Analysis of Residential Land Use in Spatial Electric Load Forecasting (2) - Application of Fuzzy Reasoning in Residential Land Use Analysis

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 land use of residential areas under the condition of high decomposition of urban land and adaptive evaluation of the use of residential land. Two fuzzy inference knowledge (rules) systems can quickly and easily analyze and study the planned annual land development and land reform, thus laying a foundation for the prediction of residential electric power load. Examples illustrate the application process and effectiveness of the method. 1 Establishment of Evaluation Models for the Advantages and Disadvantages of Land Use in Residential Areas 1 Selection of evaluation factors and establishment of evaluation systems The work of evaluating the advantages and disadvantages of land should first determine the main influencing factors. According to the evaluation of the level of land planning for urban planning, the influencing factors can be selected from the following three aspects: traffic conditions, location conditions, and ecological and environmental conditions. For example, the main factors affecting the pros and cons of the B08 plot in a certain city in China are: 1 Distance from the fast track line; 2 Distance from the center of the city G; 3 Conditions around living and production environment C3 We use “near” and “near” ”as a fuzzy linguistic variable that describes the distance G between the block and the fast track line and the distance C2 from the city center; use “good” and “bad” as the linguistic variables to describe the fuzzy linguistic variables of the surrounding living and production environment C3. The subclass is "extremely" and linguistic variables (such as near and not close). The membership function function of fuzzy linguistic variables with modal operator can be seen later in this paper. Satisfaction degree Satisfaction membership function curve: 2000-11-30 National Nature Science Foundation Project (59877017) 1.2 Establishment of tone inference (knowledge) database of tone inference operator; S's lower number mark is calculated as a function of the tone. As shown in the figure, the tone operator is schematically shown in the figure. "The basic fuzzy linguistic variables described by the * (power value of 2), and the basic linguistic membership function curves described by other modal operators are also available.

According to the expert's experience and knowledge, the following rules are given for the three factors to be evaluated: 3 Approximate reasoning process with weights of importance First establish the evaluation factors for the optimal fuzzy set, for Ci its fuzzy set is A=close to the fast track Line, for G = B = close to the central area of ​​the city, for C3 = C = 模糊, the fuzzy set of evaluation results for environmental conditions is V = second, according to expert experience, given the weight of importance of evaluation factors is w = fuzzy logic The process of approximate reasoning is to use a certain implication operator to reason in accordance with given inference rules. The fuzzy reasoning method used in this paper is the Mamdani method.

1.4 Clarification of reasoning results The degree of membership of each fuzzy linguistic variable Bi, B.(y), is necessary to make an explicit decision by transforming the inverse transformation of the membership functions of each fuzzy linguistic variable (as shown). In order to influence the choice of industrial land, there are three factors, that is: land conditions factors C1 transportation factors C2 environmental health factors C3 level, because the higher the adaptability, the better the land conditions, the level of linguistic variables (proximity), Since the closer to the traffic line, the better the land conditions, the linguistic variable “Close” negative term is Far (*) as the basic word for evaluation, and the corresponding degree adverb is the same as C1. The smaller the impact on the environment, the more suitable it is. Yujian Factory uses the linguistic variable “Low” negative word as High* as the basic word for evaluation. The corresponding degree adverb is the same as C1 according to the conditions. The higher the adaptability is, the better the evaluation principle is based on the preference of industrial land. The following propositional rules are summed up: As the result of the separate inference of each rule, such as Y is B, the distance to the shoreline, sports education, the setting of bookmark2 in the mall, etc. The main factors for the selection of residential land are: C1, distance (distance); C2, environmental sanitation conditions; C3, convenience of traffic conditions; 23 Commercial Appropriate Land Use Adaptability Approximate Reasoning Rules Main factors affecting urban commercial land selection are: G , Distance from the city center commercial area; C2, traffic conditions; C3, land use limited to space limitations in this paper omitted preference rules for residential and commercial site selection 24 Community land use adaptation decisions similar to the advantages and disadvantages of the community Evaluation of reasoning and decision-making process, in the case of determining the weight of each factor on the importance of different types of land, through the fuzzy rules of combination of fuzzy reasoning, and clear processing to get the adaptive evaluation of each plot.

The decision-making results of land use adaptation in residential plots are divided into four types: 1 The decision on the nature of land use, according to the reasoning result of the nature of open land, the nature of one or two types of land with the highest degree of fitness is selected as an alternative set, according to each function. Subdivision of land requirements for each category of land, and final determination of the nature of the ultimate land use of the open space; 2 Land replacement, according to the principle of land replacement, determine the current status and planning of the adaptive evaluation level and status of land use value assessment, according to the functional division of the classification of land The demand will determine the ultimate use nature of the land; 3 land renewal, that is, based on the principle of land renewal, determine the land level of the current renewal of the land; 4 the land used for the nature and grade of the land shall be evaluated according to its adaptability and strengths and weaknesses. As a result, the final land use intensity and power load development trend of the future community can be determined.

3 Examples and analysis Using 10 plots of residential land in function partition B08 of a city load distribution as an example to illustrate the approximative reasoning method based on fuzzy logic in the evaluation of residential land use 1 Evaluation of the advantages and disadvantages of residential land use First of all, the advantages and disadvantages of using land The evaluation of sexuality is based on the three factors given in Section 1.1. The eigenvalues ​​are extracted. The calculation result of the membership function is shown in Table 1. The current status is numbered. The current number is assumed. The importance weight of each factor is given as: 1.0 Grades of 5, 6, 8, 8, 10, 9, 8, 8, 8, and 8 3.2 Land Use Adaptability According to the adaptive evaluation process in Section 2 of this paper, industrial property and residential property load of each community The current status of the land use year and the planning year's land use evaluation value are given as: ST(i)=, the result is: 71c Rejection evaluation jalEleetonieph Land use and commercial planning adaptability (Current status 7) ,(/,j) are respectively the current year and the planned year land use adaptability evaluation matrix; AL(t)AL(t+1) are the current year, and the difference between the value of the land adaptive assessment and the actual land use value in the planning year. ; I), The matrix rows corresponding to (R) and (C) respectively indicate that each cell is 0.4 for industrial property load, residential property load and commercial property load, and then adaptability and current use value according to the current situation of the community. The evaluation of the degree of membership (Sc, S., SP) can be used to determine the type of community load. For example, the number 550 of the residential area, the current type of land for industry, planning and the status of the industrial land adaptability evaluation is not much difference, respectively. Q521 and 01476, the residential area 866 (0.053), because the plot of residential and commercial land for the evaluation of the year is much higher than its industrial land evaluation level, and its use value of the current land use is evaluated as 0. 45, therefore, The planned annual land use of this residential area belongs to the land replacement land use set, and the land replacement direction is the nature of residential and commercial land use. Similarly, the land acquisition land number is the land use adaptability level of 2~10 residential areas. The final level of adaptive decision-making results for each plot of land use is: If the linear weights of the suitability of the appraisal community and the various types of plots for land use are 0.8 and 0.2, respectively, then the appraisal of the grade of residential plots is shown in Table 2.

Table 2 Land strength level evaluation results of each residential area No. Adaptability Adequacy and Inferiority Decision Number Adaptability Adverse/Inferiority Decision Based on the final land use level decision results, the land use intensity of the planned year belongs to Grade 8 and 9 for each type of land. Grade 7, Level 7, Level 9, Level 10, Level 10, Level 8 and Level 9 Level 7 and Level 7 Conclusions The reasoning model for the evaluation of the land use and the inferiority of the urban land and the adaptive evaluation under the high decomposition of the urban land given in this paper can be sufficient. Using the knowledge and experience of experts, the influence of many factors and their importance weight on the reasoning and decision-making results is considered, which lays a solid theoretical foundation for the practical application of spatial power load forecasting based on fuzzy logic.

The reasoning system of the high-decomposition cell proposed in this paper has the advantages of fast and accurate reasoning, and it further enriches and improves the knowledge base, and can adapt to the study of residential land use analysis in the distribution network planning of different types of cities.

Practical examples show that the proposed method has broad application prospects in urban distribution network planning, can greatly improve the accuracy of spatial power load forecasting, and meet the needs of power load forecasting under market economy conditions.

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