Abstract
With the rapid development of network technology, people are increasingly dependent on the internet. When BP neural network (BNN) performs simulation calculation, it has the advantages of fast training speed, high accuracy, and strong robustness and is widely used in large-scale public (LSP) building energy consumption (BEC) monitoring platforms (LPB). Therefore, the purpose of this paper to study the energy consumption monitoring platform of large public (LP) buildings is to better monitor the energy consumption of public buildings, so as to supplement or remedy at any time. This article mainly uses the data analysis method and the experimental method to carry on the relevant research and the system test to the BNN. The experimental results show that the monitoring system (MS) platform designed in this paper has real-time performance, and its time consumption is between 2 s and 3 s, and the data accords with theory and reality.
Highlights
BP neural network (BNN) can effectively improve office efficiency and reduce costs
Data Center Module. e data center receives the point data collected by the data collector and saves it in the energy consumption monitoring system (ECMS) database. e data center will back up the data collected by the data collector every hour and every day as a backup point and calculate the difference and energy consumption
For building energy consumption monitoring systems, the main users of building owners and government energy-saving department managers do not often visit the system for business processing, so the system load and pressure caused by this are not large. erefore, in this performance test, after the energy consumption data is sent from the data collector, it is transmitted to the system data server through the network. e server receives and stores the original data packet, and the process of verifying, analyzing, and storing the data packet is regarded as one time. e collection of energy consumption is time-consuming, and the performance test of the data processing capability of the system is carried out. e test is carried out by setting the number of monitoring collection points and
Summary
BNN can effectively improve office efficiency and reduce costs. At the same time, it can be combined with traditional manual management methods to achieve resource sharing, thereby greatly reducing the burden on staff and the waste of human resources. For the problem of high energy consumption in LP buildings, Li et al proposed a LP BEC prediction model combining principal component analysis and BNN [1]. Security and Communication Networks e main research content of this paper includes the basic knowledge of BNN, monitoring related technology, energy consumption analysis model, and the overall design of the MS. 2. Design of a Large-Scale Public Building Energy Consumption Monitoring Platform Based on BP Neural Network. Artificial neural network is a machine designed to simulate the functions of the human brain It can be implemented with electronic or optoelectronic components, or it can be simulated on a traditional computer with software. (3) Calculate the output of the mth neuron in each layer of the neural network:. BP neural network has the following main defects: slow learning speed; ease to fall into local minimum; no corresponding theoretical guidance for the selection of the number of network layers and neurons; limited network promotion capacity
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