The Recurrence Interval Difference of Power Load in Heavy/Light Industries of China

The significant fluctuation of industrial electricity consumption has a high impact on power load, which makes the research on recurrence intervals between extreme events of theoretical and practical significance. The study uses a high-frequency data of heavy and light industries and employs recurrence interval analysis in different thresholds. We find that the reoccurrence interval of volatility can fit with the stretched exponential function and the probability density functions of recurrence intervals in various thresholds shows a scaling behavior. Then, the conditional probability density function and the multifractal detrended fluctuation analysis demonstrate the existence of short-range correlation, long-range correlation, and multifractal properties, respectively. We further construct a hazard function, introduce recurrence intervals into VaR calculation and establish a functional relationship between average recurrence interval and threshold. Following this result, we also shed light on policy discussion for multi-industrial electricity supply management.

[1]  Aitor Ciarreta,et al.  Modeling and forecasting realized volatility in German–Austrian continuous intraday electricity prices , 2017 .

[2]  J. Contreras,et al.  Forecasting Next-Day Electricity Prices by Time Series Models , 2002, IEEE Power Engineering Review.

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

[4]  Bing Dong,et al.  A hybrid model approach for forecasting future residential electricity consumption , 2016 .

[5]  Yi Liang,et al.  Analysis and Modeling for China's Electricity Demand Forecasting Based on a New Mathematical Hybrid Method , 2017, Inf..

[6]  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.

[7]  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.

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

[9]  Karin Kandananond Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach , 2011 .

[10]  Shlomo Havlin,et al.  Long-term memory: a natural mechanism for the clustering of extreme events and anomalous residual times in climate records. , 2005, Physical review letters.

[11]  Werner Marx,et al.  The history of the stretched exponential function , 2007 .

[12]  K. Sreenivasan FRACTALS AND MULTIFRACTALS IN FLUID TURBULENCE , 1991 .

[13]  Carl Kisslinger,et al.  The stretched exponential function as an alternative model for aftershock decay rate , 1993 .

[14]  E. M. Nifontov,et al.  Statistics of return intervals between long heartbeat intervals and their usability for online prediction of disorders , 2009 .

[15]  Guangxi Cao,et al.  Asymmetric multifractal scaling behavior in the Chinese stock market: Based on asymmetric MF-DFA , 2013 .

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

[17]  Pei-Chann Chang,et al.  Monthly electricity demand forecasting based on a weighted evolving fuzzy neural network approach , 2011 .

[18]  Robert J. Marks,et al.  Electric load forecasting using an artificial neural network , 1991 .

[19]  P. H. Figueiredo,et al.  Multifractal behavior of wild-land and forest fire time series in Brazil , 2013 .

[20]  Yaolin Shi,et al.  Recurrence interval of the 2008 Mw 7.9 Wenchuan earthquake inferred from geodynamic modelling stress buildup and release , 2017 .

[21]  J. Siegel,et al.  Application of the stretched exponential function to fluorescence lifetime imaging. , 2001, Biophysical journal.

[22]  K. Hopcraft,et al.  Accuracy analysis of measurements on a stable power-law distributed series of events , 2006 .

[23]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[24]  J. S. Murguía,et al.  Multifractal properties of elementary cellular automata in a discrete wavelet approach of MF-DFA , 2009, 0908.3345.

[25]  M. A. Rafe Biswas,et al.  Regression analysis for prediction of residential energy consumption , 2015 .

[26]  A. Vulpiani,et al.  Rare events and scaling properties in field-induced anomalous dynamics , 2012, 1212.1447.

[27]  H. Stanley,et al.  Effect of trends on detrended fluctuation analysis. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

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

[30]  B. Ewing,et al.  Price volatility and residential electricity decisions: Experimental evidence on the convergence of energy generating source , 2017 .

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

[32]  Shlomo Havlin,et al.  Return intervals of rare events in records with long-term persistence , 2004 .

[33]  Fang Wang,et al.  Multifractal detrended fluctuation analysis for clustering structures of electricity price periods , 2013 .

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

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

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

[37]  Heng Huang,et al.  Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities , 2015, IEEE Transactions on Smart Grid.

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

[39]  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.

[40]  Gregory A Voth,et al.  A multiscale coarse-graining method for biomolecular systems. , 2005, The journal of physical chemistry. B.

[41]  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.

[42]  J. Elder,et al.  Long memory in energy futures prices , 2008 .

[43]  Geoff Boeing,et al.  Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction , 2016, Syst..

[44]  Silviya Popova,et al.  A forecasting model based on time series analysis applied to electrical energy consumption , 2015 .

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

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

[47]  Julián Pérez-García,et al.  Analysis and long term forecasting of electricity demand trough a decomposition model: A case study for Spain , 2016 .

[48]  D. Sornette,et al.  Stretched exponential distributions in nature and economy: “fat tails” with characteristic scales , 1998, cond-mat/9801293.

[49]  Armin Bunde,et al.  Effect of nonlinear correlations on the statistics of return intervals in multifractal data sets. , 2007, Physical review letters.

[50]  Florian Müller-Plathe,et al.  Coarse-graining in polymer simulation: from the atomistic to the mesoscopic scale and back. , 2002, Chemphyschem : a European journal of chemical physics and physical chemistry.

[51]  M. Ulagammai,et al.  Application of bacterial foraging technique trained artificial and wavelet neural networks in load forecasting , 2007, Neurocomputing.

[52]  Shlomo Havlin,et al.  Multifractal detrended $uctuation analysis of nonstationary time series , 2002 .

[53]  P. McSharry,et al.  Probabilistic forecasts of the magnitude and timing of peak electricity demand , 2005, IEEE Transactions on Power Systems.

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

[55]  E. Dologlou,et al.  Stability of a power law relation between characteristics of earthquakes and electric precursors , 2012 .

[56]  Niu Dong-xiao,et al.  Study on Chaos Characteristics of Electricity Price Based on Power-Law Distribution , 2011 .

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

[58]  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.