The concept of space power load forecasting, also called district load forecasting, was first proposed by Wills of the United States in 1983. It is defined as the power supply scope of the future power department. According to the planned urban power grid voltage level, the urban land use will be in accordance with certain The principle is divided into regular (grid) or irregular cells of corresponding size (can be as small as 0.01km2). Through analyzing and predicting the characteristics and development rules of land use of urban communities in the planning year, the power users in the corresponding communities can be further predicted. The location, quantity, and time of load distribution until IJ So far, in the area of ​​space power load forecasting, there are mainly three methods for analyzing residential land use at home and abroad: (1) Analytical methods, ie establishing a detailed model of residential land use, Simulation of the use of residential land changes, the disadvantage of this model is that it is difficult to deal with the problem of urban transformation, so it does not have universal significance, 2 frequency domain analysis, that is, through the input of experts or planners for each type of community adaptive load points The value directly determines the number of new power users in the future; 3 Based on fuzzy logic The method of the series, that is, the use of fuzzy set theory to analyze the nature of land use in the planning year of the cell, applies the fuzzy set theory to the fuzzy inference of fuzzy propositions, and expresses the fuzzy conditional sentence by the fuzzy relation. This transforms the process of reasoning judgment into the synthesis of membership degree. In the calculation process, it is pointed out that Aw(x)=(1-w)V(4) and Aw(x)=A(x)w are both implication forms. The former implication form is used to assign weights to propositions, but the method is very To a large extent, the role of subordination is masked, and the need to weight the reasoning of inference cannot be satisfied. This paper adopts the latter implication form for propositional weighting, ie Aw(x)=A(x)w for the conjunction (logical intersection) proposition, and for the disjunction (logical concurrency) proposition, (Eu)=1- (1-A(x))' where 0, the left side of the equation w denotes the weight, the right side of the equation w denotes the power of the square, and when A(x), 0, 1 they all degenerate into a binary proposition when using a conjunction When the proposition is expressed, a new decision function considering the premise weighting is: a value in the 01 range means that 0 means not at all, and that the property and strength of the same place are among them Am is the vague term in the domain U1, and so on Ai is the fuzzy term in the domain Un. Bi is the fuzzy term in the domain V. Wj=n For the ith rule in the MISO system, the fuzzy relationship Ri can be used as: For multi-rule fuzzy inference, rules can be used. Inferring separately, then the cumulative fuzzy relationship/proposition of m propositions is expressed as: The final reasoning result can be expressed as: From the above m conditional propositions it can be seen that if each 1,2,...,n) the number of basic elements is g, and the number of basic elements of the domain V is h, then the universe U will have gn combinations of bases, Relation UXV will have gnh combinatorial bases If each basic set U/ and V has 10 elements each, then the final relation uxv will have 10+1 combinations. Therefore, the number of fuzzy relation rules to be established will be huge. , so there will be "dimensional disasters"
The phenomenon, however, can reduce the amount of computation in the reasoning process by choosing the appropriate elements of U1, U2,...,Un. If the i-th proposition is represented by a disjunctive proposition, then there are: Using the above fuzzy inference framework can be completed with Inference of the inaccurate language description system 1.2 Clear decision-making method Based on the above premise-weighted fuzzy inference, the result is a fuzzy quantity, which cannot be directly used for decision-making, but also takes a reasonable method to convert the fuzzy quantity into Accurate amount. The square of the membership function can be regarded as a new weight. This paper establishes yi to represent the discrete value between the inference results of the ith rule; -B.Cy') expresses the degree of membership of the corresponding discrete point y'; The decision-making 2 Residential land analysis principle The change of the time and space of the land use of the community is closely related to the evaluation of the advantages and disadvantages of the various land users in the city and the results of the adaptability evaluation. The evaluation of the advantages and disadvantages refers to various types of Land users' basic evaluation of the common parts of the land requirements; adaptability evaluation refers to specific targeted evaluations of various land users with their unique land use requirements.
The analysis of residential land use in space power load forecasting refers to the fact that on the one hand, according to the requirements of different load categories on the use conditions of the community, the empty space to be developed during the planning period is ranked and adaptively evaluated to determine that each development area is to be developed. The order and intensity of development, in turn, determine the time and spatial location of power users; on the other hand, it is necessary to analyze the existing land in the city’s current land use layout by evaluating the current land use status of the city and evaluating the status of the planned state. The problem of inefficient use, reasonably determining the time sequence and development intensity of land replacements that may occur in existing land (such as industries with high renting capacity to replace industries with low renting capacity) and renewal (such as large commercial buildings replacing small commercial buildings), In order to provide a reliable theoretical basis for determining the nature and development trend of the power load, provide a logical reasoning for the community's land use fuzzy decision-making - clarity - planning year ~ land adaptability evaluation logic reasoning - clarity 4 - land Iff evaluation better prices The fuzzy reasoning principle of the nature and intensity of urban lands Fig.1 Schematicd Iagramoffuzzyreasoning 3 Criteria and methods for the development of a new user in the development of a residential plot 3.1 Fuzzy residential land classification Adaptability evaluation matrix establishment Assuming that a functional community has N pieces of land to be developed, there are L types of land that can be developed. Each type of land can be divided into several levels (for example, 10 levels) according to the degree of adaptability. According to the definition of the membership function, the degree of adaptability can be land use, and 1 can indicate fully suitable land, which is divided into 10 sub-ranges. Each sub-interval indicates the corresponding adaptability level. Due to different land use properties, the adaptability evaluation criteria are different. Therefore, an adaptability evaluation matrix can be obtained relative to the nature of different land use. For example, we can obtain the following NXL matrix. Said: where ei6 indicates the degree of superiority and inferiority, the larger the value it represents the higher the degree of preference for the land, so the earlier it is developed, the higher the intensity of the development of the land 3.4 The method of determining the strength of the development of residential land The evaluation of the superiority and inferiority grades and the adaptive evaluation (Sij)(5) price for different land properties can be expressed by the following non-linear relationship: where s, j6 indicates the adaptability of the i-th land block to the land of the jth type. .
Criterion 1 For each type of land use property (such as j), the adaptability has a minimum threshold S′. If the result of a certain community's suitability evaluation for a certain type of land is less than this threshold, the land cannot be converted to this type of land. nature.
Corollary 1 For an empty parcel with zero load, if the results of adaptive evaluation for several types of land are less than the corresponding thresholds, the property of the parcel to maintain open space remains unchanged. 3.2 Guidelines for determining the nature of plots of land due to land to be developed When converting to the nature of different land uses, according to the law of value, the conversion goal should be the land type that can exert its greatest benefit. Because of the role of value, land use evaluations of the same grade with different land properties cannot have the same benefits from land use. Therefore, it is necessary to establish a grading map with equivalent benefits, as shown.
Level 3|4|5|6|7|8|9|3|4|5|6|I7|8|9|10|5|6|I Grade 7 | Level 8 | Level 9 | Level 1 | Level 2 | Level | Level 4 | Level 5 | Level 6 | Level 7 | Level 8 | Level 9 Rating scale for different land use classification criteria Criterion 2 If the suitability of several types of land The rating of the evaluation is equivalent (for example, C1, R, I2, S), and when the conversion to several types of land is likely to occur, the urban planning principle of “best use of land†is adopted to classify the land as renting capacity. The collection of high land properties such as land selection order is C*R*>S3.3. Criteria for Determining Time Sequence of Land Development Urban land development Starting from the role and benefits of land use, the earlier the land is used, the better it is. The higher the economic value and social benefits it produces, therefore, based on the analysis and judgment of multiple factors, deducing the order of the advantages and disadvantages of the plots in each plot, we can determine the time sequence and development of land use for each plot based on the amount of land demand. The most likely speed.
Assume that based on the multi-factor fuzzy logic relationship, the numerical representation between the available and the bad grade evaluation matrix elements available for each development area is inferred, and the evaluation results of the strengths and weaknesses are as follows: From the reality of planning, the same type of land is used for development. Intensity can also be divided into several levels (for example, 10 levels), and these levels can still use the value of the interval to indicate that the value corresponding to the development intensity of the community is evaluated by the advantages and disadvantages of the residential land and the adaptability to a certain type of land. To determine, the method of linear weighting can be approximated, that is, the weights W1 and W2 of the advantages and disadvantages of the land and the suitability of various types of land are respectively determined, and the following conditions are met: the load intensity level of the land to be developed and development can be determined. speed.
4 Rules and Methods for Land Substitution and Renewal Prediction of Residential Areas 4.1 Fuzzy building of land use adaptation evaluation matrix In the land land replacement and rejuvenation prediction, it is necessary to separately establish the current land use adaptation evaluation matrix Sc and the planning annual land use adaptation evaluation matrix So. The form of the adaptive matrix is ​​the same as the form of the space development adaptive evaluation matrix.
4.2 Determining the rules for the replacement or renewal of residential lands The assumption that the land suitability evaluation value can be converted to reflect the value of the land, that is, the value of the land can be represented by a suitable adaptive value, and the current value of the land is assumed to be Lu (x, y,t) (this value is mainly given by real estate professionals with rich experience in evaluation), the current value of the land is evaluated as Lv(x,j,t), and the planned value of the land is LP(x,y,t+1) ), where x and j indicate the position coordinates of the plot, t indicates the start time of the evaluation, and t+1 indicates that the current value of the land is higher than (equal to) the current value of the land and the land value of the planning year in the planned target year. The land will also maintain the original state of land use (">" corresponds to an over-development state, "=" corresponds to a stable equilibrium state). The current utilization value of the land is lower than the land value of its planning year, but higher than The current value of the land, the initial state of the land will remain unchanged, and its land use characteristics may change in the middle of the year. The current value of the land is less than its current value and land value in the planning year. Therefore, the characteristics of its land are most likely to change.
0 represents the threshold of land update at time t. When AL>L(1), the land C*, where C* is the set of land where land use change may occur (whether land replacement occurs or not depends on the land demand and other requirements The order of merits of the plots L(t) will be determined approximately according to the actual planned land appropriation 4.3 Method for determining the time of land replacement or renewal of the community The method for determining the time of land replacement or renewal of the plot is the same as the determination of the time sequence of development of the vacant land. 4.4 Methods for Determining the Land Replacement or Renewal Strength of Residential Areas The method for determining the land substituting or updating intensity of a residential area is the same as the method for determining the strength of an open land development.
5 Conclusions This paper proposes a new method of approximate reasoning and decision-making based on fuzzy logic, taking into account the weight of the importance of the premise, in case of incomplete knowledge. This method provides a powerful tool for the reasoning and decision-making of complex systems. The basic principles of urban land development and urban land redevelopment forecast in the year. This principle fully reflects the expert's reasoning and decision-making process, and provides a systematic guidance for the urban distribution network planners to complete the analysis task of the development time, the nature of the use of the land and the best use intensity under the condition of high urban land decomposing in the planning year. Theory This method is suitable for small-scale urban power distribution network planning of the spatial land load forecasting of the use of land in the analysis of the community in the sequel (part two) will be described with an example
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