Recurrence Interval Analysis on Electricity Consumption of an Office Building in China

Abstract

Abstract:The energy management of office buildings has been a rising concern for owners, researchers, and energy suppliers. The volatility of power load in office buildings threatens energy consumption and risks device security. This paper investigates the load fluctuation patterns in an office building based on user data, using recurrence interval analysis for different thresholds. The recurrence intervals of volatility are fitted by stretched exponential distribution, from which the probability density function is derived. Then, the short-term and long-term memory effect on the fluctuations are learned by conditional probability density function and multifractal detrended fluctuation analysis, respectively. A hazard function is further established to analyze the risk estimation of load volatility and derive the value at risk (VaR). Thus, a functional relationship has been established between average recurrence interval and threshold. The methodology and analysis results addressed in this paper help to understand load fluctuation patterns and aid in the design of energy consumption strategies in office buildings. According to the results of our research, conclusions and management suggestions are provided at the end of this paper.

Detection of Pipeline Leaks Using Fractal Analysis of Acoustic Signals

“…It can be seen that its shape differs from the parabolic one, which is typical for multifractal objects. Similar forms of multifractal spectrum are presented in the works of other authors [37][38][39]. According to the graph of the generalized Hurst exponent (Figure 9), it can be seen that the acoustic signals of a defective pipeline are characterized by a pronounced nonlinear dependence h(q).…”

A New Perspective on Improving Hospital Energy Administration Based on Recurrence Interval Analysis

“…Regarding the format of fitting function, the stretched exponential distribution function is overwhelmingly preferred by researchers [11,25,26], which is also our first choice here in this paper. The standard form can be specified as:…”

“…So far, 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]. Also, the introduction of high-frequent data is able to reflect the instantaneous fluctuation of power consumption, which does count for much more in hospital energy administration than daily data.…”

“…However, it has to be very careful to forecast the power consumption in the daytime, since either overestimation or underestimation will result in electricity waste, which would violate the intention. Therefore, as a management tool, power load data plays an indispensable role in forecasting electricity consumption [10,11].…”

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