Spotting Trends: Difference between revisions
From PLC Wiki
No edit summary |
No edit summary |
||
| Line 1: | Line 1: | ||
------ | ------ | ||
Spotting trends is one of the most valuable uses of data visualization within '''AQue Lite'''. By tracking parameters over time, users can identify gradual changes or consistent patterns in system performance. | Spotting trends is one of the most valuable uses of data visualization within <big>'''AQue Lite'''</big>. By tracking parameters over time, users can identify gradual changes or consistent patterns in system performance. Thus, for instance, from the history of operation of cooling systems, it would be detected that the amount of energy used by the equipment gradually grows with time, while it would steadily enhance with efficiency following some optimisation initiative. | ||
------ | ------ | ||
| Line 7: | Line 7: | ||
'''Time-Based Insights:''' | '''Time-Based Insights:''' | ||
In trends, users are able to analyze how one variable can change over time, for example, daily, weekly, monthly, or yearly. This helps describe both long-term and short-term changes. | |||
---- | ---- | ||
| Line 13: | Line 13: | ||
'''Predicting Future Behavior:''' | '''Predicting Future Behavior:''' | ||
Observing trends can help predict future system performance based on historical data. For | Observing trends can help predict future system performance based on historical data. For instance, if a cooling system has been consistently performing for months, users can expect that it is going to continue doing so unless a trend indicates otherwise. | ||
---- | ---- | ||
| Line 19: | Line 19: | ||
'''Anomaly Detection:''' | '''Anomaly Detection:''' | ||
Us prosers can detect anomalies or deviations from the expected performance by comparing current data with historical trends. Thiactive approach helps prevent issues before they become critical. | |||
By spotting trends, businesses can optimize resource allocation, avoid inefficiencies, and plan maintenance schedules based on historical performance, ensuring that systems run smoothly and efficiently. | By spotting trends, businesses can optimize resource allocation, avoid inefficiencies, and plan maintenance schedules based on historical performance, ensuring that systems run smoothly and efficiently. | ||
Revision as of 07:26, 19 November 2024
Spotting trends is one of the most valuable uses of data visualization within AQue Lite. By tracking parameters over time, users can identify gradual changes or consistent patterns in system performance. Thus, for instance, from the history of operation of cooling systems, it would be detected that the amount of energy used by the equipment gradually grows with time, while it would steadily enhance with efficiency following some optimisation initiative.
Time-Based Insights:
In trends, users are able to analyze how one variable can change over time, for example, daily, weekly, monthly, or yearly. This helps describe both long-term and short-term changes.
Predicting Future Behavior:
Observing trends can help predict future system performance based on historical data. For instance, if a cooling system has been consistently performing for months, users can expect that it is going to continue doing so unless a trend indicates otherwise.
Anomaly Detection:
Us prosers can detect anomalies or deviations from the expected performance by comparing current data with historical trends. Thiactive approach helps prevent issues before they become critical.
By spotting trends, businesses can optimize resource allocation, avoid inefficiencies, and plan maintenance schedules based on historical performance, ensuring that systems run smoothly and efficiently.