Method for forecasting mid-long-term electricity consumption in combination with population index

Abstract

The invention provides a method for forecasting mid-long-term electricity consumption in combination with a population index. The method is characterized by comprising the steps of firstly, obtaining electricity consumption of each user and a resident population number in each of past k years in a to-be-forecast region; secondly, calculating a per-capita electricity consumption forecasting value by considering a per-capita electricity consumption increment coefficient; thirdly, calculating a resident population number forecasting value; fourthly, multiplying the per-capita electricity consumption forecasting value by the resident population number forecasting value to obtain a forecasting value of the sum of electricity consumption of residential users and commercial users; fifthly, calculating electricity consumption forecasting values of industrial users, electricity consumption forecasting values of non industrial users and electricity consumption forecasting values of other users; and finally, calculating a total electricity forecasting value. The forecasting method is simple in algorithm, can reduce data demand amount, is high in operability, effectively grasps key points of electricity consumption forecasting for different electricity users, is high in forecasting result precision, and can provide marketing decision support for power supply enterprises.

Description

technical field [0001] The invention relates to the technical field of power consumption forecasting, and more specifically, relates to a medium- and long-term power consumption forecasting method combined with population indicators. Background technique [0002] Affected by factors such as economic transformation, industrial structure adjustment, and frequent population migration, the total power consumption of the power grid has also changed accordingly. Power forecasting is a basic task in the electricity market. Combining the characteristics of electricity consumption in the industry to correctly predict power consumption and provide marketing decision support for power supply companies is very important for the safe and economic operation of the power grid and the construction and development of the power market. significance. [0003] At present, according to the type of electricity price and load characteristics in my country, power users are divided into five catego...

AI Technical Summary

Problems solved by technology

At present, the electricity consumption forecasting method mainly has the following two problems. One is to pursue the fitting accuracy of the original data of the total power grid, and the other is to pursue a complex algorithm based on the premise of large-capacity samples, while ignoring the impact of population changes.

In the power forecasting work, due to the large impact of population factors on the electricity consumption of residential users and commercial users, the electricity consumption of these two types of users may be different from that of the three types of users: industrial users, non-industrial users, and other users. There is a big difference in the development law of electricity consumption in China. If you blindly pursue the fitting accuracy of data, the generalization of the prediction method will be reduced; if you pursue complex algorithms too much, it will reduce the operability due to the large amount of data demand.

The above problems are not conducive to power supply companies' forecasting of electricity consumption in various regions and improving the accuracy of prediction results; therefore, it is necessary to start from new ideas and adopt new methods for medium and long-term electricity consumption forecasting

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