All-weather 96-point daily load curve prediction and optimization correction system

Abstract

An all-weather 96-point daily load curve prediction and optimization correction system. Historical tracking load samples and multi-weather samples are obtained to determine weight of weather of each city and calculate comprehensive meteorological factors; different data types of meteorological load data are sorted and screened, and a load extreme value inflection point prediction model suitable for different data types is established; an analogous meteorological daily load curve discrimination function is established, an analogous load curve is discriminated through similarity of predicted weather and historical weather, and combined with the extreme value inflection point prediction model, a daily load curve prediction model for different date types is established; a data type distinguishing module and a weather identification module are used to automatically judge the type and weather condition of the day to be predicted and an optimal model is selected to perform prediction; and a daily load typical curve database is built, a prediction curve obtained by calculation is corrected, a predicted value and curve are stored and a result is output, thereby realizing automation of short-term load curve prediction, improving load prediction accuracy, and realizing fine management of a power grid load.

Description

technical field [0001] The invention belongs to the field of medium and short-term load forecasting in electric power systems, and in particular relates to an all-weather 96-point daily load curve forecasting and optimization correction system. Background technique [0002] The daily load curve prediction of the power system is one of the important tasks of power grid dispatching. It is an important aspect to ensure the safe and economic operation of the power system and the scientific management of the power grid. The orderly progress is of great significance. The research shows that the loads of different date types have their unique load characteristics, and also have different technical difficulties. With the effective accumulation and scientific processing of historical data of the power system, and the cross-infiltration of power load forecasting technology and related scientific fields (such as meteorology, economics, etc.), the factors that need to be considered in ...

AI Technical Summary

Problems solved by technology

The cluster analysis of meteorological-load data according to date type shows that the main difficulties currently exist are: the working day load is greatly affected by meteorological hysteresis and cumulative effect, and the existence of thermal cumulative effect during high temperature periods in summer often leads to abnormal load peaks Higher; holiday load forecasting generally requires many days in advance and continuous forecasting

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