Enterprise power consumption load prediction method based on K-means clustering RBF neural network

Abstract

The invention discloses an enterprise power consumption load prediction method based on a k-means clustering radial basis function (RBF) neural network. The method includes steps: historical load data acquisition, meteorological data acquisition, date discrimination, neural network prediction, error calculation and correction, load curve drawing, and prediction data export. A prediction result is obtained by employing the neural network via the historical load data and meteorological factor input quantity, and correction is realized via an error correction module. Based on the requirement control technology of load prediction, with the combination of an industrial enterprise production plan and the condition of power consumption load usage, the system performs requirement control via a built-in requirement curve node determination method at a control point before the load prediction value reaches the maximum requirement, whether unnecessary loads are removed is determined, the current most appropriate energy-saving scheme is automatically selected, the maximum requirement is controlled in advance, over-load operation and even tripping operation can be effectively avoided, and safety and energy-saving production is guaranteed.

Description

Technical field [0001] The invention belongs to the field of power load forecasting and control technology, and is specifically a short-term electricity load forecasting and intelligent demand control technology suitable for typical industrial enterprises. The load forecasting method is a neural network based on k-means clustering radial basis RBF function . Background technique [0002] Short-term power load forecasting uses historical data, combined with the characteristics and influencing factors of the forecasting system, to forecast the load data for the next 1-7 days. In the past, load forecasting was mostly done by the power grid planning department. For industrial enterprises, load forecasting played a positive role in energy management, energy conservation, emission reduction, and cost optimization. On the basis of short-term load forecasting, understand the recent future load development and changes, put forward demand-side management measures, and intelligently contro...

AI Technical Summary

Problems solved by technology

[0003] The traditional load forecasting methods mainly include trend extrapolation method, regression analysis method, time series method, etc. The simple fitting accuracy of the extrapolation method is low, and the regression analysis method does not consider the dynamic and nonlinear relationship between load and weather and other variables. relationship, while the time series method does not consider the impact of weather and other meteorological factors on the load

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