The process of urbanization, Beijing faces a number of environmental problems. Especially in the last few years, there has been constantly sustained fog and haze, which makes the implementation of electric heating in rural areas gradually on the agenda. After the implementation of electric heating, the irrationality of the regional power grid layout will be further amplified. With the rapid growth of electricity load in the planning area, the requirement of the power supply grid reliability and power quality is also increasing. Therefore, on the basis of power load forecasting in consideration of the the electric heating, and based on the particularity of rural power network, we achieve the Huairou District substation site with the use of improved genetic algorithm. The results can provide reference for the selection of substation of the whole Beijing rural area.
The article uses the cosine function and Euclidean distance function to optimize the traditional GM (1, 1) model. The cosine function processing the raw data columns, which could enhance the smoothness of the original series, and strengthen the general trend of the original series as well, effectively eliminate the shock interference of disturbed system. The principle of minimum Euclidean distance used to optimize the response function to determine the constants C, which could make better use of the original information. The improved model is applied to the tertiary industry in Beijing load forecasting, the results show that the improved model has higher prediction accuracy, and the improved model used to predict the next three years’ electricity consumption of tertiary industry in Beijing.
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