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Reducing High Energy Demand Associated with Air-Conditioning Needs in Saudi Arabia

Author

Listed:
  • Jubran Alshahrani

    (School of Engineering and Sustainable Development, De Montfort University, Leicester LE1 9BH, UK)

  • Peter Boait

    (School of Engineering and Sustainable Development, De Montfort University, Leicester LE1 9BH, UK)

Abstract

Electricity consumption in the Kingdom of Saudi Arabia (KSA) has grown at an annual rate of about 7% as a result of population and economic growth. The consumption of the residential sector accounts for over 50% of the total energy generation. Moreover, the energy consumption of air-conditioning (AC) systems has become 70% of residential buildings’ total electricity consumption in the summer months, leading to a high peak electricity demand. This study investigates solutions that will tackle the problem of high energy demand associated with KSA’s air-conditioning needs in residential buildings. To reduce the AC energy consumption in the residential sector, we propose the use of smart control in the thermostat settings. Smart control can be utilized by (i) scheduling and advance control of the operation of AC systems and (ii) remotely setting the thermostats appropriately by the utilities. In this study, we model typical residential buildings and, crucially, occupancy behavior based on behavioral data obtained through a survey. The potential impacts in terms of achievable electricity savings of different AC operation modes for residential houses of Riyadh city are presented. The results from our computer simulations show that the solutions intended to reduce energy consumption effectively, particularly in the advance mode of operation, resulted in a 30% to 40% increase in total annual energy savings.

Suggested Citation

  • Jubran Alshahrani & Peter Boait, 2018. "Reducing High Energy Demand Associated with Air-Conditioning Needs in Saudi Arabia," Energies, MDPI, vol. 12(1), pages 1-29, December.
    • Handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:87-:d:193706
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    References listed on IDEAS

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    Cited by:

    1. Tayeb Brahimi, 2019. "Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia," Energies, MDPI, vol. 12(24), pages 1-16, December.

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