Space power load forecasting, also known as cell load forecasting, was first proposed by Wills of the United States in 1983. It is defined as the city's land use according to the planned urban grid voltage level in the future power supply range of the power sector. The principle is divided into (grid) or irregular cells of the corresponding size (as small as 0.01km2), and the power users and corresponding powers in the corresponding community are further predicted by analyzing and predicting the characteristics and development laws of the planned urban residential land use. The location, quantity and time of the load distribution to IJ So far, in the field of space power load forecasting, there are mainly three methods for analyzing the land use of the community at home and abroad: 1 Analytical method, that is, establishing a detailed community land use model. The process of changing the use of land in the simulated community, the shortcoming of the model is that it is difficult to deal with the problem of urban reconstruction, so it does not have universal significance. The 2 frequency domain analysis method, that is, the input of each community to various types of loads by experts or planners. Numerical value, directly determine the number of new power users in the future; 3 based on fuzzy logic The method of using the fuzzy set theory to analyze the land use property of the community planning year. When applying fuzzy set theory to the fuzzy proposition fuzzy reasoning, the fuzzy conditional sentence is expressed by fuzzy relation, so that the judgment process of reasoning is transformed into the synthesis of membership degree. In the process of calculus, it is pointed out that Aw(x)=(1-w)V(4) and Aw(x)=A(x)w are all forms of implication. The former implication form is used to authorize the proposition, but the method is very To a large extent, it obscures the role of membership and cannot satisfy the need to weight the premise. In this paper, the latter implication form is used for propositional weighting, that is, Aw(x)=A(x)w for the conjunction (logical intersection) proposition, and for the disjunction (logically) proposition, (especially)=1- (1-A(x))' where 0, the left side of the equation w represents the weight, and the right side of the equation w represents the power. Obviously, when A(x), 0, 1, they all degenerate into a binary proposition. When the proposition is expressed, a new decision function is considered to consider the premise weighting: the value in the 01 interval indicates that 0 means completely no, the same in nature and intensity, where Am is the fuzzy term in the domain U1, and so on. Ai is the fuzzy term in the domain Un, Bi is the fuzzy term Wj=n in the domain V. For the i-th rule in the MISO system, the fuzzy relation Ri can be expressed as: for multi-rule fuzzy reasoning, each rule can be adopted Separate reasoning, then the cumulative fuzzy relationship of m propositions / expressed as: the final reasoning result can be expressed as: from the above m conditional propositions, if the number of basic elements of each 1, 2, ..., n) is g, and the number of basic elements of the domain V is h, then the domain U will have gn combination bases, and the modulus The relationship UXV will have gnh combination bases. If there are 10 elements in each basic set U/ and V, then the final relationship uxv will have 10+ 1 combinations. Therefore, the number of fuzzy relation rules that need to be established will be huge. Therefore, there will be "dimensional disasters"
Phenomenon However, by properly selecting the basic elements of U1, U2, ..., Un, the computational complexity of the inference process can be greatly reduced. If the i-th proposition is represented by a disjunction proposition, there is: using the above fuzzy inference framework to complete Inference of the inaccurate language description of the system 1.2 The clear decision method based on the above-mentioned premise-weighted fuzzy reasoning is a fuzzy quantity, so it can not be directly used for decision-making, but also a reasonable method to convert the fuzzy quantity into The exact amount. The square of the membership function can be regarded as a new weight. In this paper, yi is used to represent the discrete value of the inference result of the i-th rule; -B.Cy') represents the membership degree of the corresponding discrete point y'; * indicates clearness Decision 2 The principle of land use analysis The change of time and space of land use in the community is closely related to the evaluation of the advantages and disadvantages of the community and the evaluation results of the adaptability of the land users. The evaluation of the advantages and disadvantages refers to various types of The basic evaluation of the common parts of land requirements by land users; adaptive evaluation refers to the specific evaluation of various land users with their unique land use requirements.
The cell land use analysis in space power load forecasting refers to, on the one hand, according to the requirements of different load categories for the use conditions of the cell, and the ranking and adaptability evaluation of the open space to be developed during the planning period to determine that the cells to be developed are developed. The order and development intensity, in order to determine the time and space location generated by the power users; on the other hand, through the grade evaluation of the existing land use status of the city and the grade evaluation under the planning state, analyze the land existing in the urban current land layout Inefficient use, reasonable determination of the chronological and developmental strength of land replacement (such as high-capacity commercial substitution of low-capacity industries) and renewal (such as large commercial buildings replacing small commercial buildings) that may occur in existing land use, Therefore, it provides a reliable theoretical basis for determining the nature and development trend of power load, and provides the principle of logical reasoning for the fuzzy decision-making of community land use-clearization-planning year~land adaptability evaluation logic reasoning-clarification 4-landifice Fuzzy Reasoning Principles of Urban Land Nature and Intensity Fig.1Schematicd Iagramoffuzzyreasoning 3 criteria and methods for the development of a new user's space in the open space of the community 3. The establishment of an adaptive evaluation matrix for fuzzy land classifications assumes that a functional community has N blocks to be developed, and there are L types of land that can be developed. Each kind of land property can be divided into several grades according to the degree of adaptability (for example, 10 grades). According to the definition of membership function, the degree of adaptability can be used, and 1 indicates a completely suitable land, which is divided into 10 sub-intervals. Each sub-interval indicates that the corresponding adaptation level is different due to different land use properties. The criteria for adaptability evaluation are different. Therefore, an adaptive evaluation matrix relative to different land use properties can be obtained. For example, we can obtain the following NXL matrix. Representation: where ei6 indicates the degree of superiority and inferiority, the greater the value indicates the higher the preference for the plot, so the earlier it is developed, the higher the intensity of land used for development. The evaluation of superiority and inferiority and the evaluation of the adaptability (Sij)(5) for different land use properties can be expressed by the following nonlinear relationship: where s, j6 represent the adaptability of the i-th plot to the j-th land. .
For each type of land use property (such as class j), the criterion has a minimum threshold S'. If the result of a certain area's suitability evaluation for a certain type of land is less than this threshold, the land cannot be converted into such a land. nature.
Inference 1 For an empty plot with zero load, if the results of several types of land use assessment are less than the corresponding threshold, the land use property of the plot to keep the open space is unchanged. 3.2 The criteria for determining the land use property of the plot are due to the land to be developed. When transforming to different land use properties, according to the law of value, the conversion target should be the type of land that can exert its maximum benefit. Due to the role of value, the same level of land use evaluation of different land properties, the benefits of land use may not be the same, so it is necessary to establish a hierarchical map of equal benefits, as shown.
Level 3|4 Level|5 Level|6 Level|7 Level|8 Level|9 Level|3 Level|4 Level|5 Level|6 Level I7 Level|8 Level|9 Level|10 Level|5 Level|6 Level I 7 level|8 level|9 level|1) level|2 level|level|4 level|5 level|6 level|7 level|8 level|9 level different land use classification value evaluation chart criterion 2 if several types of land use adaptability If the level of evaluation is equivalent (for example, C1, R, I2, S), and the conversion to several types of land may occur, the land is classified as the renting ability based on the principle of “good land useâ€. The collection of high land use properties, such as the order of land selection, is C*R*>S3.3. The criteria for determining the time sequence of land development. The development of urban land is based on the role and benefits of land use, and the sooner the land is used. Therefore, the higher the economic value and social benefit generated by the multi-factor analysis and judgment, the inferior order of each plot of land, the time sequence and development of the land development of each plot can be determined according to the demand of the land. The most likely speed.
It is assumed that according to the multi-factor fuzzy logic relationship, the numerical values ​​of the available and inferior rating matrix elements of each cell to be developed are inferred, and the evaluation results of the advantages and disadvantages are as follows: from the actual situation of planning, the same type of land, its development The intensity can also be divided into several levels (for example, 10 levels). These levels can still be used to indicate that the value corresponding to the cell development intensity is the evaluation of the advantages and disadvantages of the residential land and the adaptability to a certain type of land. To determine, the linear weighting method can be approximated, that is, the weights W1 and W2 of the land superiority and inferiority and the suitability of various types of land are respectively determined, and the following conditions are met: the load intensity level and development of the land to be developed can be determined. speed.
Rules and methods for land replacement and renewal prediction of 4 communities 4.1 Establishment of fuzzy community land use evaluation matrix In the land replacement and renewal forecast, it is necessary to establish the current land use adaptability evaluation matrix Sc and the planned annual land use evaluation matrix So. The form of the adaptive matrix is ​​the same as the form of the open space development adaptive evaluation matrix.
4.2 The rules for determining the land replacement or renewal of the community assume that the land suitability evaluation value can reflect the value of the land after appropriate conversion, that is, the value of the land can be expressed by the appropriate fitness value, and assume that the current use value of the land is Lu (x, y,t) (this value is mainly based on the evaluation of experienced real estate professionals), 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 represent the position coordinates of the plot, t represents the start time of the evaluation, and t+1 indicates that the current use value of the land is higher than (equal to) the current value of the land and the land value of the planned year. The land will also maintain the original land use status (">" corresponds to the over-exploited state, "=" corresponds to the stable equilibrium state), then the current use value of the land is lower than the land value of its planned year, but higher than its The current status of the land, the initial state of the land will remain unchanged, and its land use characteristics may change in the middle year, then the current use value of the land is lower than its current value and the land value of the planning year. Therefore, its land use characteristics are most likely to change.
0 indicates the threshold for land renewal at time t, then when AL>L(1), the plot C*, where C* is the set of parcels where land use change may occur (whether land replacement occurs or not, according to the demand for optimized land and other The order of the land parcels) L(t) will be determined according to the actual planned land release amount. 4.3 Method for determining the land replacement or renewal time of the community. The method for determining the land replacement or renewal time of the plot and the determination of the time sequence of the open space development. 4.4 Method for determining the land replacement or renewal intensity of a community The method for determining the land replacement or renewal intensity of a community is the same as the method for determining the strength of the open space development.
5 Conclusions This paper proposes a new method based on fuzzy logic based on fuzzy logic in the case of incomplete knowledge, which provides a powerful tool for the reasoning and decision making of complex systems. The basic principles of urban land development and urban land reform prediction. This principle fully reflects the expert's reasoning and decision-making process, and provides a systematic guidance for urban distribution network planners to complete the analysis task of plotting land development time, use nature and optimal use intensity under the condition of high urban decomposition of urban land. Theory This paper is suitable for the analysis of the residential land use of space power load forecasting for medium and short-term urban distribution network planning. An example will be used in the sequel (Part 2).
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