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Research of FRARMA Forecasting Algorithm in Medium and LongTerm Electric Power Load Forecasting

Author: YangZiGuo
Tutor: ZhangChengWei
School: Dalian University of Technology
Course: Information management and e-government
Keywords: Fuzzy Clustering ARMA model FRARMA model Long-term load forecasting
CLC: F426.61;TM715
Type: Master's thesis
Year: 2010
Downloads: 114
Quote: 0
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Abstract


Medium and long term load forecasting is usually monthly, annual forecast of electricity is the basic work of the electricity sector, and for the production, marketing plans, etc. to provide a favorable basis for the formulation. Law of its development by economic, social, climate and other environmental factors are large and there are differences between the various regions, it has ambiguity. Fuzzy clustering theory can describe the influence, but itself when there is still insufficient in predicting. Maximize the advantages of fuzzy clustering and make up for its lack of theoretical and practical aspects will have a certain significance. Traditional long-term prediction fuzzy clustering algorithm for weighting the independent variable is not set, cut the level of the collection element selected by a human operator, a single relevant factor calculation method, proposed in this paper for the above shortcomings term load forecasting fuzzy clustering algorithm - the use of correlation analysis calculated from the variable weight; establish relevant factor calculation method library; select all the elements of the matrix is ??equivalent to the weight of the set is a collection of cut-off levels. ARMA (ARIMA) model is an ideology based on time series forecasting model can describe the linear dynamic process, its modeling sequence must be stationary time series. Long-term power load has obvious trends feature is not stationary time series, although they may be eliminated through the high-order difference sequence trend has been stationary time series, but also eliminates the long-term characteristics of the original sequence, resulting in a large loss of information. ARMA model for the shortcomings, the paper has been improved and made its long-term power load RARMA (regression - ARIMA) forecasting model, a regression model library linear fit of the load sequence, according to the principle of minimum residual variance fitting choice the best fit regression model. Proved by experiments, the residual series is stationary time series, therefore the best regression model residuals ARMA model as object modeling, and through model identification, parameter estimation of ARMA residuals derived prediction model, two load forecasting values ??obtained by the collection. Further studies have shown that fuzzy clustering algorithm environmental factors characteristic variables correspond stationary time series with ARMA model to predict the prospect of environmental factors in the data, fuzzy clustering algorithm can solve the environmental factors ranging deficiencies, which further raised FRARMA (Fuzzy Regression ARIMA) forecasting model. Based on the above work, the preparation of a complete and long-term load forecasting fuzzy clustering algorithm, RARMA algorithm, FRARMA algorithm and applied in practice. In the \theoretically achieved certain results, and after the system has a good practice to validate the value.

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CLC: > Industrial Technology > Electrotechnical > Transmission and distribution engineering, power network and power system > Theory and Analysis > Power system planning
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