The Research And Application On Anomaly Monitoring Of The Heat Addition By The Data Analysis Of Time Series

In recent years,due to the rapid development in the fields of industry,finance,and network security,time-series data generated over time has been widely used in various industries.The security of data has received more and more attention,and the field of anomaly detection has also received close attention from researchers.Anomaly detection is a kind of data technology that can check the abnormal definition through data mining analysis.The most serious part of resource energy consumption is that of China's heating sector.Therefore,the abnormal monitoring applied to the heating field can provide favorable information for the heating system reasonably,and reduce the heating when the minimum heating requirement is reached.In this paper,the deviation-based anomaly monitoring method is mainly studied,and the abnormality is determined by comparing the forecasting regression model fitting value with the real value.A univariate anomaly monitoring method based on LSTM neural network model is proposed for natural factors affecting heating index,such as atmospherium temperature,atmospherium sunshine,and atmospherium wind speed.And an abnormal decision procedure is proposed for the monitored abnormal data values,which is an effective determining of the abnormal data.Multi-variable anomaly monitoring methods based on Pearson correlation coefficient(PCC)and LSTM neural network is proposed for multiple parameters of heating system operations,such as supply water temperature of boiler rooms,return water temperature of boiler rooms,and supply and return water pressure of boiler rooms.Compared with the long-term memory neural network and the improved PCC-LSTM network,the experimental results show that the prediction accuracy of the PCCC-LSTM model and the convergence speed of the model are much higher than the original one.By the above experiment,multivariate anomaly monitoring is done based on the PCC-LSTM model.Finally,in order to ensure the accuracy of multivariate anomaly monitoring,a comparison method based on PCC-LSTM multivariate anomaly detection and single-dimensional anomaly detection is proposed to ensure the accuracy of anomaly detection.