AI-based Office Building Energy Consumption Analysis And Control System

At present,the total energy consumption of office buildings in China has increased rapidly.However,the energy management mode is extensive and energy waste is serious.In an environment where energy is increasingly scarce,how to save energy and use energy efficiently becomes an important task.Intelligent analysis and control of office building energy consumption has also become an important research topic.In view of the serious waste of energy consumption in traditional Chinese office buildings,this topic combines the characteristics of office building energy consumption data,using artificial intelligence,visualization charts,network communication and other related technologies to establish an office building energy analysis and control system.It mainly includes energy consumption prediction model,energy consumption status evaluation method,intelligent control method,energy consumption database and visualization platform.Office management staff can view real-time energy consumption,historical energy consumption,predicted energy consumption,and abnormal energy consumption,improving the efficiency and management level of office management personnel.The main contents of this design are:1.Design and implement the office building energy consumption database and related data tables.A protocol for transmitting energy consumption information between the system platform and the processor is designed,and the energy consumption data is received and the energy consumption data is stored in the database.2.A method for evaluating the actual energy consumption state based on the predicted energy consumption is designed.The energy consumption status is analyzed abnormally.The intelligent control function of the energy consumption equipment is realized according to the energy consumption evaluation result and the equipment control mode.3.Use SSM framework to establish a system platform,use Echarts chart to display the real-time and historical energy consumption of office buildings,predict energy consumption,and facilitate administrators to grasp the energy consumption of office buildings through the system platform.4.The influencing factors of office building energy consumption are analyzed.An artificial learning deep learning algorithm is used to establish an RNN model with LSTM neurons.The LSTM-RNN model can learn historical energy consumption data of office buildings to predict the energy consumption of office buildings in the future.5.Select an office building energy consumption information from the Buildings Datasets open energy dataset,and optimize the model parameters to a set of better values by debugging the model parameters.6.Comparing the performance of the LSTM-RNN model with the classical regression model and the three-layer BP network model in this design,by testing the energy consumption data of two office buildings,the experiment result shows that the LSTM-RNN model has higher prediction accuracy and robustness than BP network and the classical regression model.