What are the latest trends and innovations in BMS data analytics and visualization?

1 Data integration and interoperability

One of the key challenges in BMS data analytics and visualization is to integrate and harmonize data from different sources, such as sensors, meters, devices, systems, and platforms. This requires a common data model, standard protocols, and open APIs that can ensure data quality, consistency, and accessibility. Some of the emerging solutions for data integration and interoperability include the use of semantic web technologies, such as RDF and OWL, that can create a shared vocabulary and ontology for BMS data. Another example is the use of middleware, such as MQTT or OPC UA, that can facilitate data exchange and communication between heterogeneous systems and devices.

2 Data mining and machine learning

Another trend in BMS data analytics and visualization is to apply advanced data mining and machine learning techniques to extract valuable insights and patterns from large and complex data sets. These techniques can help building managers to identify anomalies, faults, and inefficiencies, as well as to predict and optimize future outcomes and behaviors. For instance, data mining and machine learning can be used to perform fault detection and diagnosis, load forecasting, demand response, energy management, and occupant behavior analysis. Some of the common methods and algorithms include clustering, classification, regression, association rules, neural networks, and deep learning.

3 Data visualization and dashboards

Data visualization and dashboards are crucial for presenting and communicating the results of BMS data analytics in an intuitive and interactive way. They can help building managers to understand, explore, and act on the data, as well as to share and collaborate with other stakeholders. Data visualization and dashboards can also enhance the user experience and engagement of the BMS system, by providing feedback, guidance, and incentives. Some of the latest trends and innovations in data visualization and dashboards include the use of augmented reality, virtual reality, gamification, and natural language processing. These can create immersive, personalized, and conversational interfaces for BMS data analytics.

4 Data security and privacy

Data security and privacy are also important aspects of BMS data analytics and visualization, especially in the context of smart buildings that collect and process large amounts of sensitive and personal data. Data security and privacy can affect the trust, confidence, and acceptance of the BMS system by the building owners, managers, occupants, and regulators. Therefore, BMS data analytics and visualization need to adopt appropriate measures and standards to protect the data from unauthorized access, modification, disclosure, or misuse. Some of the possible solutions include the use of encryption, authentication, authorization, anonymization, and consent management.

5 Data-driven decision making and action

The ultimate goal of BMS data analytics and visualization is to support data-driven decision making and action for improving the performance, efficiency, and sustainability of smart buildings. This means that the BMS system should not only provide information and insights, but also recommendations and actions that can help building managers to achieve their objectives and goals. For example, the BMS system can provide alerts, notifications, suggestions, or commands for adjusting the settings, parameters, or schedules of the building systems or devices. Moreover, the BMS system can also enable automated or semi-automated control and optimization of the building systems or devices, based on the data analysis and visualization results.

6 Data literacy and education

Finally, another trend and innovation in BMS data analytics and visualization is to enhance the data literacy and education of the building managers and occupants. Data literacy and education can help them to understand, interpret, and use the data effectively and responsibly. Data literacy and education can also increase their awareness, knowledge, and skills on the benefits and challenges of BMS data analytics and visualization. Some of the ways to promote data literacy and education include the use of tutorials, guides, manuals, webinars, workshops, or courses that can explain the concepts, methods, tools, and best practices of BMS data analytics and visualization.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?