Urban complex electricity consumption prediction method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides an urban complex electricity consumption prediction method and device, electronic equipment and a storage medium. The method comprises the steps of acquiring historical hour electricity consumption sequences of various types of electricity consumption entities in an urban complex; carrying out splitting and dimensionality reduction on the historical hour power consumption sequence according to preset calendar tags, and obtaining a first power consumption sequence for each calendar tag; carrying out nonlinear dimension reduction on the first electricityconsumption sequence in a time dimension to obtain a second electricity consumption sequence; inputting the second power consumption sequence into a preset prediction neural network for prediction toobtain a first power consumption prediction result based on the calendar label; and based on the first power consumption prediction result, calculating to obtain a power consumption prediction resultof the complex. And the prediction result of the electricity consumption of the urban complex can be more accurate.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a method, device, electronic equipment and storage medium for predicting electricity consumption of urban complexes. Background technique [0002] The so-called "urban complex" is a building group-based integration of the five core functions of business offices, hotels and catering, commercial sales, apartment houses, and cultural and entertainment complexes in geographical space, and the establishment of a mutual relationship between the various parts. The interdependence and active relationship finally formed a multifunctional and efficient "city within a city". The complex load structure and huge spatial scale make the urban complex a kind of large-scale electricity consumer in the urban power grid. As far as the urban complex itself is concerned, accurate power forecasting will help complex users to arrange energy consumption reasonably and flexibly, so as to achieve the purpo...

AI Technical Summary

Problems solved by technology

This method has certain limitations: firstly, it is impossible to accurately grasp the characteristics of each load component in the city complex as a whole by directly predicting the monthly electricity consumption; secondly, the data used to construct a single-step forecasting Low historical monthly data, the amount of this type of data is small, and the number of samples that can be used to train the model is limited, which increases the risk of over-fitting the prediction model, and ultimately makes the prediction accuracy low; finally, the load in the urban complex The composition is complex, the electricity consumption behavior is highly volatile, and the predictability of the electricity consumption sequence is low, resulting in low prediction accuracy

Therefore, the existing forecasting methods for electricity consumption in urban complexes have the problem of low forecasting accuracy.

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