Fuzzy based time series forecasting of electric load

The paper presents a new method for the forecast of time series. This method requires no model of the signal process. It is based on an arrangement of the current situation with situations from the past. With the aid of a fuzzy-processing algorithm, the forecast values are determined from this arrangement. The effectiveness is shown at an example for electric load forecast of a power distribution company.

[1]  I. Grossmann,et al.  Global optimization of nonconvex mixed-integer nonlinear programming (MINLP) problems in process synthesis , 1988 .

[2]  Panos Y. Papalambros,et al.  Solution of mixed-discrete structural optimization problems with a new sequential linearization algorithm , 1990 .

[3]  A. M. Geoffrion.  Generalized Benders decomposition , 1972 .

[4]  Christodoulos A. Floudas,et al.  Nonlinear and Mixed-Integer Optimization , 1995 .

[5]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[6]  Donald M. Simmons.  Linear programming for operations research , 1972 .

[7]  I. Grossmann,et al.  A combined penalty function and outer-approximation method for MINLP optimization : applications to distillation column design , 1989 .

[8]  K. Steiglitz,et al.  Adaptive step size random search , 1968 .

[9]  Alexander H. G. Rinnooy Kan,et al.  Decomposition in general mathematical programming , 1993, Math. Program..

[10]  Timothy Masters,et al.  Neural, Novel & Hybrid Algorithms for Time Series Prediction , 1995 .

[11]  Sven Leyffer,et al.  Deterministic Methods for Mixed Integer Nonlinear Programming , 1993 .

[12]  Sven Leyffer,et al.  Solving mixed integer nonlinear programs by outer approximation , 1994, Math. Program..

[13]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[14]  Romualdo Salcedo,et al.  Solving nonconvex nonlinear programming and mixed-integer nonlinear programming problems with adaptive random search , 1992 .

[15]  Omprakash K. Gupta,et al.  Branch and Bound Experiments in Convex Nonlinear Integer Programming , 1985 .

[16]  F. Körner.  A new branching rule for the branch and bound algorithm for solving nonlinear integer programming problems , 1988 .

[17]  R. Fletcher.  Practical Methods of Optimization , 1988 .

[18]  Douglass J. Wilde,et al.  Foundations of Optimization. , 1967 .

[19]  A. Kandel.  Fuzzy Mathematical Techniques With Applications , 1986 .

[20]  Peter C. Young,et al.  Recursive Estimation and Time Series Analysis , 1984 .

[21]  B. V. Shah,et al.  Some Algorithms for Minimizing a Function of Several Variables , 1964 .

[22]  R. E. Kalman,et al.  Optimum Seeking Methods. , 1964 .

[23]  Robert Hooke,et al.  `` Direct Search'' Solution of Numerical and Statistical Problems , 1961, JACM.

[24]  R Eglese,et al.  Business Optimisation: Using mathematical programming , 1997, J. Oper. Res. Soc..

[25]  H. Zimmermann.  DESCRIPTION AND OPTIMIZATION OF FUZZY SYSTEMS , 1975 .

[26]  L. R. Foulds.  Optimization Techniques: An Introduction , 1981 .

[27]  Ignacio E. Grossmann,et al.  An outer-approximation algorithm for a class of mixed-integer nonlinear programs , 1986, Math. Program..

[28]  Aimo Törn.  Clustering Methods in Global Optimization , 1986 .

[29]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .