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Dealing with Demand in Electric Grids with an Adaptive Consumption Management Platform

Author

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  • Diego M. Jiménez-Bravo
  • Juan F. De Paz
  • Gabriel Villarrubia
  • Javier Bajo

Abstract

The control of consumption in homes and workplaces is an increasingly important aspect if we consider the growing popularity of smart cities, the increasing use of renewable energies, and the policies of the European Union on using energy in an efficient and clean way. These factors make it necessary to have a system that is capable of predicting what devices are connected to an electrical network. For demand management, the system must also be able to control the power supply to these devices. To this end, we propose the use of a multiagent system that includes agents with advanced reasoning and learning capacities. More specifically, the agents incorporate a case-based reasoning system and machine learning techniques. Besides, the multiagent system includes agents that are specialized in the management of the data acquired and the electrical devices. The aim is to adjust the consumption of electricity in networks to the electrical demand, and this will be done by acting automatically on the detected devices. The proposed system provides promising results; it is capable of predicting what devices are connected to the power grid at a high success rate. The accuracy of the system makes it possible to act according to the device preferences established in the system. This allows for adjusting the consumption to the current demand situation, without the risk of important home appliances being switched off.

Suggested Citation

  • Diego M. Jiménez-Bravo & Juan F. De Paz & Gabriel Villarrubia & Javier Bajo, 2018. "Dealing with Demand in Electric Grids with an Adaptive Consumption Management Platform," Complexity, Hindawi, vol. 2018, pages 1-14, March.
    • Handle: RePEc:hin:complx:4012740
    DOI: 10.1155/2018/4012740
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    References listed on IDEAS

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    1. Chen, Yibo & Tan, Hongwei, 2017. "Short-term prediction of electric demand in building sector via hybrid support vector regression," Applied Energy, Elsevier, vol. 204(C), pages 1363-1374.
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    Cited by:

    1. Kyungjin Yoo & Seth Blumsack, 2018. "The Political Complexity of Regional Electricity Policy Formation," Complexity, Hindawi, vol. 2018, pages 1-18, December.

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