Based on the theoretical research of fuzzy logic based approximate reasoning method, this paper establishes a detailed fuzzy inference knowledge (rule) system for the evaluation of the advantages and disadvantages of residential land use and the evaluation of residential land use in the high decomposition state of urban land. Two fuzzy inference knowledge (rule) systems can quickly and conveniently analyze and study the planned annual community land development and land transformation, thus laying a foundation for the community power load forecasting. The example illustrates the application process and effectiveness of the method. The establishment of the evaluation model of land use quality and inferiority 1 The selection of evaluation factors and the evaluation system The establishment of land superiority and inferiority evaluation should first determine the main influencing factors. According to the evaluation of the land quality and inferiority level of urban planning, the influencing factors can be selected from the following three aspects: traffic conditions, location conditions and ecological environment conditions. For example, the main factors affecting the quality of the B08 community in a certain city in China are: 1 distance from the fast track line; 2 distance from the city center; 3; living conditions around the living environment, C3, we use "near" and "not near" "Being as a fuzzy linguistic variable describing the distance G between the land and the fast track line and the distance C2 from the city center; using "good" and "bad" as the fuzzy linguistic variables describing the surrounding life and production environment C3 The sub-menu is "very", the linguistic variables (such as near, not near), and the fuzzy linguistic variables with the tone operator are subject to the degree function curve. See the post-satisfaction satisfaction degree membership function curve: 2000-11-30 National Nature Science Fund-funded Project (59877017) 1.2 The establishment of a semantic operator for the fuzzy inference knowledge (rule) library; the numerical sub-angle of S is the degree function curve for the calculation of tone. The mood operator is shown schematically in the figure. The basic fuzzy linguistic variables described by "very * (power value 2)), and the membership function curves of the basic linguistic variables described by other tone operators are equally available.

According to the experience and knowledge of the experts, the following rules are given for the three factors to be evaluated: 3 The approximate reasoning process with importance weight first establishes the evaluation factor for the excellent fuzzy set, and for Ci, the fuzzy set is A=close to the fast track. Line, for G is B = close to the city center area, the fuzzy set of the environmental condition evaluation results for C3 is C = 为 is V = second, according to expert experience, the importance weight of the evaluation factor is given as w = using fuzzy logic The process of approximate reasoning is to use a certain implication operator for reasoning according to a given inference rule. The fuzzy reasoning method used in this paper is the Mamdani method.

1.4 The clearing method of the inference result The membership degree B.(y) of each fuzzy linguistic variable Bi, in order to make a definite decision, it is necessary to transform it by the inverse transformation of the membership function of each fuzzy linguistic variable (as shown). Therefore, there are three factors affecting the choice of industrial land, namely: land use factor C1 transportation factor C2 environmental health factor C3, because the higher the adaptability, the better the land use condition, the level of linguistic variables (proximity), As the closer to the traffic line, the better the land use condition, the linguistic variable "Close" is the far word (Far)* as the basic word of evaluation, and the corresponding degree adverb is the same as C1. The smaller the impact on the environment, the more suitable. In the Yujian factory, the linguistic variable “Low” is used as the basic word of evaluation. The corresponding degree adverbs are the same as C1, and the higher the adaptability, according to the preference of industrial land. , summed up the following propositional rules: because - the results of the different rules of each rule, such as Y is B, said that the distance from the shoreline, sports education, shopping malls, etc. The main factors for the selection of residential land use are: C1, application distance); C2, environmental sanitation conditions; C3, convenient transportation conditions. 23 Appropriate inference rules for commercial land use Appropriate inference rules affect the main factors of urban commercial land selection: G Distance to the urban central business district; C2, traffic conditions; C3, the use of landmarks is limited to the length of this article. The preference rules for the location of residential and commercial land use are omitted. The decision of land use adaptability is similar to the superiority and inferiority of the community. In the evaluation reasoning and decision-making process, in the case of determining the importance weight of each factor to different land types, the fuzzy reasoning is carried out through the fuzzy rule synthesis rule, and the appraisal evaluation of each land block is obtained.

The land use adaptive decision results of the residential land are divided into four kinds of situations: 1 land use property decision of the open space, according to the reasoning result of the land use property, the one or two land use properties with the highest fitness evaluation are selected as the alternative set, according to each function The demand for the land in each sub-division, and finally determine the final land use property of the open space; 2 land replacement, according to the principle of land replacement, determine the status quo and the assessment of the degree of adaptation of the plan and the value of the current land use, according to the functional land The demand for land determines the final use of the land; 3 land renewal, that is, according to the principle of land renewal, the land use grade of the current land is determined to be updated; 4 the land with the same nature and grade of land use is evaluated according to its adaptability and advantages and disadvantages. As a result, the final land use intensity and power load development trend of the future cell can be determined.

3 Examples and analysis Taking 10 plots of land use of a city load distribution prediction functional partition B08 as an example, the approximate reasoning method based on fuzzy logic is used to evaluate the advantages and disadvantages of the land in the residential land evaluation. For the evaluation of sex, the eigenvalues ​​are extracted according to the three factors given in Section 1. The calculation results of the membership function are shown in Table 1. The current number of the current number is the current number. The importance weight of each factor is assumed to be: 1.0. , 5,6,8,10,9,8,8,8,8-level 3.2 Land use adaptability evaluation According to the adaptive evaluation process in Section 2 of this paper, the industrial commercial and residential nature loads of each community are respectively carried out. The status quo of the current year and the land use assessment of the planning year. The current use value of the land is given as: ST(i)=, then the result is: 71c responsiveness evaluation of jalEleetonieph residential land and commercial planning adaptability (status 7) (/, j) are the current year and planned annual land use evaluation matrix; AL(t)AL(t+1) is the difference between the current year, the planned annual land suitability assessment value and the current land actual utilization value. ; The matrix rows corresponding to I), (R), and (C) respectively indicate the industrial nature load, the residential nature load, and the commercial nature load 0.4 of each cell, and the adaptive planning adaptability and current use value according to the current status of the community. The evaluation of the membership degree (Sc, S., SP) can make the decision of the cell load category. For example, the number of the land is 550. The current land use type is industrial, and the industrial land use evaluation of planning and current status is not much different. Q521 and 01476, the residential area of ​​the 866 (0.053), because the residential area of ​​the planned residential and commercial land evaluation level is much higher than the evaluation level of its industrial land, and the use value of the current land use is evaluated as 0.45, therefore, The planned annual land use situation of the community belongs to the land use collection of land replacement, and the land replacement direction is the nature of residential and commercial land; similarly, the land use adaptability grade with the serial number of 2~10 cells can be obtained. The results of the adaptive decision-making of each district in the final level year are as follows: If the linear weighting values ​​of the various types of land use adaptability and superiority and inferior membership degree of the evaluation community are 0.8 and 0.2 respectively, the evaluation of the land use grade is shown in Table 2.

Table 2 Land use intensity rating evaluation results of each community. Adaptability Advantages and inferiority Decision number Adaptability Advantages and inferiority decision From the final land use level decision result, the land use intensity of the planning year belongs to Grade 8 and 9 of each type of land. Level, 7th, 9th, 10th, 10th, 8th, 9th, 7th, 7th and 4th conclusions The inference model for the evaluation of the land use and the evaluation of the adaptive land under the high decomposition of urban land given in this paper can be sufficient. Using the knowledge and experience of experts, the influence of many factors and importance weights on the results of inference decision-making is considered, which lays a solid theoretical foundation for the practical application of space power load forecast based on fuzzy logic.

The inference system of high decomposition cell proposed in this paper has the advantages of fast and accurate reasoning, and further enriches and perfects the knowledge base, and can adapt to the research work of community land analysis in different types of urban distribution network planning.

The practical examples show that the proposed method has broad application prospects in urban distribution network planning, which can greatly improve the accuracy of space power load forecasting and adapt to the needs of power load forecasting under market economy conditions.

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