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Electricity usage scheduling in smart building environments using smart devices.

Eunji Lee1, Hyokyung Bahn2

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This study introduces a smart building electricity scheduling algorithm that uses real-time pricing to dynamically adjust device power modes. The new approach significantly reduces electricity costs for smart buildings.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Smart grid technologies and smart meters enable real-time electricity usage monitoring in buildings.
  • Utility companies implement time-of-use electricity pricing, with higher rates during peak demand periods.

Purpose of the Study:

  • To develop an efficient electricity usage scheduling algorithm for smart buildings.
  • To dynamically manage electric device power modes based on real-time electricity pricing.

Main Methods:

  • Formulating electricity usage scheduling as a real-time task scheduling problem.
  • Employing a genetic algorithm heuristic to optimize scheduling within time constraints.
  • Implementing the algorithm on a smart device to detect price changes and adjust power modes.

Main Results:

  • The proposed algorithm reduces smart building electricity charges by an average of 25.6%, with a maximum reduction of 33.4%.
  • The genetic algorithm effectively navigates the complex search space for optimal scheduling.

Conclusions:

  • The developed algorithm offers a practical and effective solution for reducing electricity costs in smart buildings.
  • Dynamic scheduling based on real-time pricing is a viable strategy for energy management in smart environments.