Tourist area resident household electricity prediction method

Abstract

The invention discloses a tourist area resident household electricity prediction method, characterized by comprising the steps of: step 1, collecting data: including target year per capita household electricity consumption C, target year permanent resident total population N, target year total floating population M, target year population distribution characteristics and a maximum load equivalent hour; step 2, calculating equivalent permanent residents N1; step 3, calculating a population imbalance coefficient sigma; step 4, correcting the maximum load equivalent hour; step 5, predicting required electric quantity An; and step 6, predicting a residential electricity consumption maximum load Pn.max. The method makes tourist area resident household electricity prediction more practical, does not depend on historic data, and is suitable for resident household electricity prediction of areas lacking historical data or having no reference values, especially for tourist area resident household electricity prediction.

Description

technical field [0001] The invention relates to a method for predicting domestic electricity consumption of residents in tourist areas. Background technique [0002] Power load forecasting is not only the basis of power system planning and design, but also an important part of it. The purpose of power load forecasting is to rationally arrange the construction scale of power sources and power grids. It is a basic work related to the national economy and people's livelihood. The scale and layout of power grid substations, the grid connection mode and the selection of transmission line conductor cross-sections are all closely related to power load forecasting. [0003] The load forecast value used in power system planning and design generally refers to the annual maximum load. The forecast period can be divided into long-term (15-30 years), medium-term (5-15 years) and short-term (less than 5 years) forecasts, and should be as far as possible It is consistent with the time lim...

AI Technical Summary

Problems solved by technology

[0005] (1) Lack of historical data for power load forecasting: For example, tourist resorts are either islands or mountains, and the power load is basically scattered local residents, so there is basically no historical data to rely on for load forecasting

[0006] (2) The development and construction of tourist areas often show the characteristics of leap-forward development, and the material and cultural living standards of the people show a step-wise improvement. Therefore, the historical data used for load forecasting cannot reflect the living consumption of residents in such areas The characteristic of electricity, that is, historical data has no reference value

However, the number of residents in tourist resorts is unbalanced in time, which will reduce the maximum load utilization hours of residents' electricity consumption.

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