Type-2 fuzzy logic-based multi-threaded time sequence analysis

2018 ◽  
Vol 8 (1/2) ◽  
pp. 114
Author(s):  
Lu Yang ◽  
Zhi Qiang Liu ◽  
Jian Feng Yan
Keyword(s):  
Fuzzy Logic ◽  
Sequence Analysis ◽  
Time Sequence ◽  
Time Sequence Analysis

Application Study of Load Forecasting of Central Air-Conditioning System Based on Time Sequence Analysis Method

2012 ◽  
Vol 220-223 ◽  
pp. 622-625
Author(s):  
Xue Li Zhu ◽  
Bo Dong ◽  
Yong Jun Zhu
Keyword(s):  
Sequence Analysis ◽  
Air Conditioning ◽  
Time Lag ◽  
Load Forecasting ◽  
Time Sequence ◽  
Analysis Method ◽  
Air Conditioning System ◽  
Central Air Conditioning ◽  
Heating Load ◽  
Time Sequence Analysis

With the characteristics of non-stationarity, non-linearity, time-lag of refrigeration/ heating supplying in minds, load forecasting of central air-conditioning system is carried using time sequence analysis method. Firstly, acquisition sample data of central air-conditioning system is pretreated, and random time sequence AR model of system is formulated. Then, forecasting of AR refrigeration/heating load based on Yule-walker method is conducted. In order to enhance forecasting accuracy, crossover forecasting is introduced into the load forecasting, that is, to use vertical forecasting to follow household demands for load and horizontal forecasting to track changes of weather. Then, weight cross is made to vertical and horizontal forecasting results. Finally, refrigeration/heating load forecasting software of central air-conditioning system is developed, which is used in energy-saving monitoring and control of central air-conditioning system.

NONLINEAR TIME SEQUENCE ANALYSIS

1991 ◽  
Vol 01 (03) ◽  
pp. 521-547 ◽  
Author(s):  
PETER GRASSBERGER ◽  
THOMAS SCHREIBER ◽  
CARSTEN SCHAFFRATH
Keyword(s):  
Dynamical Systems ◽  
Sequence Analysis ◽  
Lyapunov Exponents ◽  
Spectral Methods ◽  
Nonlinear Dynamical Systems ◽  
Short Review ◽  
Time Sequence ◽  
Nonlinear Dynamical ◽  
Time Sequences ◽  
Time Sequence Analysis

We review several aspects of the analysis of time sequences, and concentrate on recent methods using concepts from the theory of nonlinear dynamical systems. In particular, we discuss problems in estimating attractor dimensions, entropies, and Lyapunov exponents, in reducing noise and in forecasting. For completeness and since we want to stress connections to more traditional (mostly spectrum-based) methods, we also give a short review of spectral methods.

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