Power load prediction method and system based on deep belief network

Abstract

The invention discloses a power load prediction method based on a deep belief network. The power load prediction method comprises the following steps: aggregating historical data of a power load by adopting a sparse self-encoding neural network; constructing a composite optimized deep belief network prediction model based on a restricted Boltzmann machine, wherein the deep belief network prediction model sequentially comprises a Gaussian-Bernoulli restricted Boltzmann machine, a Bernoulli-Bernoulli restricted Boltzmann machine and a linear regression output layer from input to output, carryingout pre-training by adopting an unsupervised training method, and then carrying out parameter fine tuning by adopting a BP algorithm added with impulse terms; and inputting the aggregated historicaldata into the deep belief network prediction model for prediction. The invention further discloses a power load prediction system based on the deep belief network. According to the method, the regularity of historical load data can be better mined, so that the prediction efficiency is improved, meanwhile, the influence of different factors can be fully considered, and the prediction precision is improved.

Description

technical field [0001] The present invention relates to a power load forecasting method and system, in particular to a power load forecasting method and system based on a deep belief network. Background technique [0002] At present, short-term load forecasting is the most important for the management and scheduling of the power system. It can provide data support for the power generation plan to determine the power generation plan that best meets the economic requirements, safety requirements, environmental natural requirements and equipment restrictions. , to ensure the economical and safe operation of the power system. At present, with the continuous development and improvement of the power system, higher requirements are put forward for the short-term load forecasting of the power grid. Although there are relatively mature traditional methods, the traditional methods generally have the problem of low prediction accuracy. Although the predicted results have certain refer...

AI Technical Summary

Problems solved by technology

However, in the existing technology, a single intelligent prediction method is difficult to deal with the challenges brought by multi-dimensional data to the prediction accuracy and efficiency, while the prediction method based on deep belief network can achieve better prediction by combining multiple data processing methods and intelligent algorithms. precision and efficiency

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