Research On Spatial Load Forecasting Based On The Improved Genetic Algorithm

With the development of the city, on the power supply capacity of proposed newrequirements, but the relationship between power grid construction and urban land isincreasingly tense. Forward-looking grid construction requirements can be planned inadvance and incorporated into the overall urban planning, protection of substation sites andline corridor. Electricity load forecasting higher requirements, not only need to have the exactamount of load forecasting, detailed spatial load forecasting is also very necessary.In this paper, the development of spatial load forecasting made a detailed study, masterresearch status spatial load forecasting. Describes the space work load cell division, loadcharacteristics and the load while the rate of development and other issues. And on the aboveproblem, this article will use the urban regulatory detailed planning of land for a minimumload forecasting unit. The existence of saturation plots load, intends to use the S-curve to fitthe predictions. Typical load curves superimposed on the load value, to avoid human factors.According factors that affect urban development, to establish land evaluation criteria byAHP. By expert judgment matrix evaluated. To determine the consistency of the value of theminimum objective function, genetic algorithm for optimal use of judgment matrix. Landevaluation model is more objective.Because of the increasingly urban planning norms, most cities have urban regulatorydetailed planning of land use of the city carried out a detailed plan. Nature has a constructionland use, building strength and volume rate control targets. By geographic informationsystem software, the city controlled detailed planning and power load data fusion in a unifiedsoftware platform to improve spatial analysis capabilities. According to the developmentparcels, the city has been used as development areas and undeveloped areas. Developed areaof land for the use of the S-curve fitting to predict. Undeveloped land evaluation model usingscore, score high value of land will be more likely to be developed. Undeveloped land loaddensity areas refer to other plots. And to balance the load between the predicted value and thetotal space load forecasting value.Finally, the use of GIS for spatial load forecasting, got the next load forecast results, and load density distribution. The numerical examples show that the method is effective. Hasengineering value.