 2009. "Indications for a changing electricity demand pattern: The temperature dependence of electricity demand in the Netherlands," Energy Policy, Elsevier, vol. 37(4), pages 1542-1551, April.
    6. Ashfaq Ahmad & Nadeem Javaid & Abdul Mateen & Muhammad Awais & Zahoor Ali Khan, 2019. "Short-Term Load Forecasting in Smart Grids: An Intelligent Modular Approach," Energies, MDPI, vol. 12(1), pages 1-21, January.
    7. Abu-Shikhah, Nazih & Elkarmi, Fawwaz, 2011. "Medium-term electric load forecasting using singular value decomposition," Energy, Elsevier, vol. 36(7), pages 4259-4271.
    8. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
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