Abstract: The invention involves the automated testing of HVAC units using an energy management system. The automated HVAC test is performed to understand if one or more HVAC units are operational across one or more locations. If an HVAC unit is not operational, HVAC testing could be performed to understand which component or stage of the HVAC unit is not working as designed. The automated HVAC test is also used to calculate the efficiency of the HVAC unit(s) being tested. The various HVAC tests are performed on all HVAC units as a form of preventative maintenance and diagnostics. These tests can be scheduled on-demand, for a future date and time, or on a recurring schedule (monthly or quarterly). A report is generated for each HVAC test and can be viewed and exported from a cloud-based energy management platform.

Abstract: A method including training, by one or more processors, a generative AI model using first operating data from building equipment and a plurality of first service reports indicating a plurality of first problems associated with the building equipment. The method may include predicting, by the one or more processors using the generative AI model, one or more future problems likely to occur with the building equipment based on second operating data from the building equipment. The method may include automatically initiating, by the one or more processors, one or more actions to prevent the one or more future problems from occurring or mitigate an effect of the one or more future problems.

Abstract: A method including training, by one or more processors, a generative AI model using a plurality of first service requests handled by technicians for servicing building equipment. The generative AI model may be trained to predict root causes of a plurality of first problems corresponding to the plurality of first service requests. The method may include receiving, by the one or more processors, a second service request for servicing building equipment. The method may include predicting, by the one or more processors using the generative AI model, a root cause of a second problem corresponding to the second service request based on characteristics of the second service request and one or more patterns or trends identified from the plurality of first service requests using the generative AI model.

Abstract: Systems and methods are disclosed relating to building management systems with building equipment servicing. For example, a system can include at least one machine learning model configured using training data that includes at least one of unstructured data or structured data regarding items of equipment. The system can provide inputs, such as prompts, to the at least one machine learning model regarding an item of equipment, and generate, according to the inputs, responses regarding the item of equipment, such as responses for detecting a cause of an issue of the item of equipment, performing a service operation corresponding to the cause, or guiding a user through the service operation.

Abstract: A method including training, by one or more processors, a generative AI model using a plurality of first service requests handled by technicians for servicing building equipment. The generative AI model may be trained to predict root causes of a plurality of first problems corresponding to the plurality of first service requests. The method may include receiving, by the one or more processors, a second service request for servicing building equipment. The method may include predicting, by the one or more processors using the generative AI model, a root cause of a second problem corresponding to the second service request based on characteristics of the second service request and one or more patterns or trends identified from the plurality of first service requests using the generative AI model.

Abstract: A method includes receiving, by one or more processors, an unstructured service report corresponding to a service request handled by one or more technicians for servicing building equipment. The unstructured service report may include unstructured data not conforming to a predetermined format or conforming to a plurality of different predetermined formats. The method may include extracting, by the one or more processors, a set of standards from one or more technical documents associated with the building equipment. The method may include automatically generating, by the one or more processors using a generative AI model, a structured service report in the predetermined format for delivery to a customer associated with the building equipment. Generating the structured service report may include standardizing the structured service report to comply with the set of standards extracted from the one or more technical documents associated with the building equipment.

Abstract: A method includes training, by one or more processors, a generative AI model using training data including a plurality of first service requests indicating a plurality of first problems associated with building equipment and a plurality of first actions performed in response to the plurality of first service requests. The method may include receiving, by the one or more processors, a second service request indicating a second problem associated with building equipment. The method may include automatically determining, by the one or more processors using the generative AI model, one or more second actions to perform based on characteristics of the second service request. The method may include automatically initiating, by the one or more processors, the one or more second actions to address the second problem associated with the building equipment.

Abstract: A method including training, by one or more processors, a generative AI model using first operating data from building equipment and a plurality of first service reports indicating a plurality of first problems associated with the building equipment. The method may include predicting, by the one or more processors using the generative AI model, one or more future problems likely to occur with the building equipment based on second operating data from the building equipment. The method may include automatically initiating, by the one or more processors, one or more actions to prevent the one or more future problems from occurring or mitigate an effect of the one or more future problems.

Abstract: A method includes receiving, by one or more processors, a plurality of first unstructured service reports corresponding to a plurality of first service requests handled by technicians for servicing building equipment. The plurality of first unstructured service reports may include unstructured data not conforming to a predetermined format or conforming to a plurality of different predetermined formats. The method may include training, by the one or more processors, a generative AI model using the plurality of first unstructured service reports. The method may include performing, by the one or more processors using the trained generative AI model, one or more actions with respect a second service request subsequent to training the generative AI model.

Abstract: A method including training, by one or more processors, a generative AI model using a plurality of first unstructured service reports corresponding to a plurality of first service requests handled by technicians for servicing building equipment. The plurality of first unstructured service reports include unstructured data not conforming to a predetermined format or conforming to a plurality of different predetermined formats. The method includes receiving, by the processors, a second service request for servicing building equipment. The method includes generating, by the processors using the generative AI model, a user interface prompting a user to provide information about a problem leading to the second service request as unstructured data not conforming to the predetermined format or conforming to the plurality of different predetermined formats.

Abstract: A method includes receiving, by one or more processors, unstructured service data corresponding to one or more service requests handled by technicians for servicing building equipment of a building. The method may include detecting, by the one or more processors, an identifier of the building equipment, a space of the building, or a customer associated with the building using the unstructured service data. The method may include retrieving, by the one or more processors based on the identifier of the building equipment, the space, or the customer, additional data associated with the building equipment, the space, or the customer from one or more additional data sources separate from the unstructured service data. The method may include training, by the one or more processors, a generative AI model using training data including the unstructured service data and the additional data.

Abstract: A method includes receiving, by one or more processors, an unstructured service report corresponding to a service request handled by one or more technicians for servicing building equipment. The unstructured service report may include unstructured data not conforming to a predetermined format or conforming to a plurality of different predetermined formats. The method may include automatically generating, by the one or more processors using a generative AI model, a structured service report in the predetermined format for delivery to a customer associated with the building equipment. The structured service report may include additional content generated by the generative AI model and not provided within the unstructured service report.

Abstract: A method includes training, by one or more processors, a generative AI model using a plurality of first service requests handled by technicians for servicing building equipment and outcome data indicating outcomes of the plurality of first service requests. The generative AI model may be trained to identify one or more patterns or trends between characteristics of the plurality of first service requests and the outcomes of the plurality of first service requests. The method may include receiving a second service request for servicing building equipment. The method may include automatically determining, using the generative AI model, one or more responses to the second service request based on characteristics of the second service request and the one or more patterns or trends between the characteristics of the plurality of first service requests and the outcomes of the plurality of first service requests identified using the generative AI model.

Abstract: The invention involves the automated testing of HVAC units using an energy management system. The automated HVAC test is performed to understand if one or more HVAC units are operational across one or more locations. If an HVAC unit is not operational, HVAC testing could be performed to understand which component or stage of the HVAC unit is not working as designed. The automated HVAC test is also used to calculate the efficiency of the HVAC unit(s) being tested. The various HVAC tests are performed on all HVAC units as a form of preventative maintenance and diagnostics. These tests can be scheduled on-demand, for a future date and time, or on a recurring schedule (monthly or quarterly). A report is generated for each HVAC test and can be viewed and exported from a cloud-based energy management platform.

Abstract: The invention involves the automated testing of HVAC units using an energy management system. The automated HVAC test is performed to understand if one or more HVAC units are operational across one or more locations. If an HVAC unit is not operational, HVAC testing could be performed to understand which component or stage of the HVAC unit is not working as designed. The automated HVAC test is also used to calculate the efficiency of the HVAC unit(s) being tested. The various HVAC tests are performed on all HVAC units as a form of preventative maintenance and diagnostics. These tests can be scheduled on-demand, for a future date and time, or on a recurring schedule (monthly or quarterly). A report is generated for each HVAC test and can be viewed and exported from a cloud-based energy management platform.

Abstract: The invention involves the automated testing of HVAC units using an energy management system. The automated HVAC test is performed to understand if one or more HVAC units are operational across one or more locations. If an HVAC unit is not operational, HVAC testing could be performed to understand which component or stage of the HVAC unit is not working as designed. The automated HVAC test is also used to calculate the efficiency of the HVAC unit(s) being tested. The various HVAC tests are performed on all HVAC units as a form of preventative maintenance and diagnostics. These tests can be scheduled on-demand, for a future date and time, or on a recurring schedule (monthly or quarterly). A report is generated for each HVAC test and can be viewed and exported from a cloud-based energy management platform.

Abstract: The invention involves the automated testing of HVAC units using an energy management system. The automated HVAC test is performed to understand if one or more HVAC units are operational across one or more locations. If an HVAC unit is not operational, HVAC testing could be performed to understand which component or stage of the HVAC unit is not working as designed. The automated HVAC test is also used to calculate the efficiency of the HVAC unit(s) being tested. The various HVAC tests are performed on all HVAC units as a form of preventative maintenance and diagnostics. These tests can be scheduled on-demand, for a future date and time, or on a recurring schedule (monthly or quarterly). A report is generated for each HVAC test and can be viewed and exported from a cloud-based energy management platform.

Abstract: The invention involves the automated testing of HVAC units using an energy management system. The automated HVAC test is performed to understand if one or more HVAC units are operational across one or more locations. If an HVAC unit is not operational, HVAC testing could be performed to understand which component or stage of the HVAC unit is not working as designed. The automated HVAC test is also used to calculate the efficiency of the HVAC unit(s) being tested. The various HVAC tests are performed on all HVAC units as a form of preventative maintenance and diagnostics. These tests can be scheduled on-demand, for a future date and time, or on a recurring schedule (monthly or quarterly). A report is generated for each HVAC test and can be viewed and exported from a cloud-based energy management platform.