A method and apparatus for power system thermal load forecasting

Abstract

The invention discloses a method and a device for forecasting the heat load of a power system. The method comprises the following steps: S1, preprocessing the historical data of the heat load of the power system; S2, obtaining a data day reference line according to the historical daily data after the preprocessing; S3, dividing the obtained data daily reference line into a plurality of time periods; S4: screening the historical day data, and calculating the trend similarity value of the selected historical day data and the data day datum line in each divided time period; 5, selecting the historical day data corresponding to the trend similarity value great than the preset reference value to form a similarity sequence matrix; S6: the similarity sequence matrix is inputted into the constructed limit learning machine ELM for training, and the prediction model is obtained, and the power system heat load prediction is carried out. The invention adopts the time sequence representation methodbased on trend segmentation, effectively retains the important change trend information in the time series of the heat load, and can more accurately predict the change trend of the heat load.

Description

Technical field [0001] The present invention relates to the technical field of data analysis, in particular to a method and device for predicting the thermal load of a power system. Background technique [0002] Time series are widely used in people's daily life and industrial production, such as real-time transaction data of funds or stocks, daily sales data of retail markets, sensor monitoring data of process industries, astronomical observation data, aerospace radar, satellite monitoring data, real-time Weather temperature and air quality index, etc. The industry has so far proposed many time series analysis methods, including similarity query methods, classification methods, clustering methods, prediction methods, and anomaly detection methods. Among them, many methods need to judge the similarity of time series. Therefore, time series similarity measurement methods have a wide range of application requirements in the industry. [0003] However, the existing power system ther...

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Problems solved by technology

[0003] However, the existing power system thermal load forecasting is based on a single weather factor to select similar trend algorithms without taking into account the factors that affect load changes in different time periods on the same load day

Thus affecting the accuracy of heat load forecasting

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