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SkyFoundry SkySpark
SkySpark Analytics automatically analyzes data from smart devices and equipment systems to identify issues, faults, deviations, and anomalies enabling improved performance, reduced downtime, and operational savings.
Hub Highlights:
Includes over 500 built-in analytic functions to help find equipment and system issues.
Includes a rich set of applications to visualize data and analytic results that can be displayed in standard web browsers without plug-ins. SkySpark can also output analytic results to third party applications via open APIs.
Bundled with Arc, a workflow engine for creating customized workflows to track work orders.
Last Edited on: 10/25/2023
Software Location
On-premise or Cloud-based
Software Configuration
Vendor or Certified System Integrator
Data sources
BAS real-time and historical data, Third-party cloud
Communications Protocols
IP-based. Interfaces with standard protocols such as Modbus TCP, Obix, Haystack, SNMP, Sedona, OPC, MQTT, SQL, CSV import (manual batch or automated), and a REST API.
Software Maintenance Model
Updates and support provided under yearly maintenance agreement
Intended Users
Site staff, FDD vendor, Third-party consultant
Data Ownership
End-customer
Computerized Maintenance Management Systems (CMMS)
Yes
Supervisory Control
Yes
Prioritization of Faults
Yes
Assessment of Fault Impacts
Energy and costs impacts
Interval Meter Data Analytics
Yes
Due to the nature of fault detection and diagnostic systems (highly customizable/key performance indicators dependent on building characteristics), the evaluation team did not conduct lab-based performance evaluations. The "test procedures" document below explains the data that were collected and results can be found in the technical specs and user experience sections.
Last Edited on: February 21, 2025
Usability and performance
How does the product impact energy efficiency?
Uses cutting-edge machine learning algorithms to detect energy anomalies and suggest optimization strategies. It can automatically identify patterns and correlations in energy data that might be missed by human analysts, providing actionable insights to reduce waste and improve efficiency. The system is able to learn and adapt over time, towards continual improvement in energy management.
Installation
What is the typical installation time for the product?
Installation usually completed in 1-2 weeks, depending on the size and complexity of the facility. This timeframe includes setting up data connections, configuring the machine learning algorithms, and creating initial dashboards and reports. The system's flexible architecture allows for a relatively quick deployment while still providing advanced analytics capabilities.
Maintenance
What self-maintenance and configuration options does the product offer to users?
Includes a robust set of self-maintenance tools for rule creation and data analysis. Users can create and modify analytic rules, adjust machine learning parameters, and customize dashboards and reports. This flexibility allows organizations to continually refine their energy management strategies and adapt the system to their specific needs.
