The application of quality control charts for identifying changes in time-series home energy data

Abstract

Energy consumption in the home is heavily influenced by the occupants and the routines they adopt. Although these routines tend to be regarded as somewhat static in nature, more recent evidence from the social sciences suggests that patterns of consumption are actually more fluid and constantly evolve to accommodate the contingencies of everyday living. This makes detecting changes in patterns of activity and their impact on energy consumption difficult, particularly when these patterns are often invisible to the householder to begin with. Being able to identify when a change occurs, therefore, could be a powerful tool to establish the context of change and so to determine more appropriate corrective action to curb waste and create opportunities for greater flexibility in consumption. The growing adoption of smart meters and home energy monitoring provide a platform for numerical approaches, yet there is little work reported in the literature and none that have attempted to evaluate effectiveness of such methods applied to detect changes in behaviour using field monitoring data from family homes. This paper reports on the application of a Change Point Detection method based on statistical quality control charts applied to identify changes in activities a family home using typical monitoring data. The approach was found to be very effective, identifying 78% of the changes that occurred over a two-year period and hence the outlook for such methods is promising. The findings suggest that such techniques could significantly improve the quality of information provided in energy feedback and so could play a significant role in the pursuit of more efficient energy use in the home by adding value to monitoring systems and services.

Providing A Model for Predicting and Detecting Destructive Processes to Prevent the Production of Waste and Defective Products: A Data Mining Approach

“…The CEEMDAN decomposed the raw data with large fluctuations to enhance the prediction performance of XGBoost and 379 RF and analyzed the water quality of the Gales Creek site of Tualatin River in 380 Oregon, USA. Vivancos et al [18] presented application of a Change Point Detection method based on statistical quality control charts for changing in activities a family home by using typical monitoring data. This technique could enhance the the quality of information provided in energy feedback significantly.…”

Behavioral Change Prediction from Physiological Signals Using Deep Learned Features

“…In recent years, the detection and prediction of changes in time series data obtained from observations of a monitored system have become a relevant research topic in various fields [1][2][3]. In particular, change-point detection has attracted considerable interest in the medical and neurological fields, where the accurate determination of changes in physiological parameters is particularly critical [4,5].…”

Behavioral Change Prediction from Physiological Signals Using Deep Learned Features

“…In recent years, the detection and prediction of changes in time series data obtained from observations of a monitored system has become a relevant research topic in various fields [1,2,3]. In particular, change-point detection has attracted considerable interest in medical and neurological fields, where the accurate determination of changes in physiological parameters is particularly critical [4,5].…”