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

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.

[1]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[2]  V. R. Dehkordi,et al.  Hourly prediction of a building's electricity consumption using case-based reasoning, artificial neural networks and principal component analysis , 2015 .

[3]  Mesut Avci,et al.  Demand Response-Enabled Model Predictive HVAC Load Control in Buildings using Real-Time Electricity Pricing , 2013 .

[4]  Pengjian Shang,et al.  The scaling properties of stock markets based on modified multiscale multifractal detrended fluctuation analysis , 2015 .

[5]  Afshin Afshari,et al.  Inverse modeling of the urban energy system using hourly electricity demand and weather measurements, Part 1: Black-box model , 2017 .

[6]  Nelson Fumo,et al.  Methodology to estimate building energy consumption using EnergyPlus Benchmark Models , 2010 .

[7]  Dongjun Suh,et al.  An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea , 2012 .

[8]  James W. Taylor,et al.  An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting , 2010, Neural Networks.

[9]  M. Delimar,et al.  Dynamic Hybrid Model for Short-Term Electricity Price Forecasting , 2014 .

[10]  H. Stanley,et al.  Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series , 2002, physics/0202070.

[11]  H. Kantz,et al.  Recurrence time analysis, long-term correlations, and extreme events. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Zhi-Qiang Jiang,et al.  Extreme value statistics and recurrence intervals of NYMEX energy futures volatility , 2012, 1211.5502.

[13]  P. Ivanov,et al.  Effect of extreme data loss on long-range correlated and anticorrelated signals quantified by detrended fluctuation analysis. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  G. Hagerman,et al.  North Atlantic wind and wave climate: Observed extremes, hindcast performance, and extratropical recurrence intervals , 2012, 2012 Oceans.

[15]  W. Sharp,et al.  Reading a 400,000-year record of earthquake frequency for an intraplate fault , 2017, Proceedings of the National Academy of Sciences.

[16]  Kazuko Yamasaki,et al.  Scaling and memory in volatility return intervals in financial markets. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Hamid Shaker,et al.  Short-term electricity load forecasting of buildings in microgrids , 2015 .

[18]  Z.A. Bashir,et al.  Applying Wavelets to Short-Term Load Forecasting Using PSO-Based Neural Networks , 2009, IEEE Transactions on Power Systems.

[19]  Sousso Kelouwani,et al.  Household electricity demand forecasting using adaptive conditional density estimation , 2017 .

[20]  Lon-Mu Liu,et al.  Dynamic structural analysis and forecasting of residential electricity consumption , 1993 .

[21]  V. Bianco,et al.  Electricity consumption forecasting in Italy using linear regression models , 2009 .

[22]  R. Hughson,et al.  Coarse-graining spectral analysis: new method for studying heart rate variability. , 1991, Journal of applied physiology.

[23]  Merve Bedir,et al.  Determinants of electricity consumption in Dutch dwellings , 2013 .

[24]  Gwo-Ching Liao,et al.  Application of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short-term load forecasting , 2006, IEEE Transactions on Evolutionary Computation.

[25]  Yuancheng Li,et al.  Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior , 2016, Algorithms.

[26]  E. Kara,et al.  Behavioral patterns and profiles of electricity consumption in dutch dwellings , 2017, Architecture and the Built Environment.

[27]  S. F. Ghaderi,et al.  Integration of Artificial Neural Networks and Genetic Algorithm to Predict Electrical Energy consumption , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[28]  Youngdeok Hwang,et al.  Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings , 2016 .

[29]  Harvard Medical School,et al.  Effect of nonstationarities on detrended fluctuation analysis. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  Khuram Pervez Amber,et al.  Electricity consumption forecasting models for administration buildings of the UK higher education sector , 2015 .

[31]  Yi Lu,et al.  Multi-scale recurrence quantification analysis of heartbeat interval series in healthy vs. Heart failure subjects , 2014, 2014 7th International Conference on Biomedical Engineering and Informatics.

[32]  Dong-Hua Wang,et al.  Risk estimation of CSI 300 index spot and futures in China from a new perspective , 2015 .

[33]  Wei-Xing Zhou,et al.  Recurrence interval analysis of high-frequency financial returns and its application to risk estimation , 2009, 0909.0123.

[34]  Wai Ming To,et al.  Modeling of Monthly Residential and Commercial Electricity Consumption Using Nonlinear Seasonal Models—The Case of Hong Kong , 2017 .

[35]  Pengjian Shang,et al.  Chaotic SVD method for minimizing the effect of exponential trends in detrended fluctuation analysis , 2009 .