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.
Renewable energy sources are becoming increasingly important due to climate change and the energy crisis. Wind energy projects and applications in particular have been growing recently. Meteorological parameters such as wind speed, ambient temperature, relative humidity, and air pressure directly affect wind turbine electricity generation. Understanding the relationships among these parameters is necessary to determine wind energy potential and to predict electricity generation. The current work is based on data obtained from a 1.5 kW wind turbine constructed inİstanbul Technical University's meteorological park in Turkey. A one minute time interval was used in the data analysis. In this study, upwind/downwind meteorological variables of the wind turbine rotor area were investigated and analyzed in detail. It was observed that the turbine used in this study performs better under low wind speed conditions than under high wind speed conditions. The difference in relative humidity between the upwind and downwind rotor area reached 4%. It is also important to note that, depending on wind speed, temperature differences between the upwind and downwind rotor area reached 5%. Equations for upwind/downwind variables were obtained for each meteorological parameter.
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