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Electricity Demand Forecasting of Hospital Buildings in Istanbul

Author

Listed:
  • Ibrahim Soyler

    (Directory of Health, Healthcare Investments Branch, Istanbul 34122, Turkey)

  • Ercan Izgi

    (Electrical Engineering, Yıldız Technical University, Istanbul 34220, Turkey)

Abstract

Electricity demand forecasting is essential for utilities. For the consumer, predictability of demand is vital for efficient operation, installation, sizing and maintenance planning. Hospitals, which are among the institutions with high-energy consumption, provide uninterrupted service 24 h a day, 7 days a week. Every hospital building is unique, and many do not conform to a typical shape or floor plan. Depending on the services provided, each hospital can differ significantly in terms of energy demand. Therefore, demand forecasting is one of the most complex elements of hospital construction. Although there are many studies on energy optimization related to hospital buildings in the literature, there is a knowledge gap regarding the maximum power estimation of hospitals. In this study, the annual electrical energy use of 23 public hospitals with over 100 beds in Istanbul is measured, and after determining the monthly peak loads, two new forecasting models are generated using regression techniques for maximum demand forecasting. It is determined that the design criteria used in power calculations in hospitals was very high. A positive result was obtained from the linear regression technique, which is one of the basic regression techniques, and it was shown that the maximum power needs of the hospital can be estimated with great confidence by determining a new design factor in the light of the determined values. This study allows designers to set maximum demands and select transformer and generator sizes with a single formula.

Suggested Citation

  • Ibrahim Soyler & Ercan Izgi, 2022. "Electricity Demand Forecasting of Hospital Buildings in Istanbul," Sustainability, MDPI, vol. 14(13), pages 1-16, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:8187-:d:855905
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    References listed on IDEAS

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    Cited by:

    1. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2023. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms," Energies, MDPI, vol. 16(11), pages 1-23, June.
    2. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2022. "Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island," Energies, MDPI, vol. 15(16), pages 1-22, August.

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