Our client, a utilities company (“Company”) responsible for building digital infrastructure of a smart city, uses smart metering systems to enable hyperconnectivity & data intelligence for city operations. The data from these smart meters (essentially IoT devices) is used for monitoring consumption patterns as well as billing. These operations rely heavily on the quality of data transmitted by IoT devices. However, such data often comes embedded with several quality issues, such as
- Missing data or data coming in at irregular intervals or abrupt gradient changes
- Out-of-range data, presence of synthetic data or unusual ‘noise’ patterns
- Lack of correlation with other parameters
- Nonconformance to sensor’s expected behavior (i.e., ‘fingerprint’)
In addition to the above, smart meters possess limited in-built capabilities to detect anomalies and provide alerts. Company’s existing platform was unable to mitigate these challenges, and it needed a new approach. This Company joined forces with Scry and trained Concentio® to create an end-to-end system that not only uncovers issues with IoT data but also improves the quality using Scry’s proprietary suite of advance analytics algorithms.
Our team of data scientists and subject matter experts (SMEs) first worked to understand the problem in depth. Next, Scry trained Concentio® on historical meter data to establish the expected behavior of these smart meters. Simultaneously, Concentio’s data preprocessing steps ensured that the data used in its advanced algorithms is of good quality. Once trained, this software enabled following capabilities:
- Augmenting missing data using algorithms such as interpolation, time series analysis, and resampling.
- Raising smart alerts in real time based on device’s deviation (anomaly) from its expected behavior (fingerprint).
- Managing alert severity and confidence level as a function of its magnitude & duration.
- Suppressing false device alerts by removing unwanted noise from the data.
- Enabling feedback loop for retraining the model and continuous improvements.
This solution proved its value by improving the data quality and enabling automated anomaly detection, thus helping the client leverage smart meters data for improving city operations.
- 99% – Suppression of false and repetitive alerts
- 90% – Accuracy in handling missing values
- 88% – Improvement in alert detection time
- 76% – Anomalies detected faster than the device