Energy Management Information System

The vendor is required to provide energy management information system (“EMIS”) which includes utility bill management can automate many of the time-consuming steps involved in analysis and reporting.
- Automating spreadsheet-based data management, analysis, reporting, and chargeback processes where possible.
- Consolidating utility invoicing, interval data and emissions data on a single platform to expand and speed reporting capacity.
- Ongoing auditing of utility invoices for all uo locations to ensure accurate billing.
- Ongoing energy analysis that automatically flags atypical cost/consumption based on weather-normalized baselines in order to catch issues early.
- Automating essential facets of measurement and verification protocols to help manage ongoing reporting from multiple efficiency projects.
- Automating KPI reporting for different campuses and different subcategories of buildings to easily keep stakeholders informed.
- Data integration. Proposed platform must:
• Be capable of reading, analyzing and reporting on utility invoice data.
• Have the capability to integrate utility meter data from existing owner systems such as university’s utility data warehouse (SQL server), Schneider PME, and central plant meters via factory talk.
• Be capable of collecting data at 15 min intervals or less.
• Be capable of making data available for analysis in real or near-real time through a continuous and automated data acquisition process.
• Have the ability to consolidate meter readings to create virtual meters.
• Have the ability to upload and store a minimum history of 5 years of energy use data.
• Have the ability to store at least 5 years of trend data at 15-minute intervals for analysis, reporting, and visualization.
• Must describe what steps it takes to ensure that the data integration process does not impact the speed of existing systems.
- Data validation.
• Work with university staff to ensure that all data brought into the system is as accurate as possible and assist is identifying inaccurate data.
• Validate the data connection between the platform and existing systems.
• Address insufficient/inaccurate data collection intervals, false negative and false positive diagnostics, dropped communications, and erroneous tagging.
- Interval meter data analytics. The platform must be capable of:
• Tracking and providing flexible charting capabilities for multiple meters on an hourly or sub-hourly (e.g., 15-minute) basis.
• Calculating and charting historic, daily, monthly, and annual energy costs using customizable rates including time-of-use tiers.
• Normalizing data according to factors that are known to affect energy consumption, such as floor area, number of occupants, heating degree days, and cooling degree days.
• Converting, displaying, and reporting consumption in a variety units including BTU, klbs, ton-hours, kw, kgal, etc. using editable conversion factors.
• Data trending with selectable time period, data points (multiple) and scaling options.
• Benchmarking to allow users to add custom KPIS and custom normalization factors.
• Benchmarks must be configurable to allow for comparison to various prior time periods.
• Cross-sectional benchmarking to allow users to create “peer groups” and will rank buildings by a performance index.
• Interfacing with the energy star portfolio manager to automatically produce energy star scores for user-selected buildings.
• Producing longitudinal benchmarking with the ability to compare the KPI in a fixed period (day/week/month/year) for a building, system, or equipment component against past (and/or predicted) performance of the same period length.
• Producing heat maps of energy consumption, color coding the magnitude of the metered energy usage for a user-selected time period of historic data.
• Characterizing and predicting the expected energy usage based on key drivers such as weather (degree days or outside air temperature), occupancy, time of day/week, and other variables for use in energy savings calculations, near-future load predictions, energy use comparisons, and energy anomaly detection.
• Identifying and flagging unexpectedly high or low energy use at the meter/building level with user-defined thresholds and the ability to track anomaly persistence.
• Providing peak load monitoring over selectable time periods including customizable alerts to specified individuals.
• Detecting meter and sensor data quality issues such as gaps, spikes, and flat-lines.
- Utility bill analytics. The platform must be capable of:
• Importing utility bills from an existing processing system, database archive or directly from vendor emails.
• Reading utility bills and checking for inaccuracies including mathematical errors, anomalous cost/consumption, incorrect rate tier, double billing, inactive accounts, etc.
• Providing capabilities for savings analysis and comparison across a building portfolio through benchmarking, deriving energy use intensity, aligning bills with a calendar month, and normalizing usage based on weather data using standard protocols such as international performance measurement and verification protocol (“IPMVP”).
• Customizing reporting based on variable subsets of buildings.
• Generating chargebacks (tenant billing).
• Charting and report energy costs against budget (accruals).
- Ability to create a map that displays KPI data for each University building and the ability to select individual buildings to view building energy reporting.
- Ability to produce detailed reporting on emissions at the campus and individual building level, including emissions avoided through efficiency projects.
- Ability for platform to detect meter and sensor data quality issues such as gaps, spikes, and flat-lines with the capacity to automatically fill and/or correct data.
- Ability to extract real or near real time data from the bas with the ability to create trends for equipment evaluation.
- Ability to integrate with other platforms such as the CMMS (aim).
- Ability to add fault detection and diagnosis tools in the future.
- Ability to add automated system optimization tools in the future.
- At least 20 years of standing in the energy management software industry.
- Prior experience working with higher education institutes.
- Contract Period/Term: 3 years