OBEM gathers data sets from various sources with other customer-specific data, and normalizes them in a central location. The normalized data can then be analyzed to look for anomalies and potential problems, to identify opportunities for improvement or optimization. Effectiveness in storage space utilization is a result of normalization. Methodologies include:
- Data Cleansers
- Data Cleansers provide the user with an ability to see the anomalies with respect to the user-defined boundaries.
- Data Spike
- Data Spike displays a spike in demand and consumption providing the user with information to address the sudden increase and thereby maintaining the loads, avoiding penalties, for example, demand limiting.
- Out-of-Range Data
- Out-of-Range Data eliminates unwanted outliers with respect to the limits defined by user, for each point.
- Meter Roll Over Logic
- Meter Roll Over Logic addresses the scenario where meter consumption value is reset when it exceeds the maximum value for that device. The logic addresses the difference in calculations that occur when such meter resets occur.