Electricity Demand Forecasting of Hospital Buildings in Istanbul

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.

Automated summary

Electricity demand forecasting is crucial for utilities and efficient operation. Hospitals, with their unique characteristics and high-energy consumption, pose challenges in predicting maximum power demand. This study measures the annual electrical energy use of 23 public hospitals in Istanbul and develops two new forecasting models using regression techniques. The results demonstrate the potential of confidently estimating maximum power needs and informing the selection of transformer and generator sizes.