Office building load prediction method based on particle swarm neural network

Abstract

The invention discloses an office building load prediction method based on a particle swarm neural network. The method includes the following steps of: determining the input feature variable and the output target vector of an office building load prediction neural network; initializing a particle swarm solution set; calculating the fitness value of each particle; updating the local optimal position and the global optimal position of each particle; updating speeds and positions of particles; judging ending conditions; is the ending conditions are met, outputting the current optimal position; assigning the neural network and simulating the neural network, and predicting the load of an office building. Through the office building load prediction method based on the neutral network, all internal disturbance and external disturbance factors influencing fluctuation of the official building load are comprehensively considered. Meanwhile, aiming at the special periodic electricity consumption characteristic of the office building, the periodic load change is also considered; the high-precision load prediction of the office building is achieved by using manually simulating the neutral network; the office building load prediction method based on the particle swarm neural network has the advantages of high load prediction precision and simple and easy to implement.

Description

technical field [0001] The invention relates to a load forecasting method, in particular to a particle swarm neural network-based office building load forecasting method. Background technique [0002] In the case of unbalanced supply and demand of the power grid, failure of power supply nodes in some areas, and transmission congestion, the dispatch pressure of the power dispatching department increases, and the balance of supply and demand on the grid side cannot be maintained. On the other hand, the current smart grid is facing serious problems in regional development, resulting in a contradiction between the regional maximum load and equipment utilization, and this contradiction is caused by the rapid development of local local load and the low overall load level , In order to alleviate the contradiction, the whole line was cut off in the past, which had a great impact on the power consumption of the user side. More importantly, the current office building users have low ...

AI Technical Summary

Problems solved by technology

For ordinary technicians, this method is extremely complicated and not easy to implement

For the time series forecasting method, although the implementation process is relatively simple, because only the historical load data influencing factors of the building are considered, other important influencing factors such as outdoor average temperature, outdoor average humidity, and personnel changes are not considered, resulting in load forecasting problems. The accuracy is not up to the standard of high-precision load forecasting algorithm

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