Training method and device of load prediction model, readable medium and electronic equipment

Abstract

The invention discloses a training method and device of a load prediction model, a readable medium and electronic equipment, and relates to the field of computers, and the method comprises the steps:obtaining training data features of a plurality of historical load prediction tasks and a selected historical algorithm; taking the training data characteristics of each historical load prediction task and the selected historical algorithm as one piece of training data, and performing machine learning according to each piece of training data to obtain an algorithm selection model; obtaining a plurality of pieces of sample load data of the current load prediction task, and extracting current data characteristics of each piece of sample load data; predicting a current algorithm matched with thecurrent data feature from all historical algorithms through an algorithm selection model; obtaining at least one hyper-parameter corresponding to the current algorithm; and according to the current algorithm, the at least one hyper-parameter and each sample load data, training to obtain a current load prediction model. According to the technical scheme, the calculation time can be shortened, the calculation resources are saved, and the manual intervention degree is reduced.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a training method, device, readable medium and electronic equipment of a load forecasting model. Background technique [0002] In order to realize the regulation of production capacity equipment in combination with actual production demand to save energy, it is often necessary to perform load forecasting tasks, that is, to train a corresponding load forecasting model for each load forecasting task, so that the load forecasting model obtained through training can subsequently predict the capacity of production capacity equipment Loads (eg, electrical loads, heating loads, cooling loads). [0003] At present, there are a large number of algorithms that can be applied to the load forecasting task. When there is a new load forecasting task and the corresponding load forecasting model needs to be trained for it, it is necessary to apply all the algorithms that can be applied to the ...

AI Technical Summary

Problems solved by technology

[0004] When implementing the training load forecasting model through the above method, it is necessary to test all the algorithms that can be applied to the load forecasting task on the sample load data of the new load forecasting task, thus spending more computing time and computing resources, and the degree of manual intervention higher

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