Design And Implementation Of Base Station Air Conditioning Operation And Maintenance And Energy Dispatching Management Platform Based On Mechanical Learning

The energy saving of communication base stations is of great significance to the telecommunications industry.In the future,the impact of mobile communication network energy consumption will be more serious,because the upcoming 5G network will have more traffic load.Reducing the energy consumption of mobile communication networks has caused great concern.Under the premise of ensuring the normal operation of communication equipment and communication facilities,it is necessary to analyze the energy-saving of base station air conditioners.Thesis paper takes the base station air conditioning operation and maintenance strategy and the energy consumption scheduling platform as the research object,analyzes the energy consumption mode of the base station air conditioner through data mining and other means,adopts different operation and maintenance strategies for different modes,and predicts the energy consumption of the base station air conditioning.,proposed to use energy scheduling.It aims to build a base station air conditioning operation and maintenance and energy management platform.Thesis paper first analyzes the quality characteristics of air conditioning data in the platform.According to the quality characteristics of the data,the data is preprocessed by the theoretical method of mechanical learning,and a preprocessing system suitable for base station air conditioning data is proposed.The significance is to provide reliable data for the next data analysis and reduce its complexity.Secondly,using decision tree,K-means,etc.to identify the energy consumption mode of the base station air conditioning system,and using genetic algorithm to optimize K-means,the comparison analysis is carried out to verify the effectiveness of the algorithm.Thirdly,the EMD-AGA-LSTM prediction model is proposed to predict the energy consumption of base station air conditioners in a short period of time.By analyzing the influence of the input mode on the prediction model,the optimal input mode is found.By analyzing the effectiveness of the EMD empirical mode decomposition,the validity of the model decomposition to the prediction model is verified.By comparing the proposed combination of models and comparing with the commonly used prediction models,the effectiveness of the air conditioning energy consumption prediction method proposed in thesis paper is verified.Finally,applying the above theory and method,design and implement the base station air conditioning operation and maintenance strategy and energy consumption scheduling management platform.