Abstract
Based on 15-min high-frequency power load data from a Chinese hospital, by adopting recurrence interval analysis, an attempt is made to provide a new perspective for improving hospital energy administration in electrical efficiency and safety. Initially, the definition of extreme fluctuation of the power load, as well as the recurrence interval, is given. Next, the stretched exponential distribution function is provided, which fits quite well with the probability density distribution of recurrence intervals. Then, tests on recurrence intervals, including scaling behavior and short-term and long-term memory effect are conducted. At last, a risk estimation method of VaR is proposed for hospital energy administrator to forecast risk probability. Results clearly indicate that the recurrence interval analysis (RIA) method works well on forecasting extreme power load fluctuation in hospital. However, there is no evidence to support the existence of the long-term memory effect of recurrence intervals, which means that hospital energy management plans have to be continuously fixed and updated with time. Some relevant applicant suggestions are provided for the energy administrator at the end of this paper.
Highlights
Hospitals all around the world consume an immense scale of energy in different ways and forms every year, which represent around 6% of the total energy consumption in the utility buildings sector [1]
In the United States (US), it is declared by the Department of Energy that reducing hospital energy consumption by 20–30% is entirely feasible [3], while this number could soar to 45% in practice [4]
How to balance the electricity efficiency and electrical safety is a tough job for the hospital energy
Summary
Hospitals all around the world consume an immense scale of energy in different ways and forms every year, which represent around 6% of the total energy consumption in the utility buildings sector [1]. To the best knowledge of authors, the regression analysis method presuming the functional relationships of variables, are widely used in relevant researches since they can accurately quantify the tendency of power consumption [12,13,14]. To forecast the risk probability of shock load as well as to avoid errors from presumable function and excessive round-off, the recurrence interval analysis (RIA) method is adopted in this paper. This RIA method has proved to be effective in forecasting natural hazards, financial market volatility, and electricity consumption features in units of enterprise and office building [11,20,21,22,23].
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