원문정보
초록
영어
In this paper, adjust the system parameters back-propagation algorithm based on fuzzy similarity interval type proposed by the fuzzy rule base to streamline redundant fuzzy sets, we can also merge with the means to reduce the number of redundant fuzzy rules, then singular value decomposition method is preferred fuzzy rules. The algorithm can effectively eliminate the adverse effects caused by redundant fuzzy rule, which improve the interpretability of fuzzy rules to reduce the computational complexity of the fuzzy reasoning process, and to improve the approximation accuracy of the system. Based on the long-term and short-term load power load characteristics analysis, to identify the influence of the load itself changes and related factors, gray system theory, neural network model and chaotic time series methods, models and methods for forecasting power load range were research. Examples verified, interval prediction has better precision, demonstrate the effectiveness of the interval prediction algorithm, the research results can be used in power market analysis and forecasting systems, power system operation and provide scientific basis for management decisions.
목차
1. Introduction
2. The Related Theory
2.1 Research Status of Fuzzy Systems
2.2 Load Forecasting Issues to Consider
3. Type Two Fuzzy Logic Theory
3.1 Interval Type Two Fuzzy Similarity
3.2 Error Analysis Based on Grey Theory
3.3 Long Term Load Forecasting Model Range
4. The Experiment and Analysis
4.1 Prediction Analysis
4.2 Calculation Examples
5. Conclusion
Acknowledgments
References