Abstract: A controller for classifying devices of a building management system (BMS). The controller may be configured to obtain an entity name for a device, extract a core name from the entity name, compare the core name to candidate core names, determine scores for each comparison, identify a highest score, identify a class of a candidate core name, and classify the device in the class.

Abstract: A controller for classifying devices of a building management system (BMS). The controller may be configured to obtain an entity name for a device, extract a core name from the entity name, compare the core name to candidate core names, determine scores for each comparison, identify a highest score, identify a class of a candidate core name, and classify the device in the class.

Abstract: A controller for classifying devices of a building management system (BMS). The controller may be configured to obtain an entity name for a device, extract a core name from the entity name, compare the core name to candidate core names, determine scores for each comparison, identify a highest score, identify a class of a candidate core name, and classify the device in the class.

Abstract: A controller for classifying devices of a building management system (BMS). The controller may be configured to obtain an entity name for a device, extract a core name from the entity name, compare the core name to candidate core names, determine scores for each comparison, identify a highest score, identify a class of a candidate core name, and classify the device in the class.

Abstract: A system and approach for diagnostic visualizations of, for example, building control systems data. A focus may be on a similarity metric for comparing operations among sites relative to energy consumption. Normalizing factors may be used across sites with varying equipment consumption levels to be compared automatically. There may also be a high level overview of an enterprise of sites. For instance, consumption totals of the sites may be normalized by site size and length of time of a billing period to identify such things as outlier sites. One may use a main view of geographic distribution dynamically linked to subviews showing distribution by size, by aggregated climate, and so on. With these views, one may quickly drill through the enterprise and identify sites of interest for further investigation. A key metric may be intensity which invokes viewing virtually all sites by normalized consumption for a unit amount of time.

Abstract: A system and approach for diagnostic visualizations of, for example, building control systems data. A focus may be on a similarity metric for comparing operations among sites relative to energy consumption. Normalizing factors may be used across sites with varying equipment consumption levels to be compared automatically. There may also be a high level overview of an enterprise of sites. For instance, consumption totals of the sites may be normalized by site size and length of time of a billing period to identify such things as outlier sites. One may use a main view of geographic distribution dynamically linked to subviews showing distribution by size, by aggregated climate, and so on. With these views, one may quickly drill through the enterprise and identify sites of interest for further investigation. A key metric may be intensity which invokes viewing virtually all sites by normalized consumption for a unit amount of time.

Abstract: A system and approach for diagnostic visualizations of, for example, building control systems data. A focus may be on a similarity metric for comparing operations among sites relative to energy consumption. Normalizing factors may be used across sites with varying equipment consumption levels to be compared automatically. There may also be a high level overview of an enterprise of sites. For instance, consumption totals of the sites may be normalized by site size and length of time of a billing period to identify such things as outlier sites. One may use a main view of geographic distribution dynamically linked to subviews showing distribution by size, by aggregated climate, and so on. With these views, one may quickly drill through the enterprise and identify sites of interest for further investigation. A key metric may be intensity which invokes viewing virtually all sites by normalized consumption for a unit amount of time.

Abstract: A system and approach for diagnostic visualizations of, for example, building control systems data. A focus may be on a similarity metric for comparing operations among sites relative to energy consumption. Normalizing factors may be used across sites with varying equipment consumption levels to be compared automatically. There may also be a high level overview of an enterprise of sites. For instance, consumption totals of the sites may be normalized by site size and length of time of a billing period to identify such things as outlier sites. One may use a main view of geographic distribution dynamically linked to subviews showing distribution by size, by aggregated climate, and so on. With these views, one may quickly drill through the enterprise and identify sites of interest for further investigation. A key metric may be intensity which invokes viewing virtually all sites by normalized consumption for a unit amount of time.