Research on the prediction method of electricity consumption for air conditioners in college dormitories for safety
ZHANG Yunlei
LI Ziang
MA Xiangyao
LI Dongyan
Abstract：In recent years,with the popularity of air conditioning in university dormitories,the electricity con-sumption of student dormitories has gradually increased.As the primary component of electricity consumption,analyzing the electricity consumption characteristics of air conditioning and predicting the electricity consump-tion can help with electricity safety management and dormitory wiring planning.In addition,it can also help students to plan the use of air conditioning rationally,prevent safety risks caused by circuit overload.We pro-pose a method for short-term electricity consumption prediction in college dormitory air conditioning based on Bi-LSTM recurrent neural networks.By collecting data on electricity consumption of air conditioning in uni-versity dormitories and establishing a dataset for short-term electricity consumption prediction,we use a Bi-LSTM network to extract the temporal information of electricity consumption samples,while incorporating regu-larization into the network to avoid network degradation problems.Based on this,we use our model to make predictions of electricity consumption for the next day.Experimental results show that the proposed model has a clear improvement in prediction accuracy and stability compared to traditional prediction methods.We first preprocessed and extracted features from the dataset,including cleaning the data,clustering,normalizing the data,and selecting features.Then,we trained various prediction models with the preprocessed data and com-pared them.Finally,we chose the Bi-LSTM recurrent neural network model to predict short-term electricity consumption.In practical applications,this method can help dormitory management departments in universities to predict the future electricity consumption of air conditioning for the next day more accurately.In turn,the administrators adjust equipment running status and rationalize the planning of electricity use to a-chieve energy conservation,emission reduction and optimization of equipment utilization efficiency.
Keywords：safety managementelectricity consumption predictionrecurrent neural networksuniversity dor-mitories
Publication Date：2023-12-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 105-114 )