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Energy costs are a significant line item for commercial and industrial facilities, yet most organizations lack visibility into where energy is wasted. Consumption data sits in meter systems and building management platforms, but translating raw readings into actionable efficiency recommendations requires analytical effort that facility managers rarely have time for. The result is persistent waste — HVAC running in unoccupied spaces, lighting schedules misaligned with usage, and equipment operating outside optimal ranges. Wayak connects your meter data and building systems, runs Python-based consumption analysis in a playbook, and delivers facility-specific efficiency recommendations. An agent gives facility managers instant access to consumption breakdowns, benchmarking comparisons, and cost impact estimates — making it easy to prioritize the improvements that save the most money.

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What you need

Data sources

  • Meter data management system — interval consumption readings (15-minute or hourly) by meter, building, and end-use category
  • Building management system (BMS) — HVAC schedules, setpoints, lighting controls, and equipment run-time data
  • Weather API — historical and forecast temperature, humidity, and solar radiation for normalization

Knowledge spaces

  • Energy efficiency standards — upload ASHRAE standards, ENERGY STAR benchmarks, and internal efficiency targets by building type
  • Building profiles — upload building specifications including square footage, occupancy schedules, equipment inventories, and retrofit history
Semantic layer: Define these in your ontology before setting up the agent.
ComponentNameDefinition
ObjectFacilityMaps to the facilities table in the building management system. Represents a building or campus with location, type, and specifications
ObjectMeterMaps to meters in the meter data management system. Represents a consumption measurement point with end-use category
MetricEnergy Use Intensity (EUI)Total energy consumption (kBtu) / gross floor area (sq ft), measured annually or over a rolling 12-month window
MetricCost per Square FootTotal energy cost / gross floor area, using current utility rate schedules
DimensionBuilding TypeCategorizes facilities by use (office, warehouse, retail, data center, manufacturing)
DimensionEnd UseGroups consumption by category (HVAC, lighting, plug loads, process equipment)
See building a semantic layer for a step-by-step guide.

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Agent setup

1

Create the agent

Go to Agent Space → New agent.
FieldValue
NameEnergy Efficiency Analyst
RoleFacility Energy Optimization Specialist
GoalAnalyze facility energy consumption, identify waste patterns, benchmark against standards, and recommend efficiency improvements with cost impact estimates
2

Set the description

You are a facility energy optimization specialist who analyzes consumption data to find savings opportunities. You break down energy use by end-use category, compare facilities against ASHRAE and ENERGY STAR benchmarks, and identify anomalies like after-hours consumption or weather-inconsistent HVAC behavior. You quantify the cost impact of each finding and prioritize recommendations by payback period. You present data clearly with supporting comparisons so facility managers can justify improvement investments.
3

Scope data access

Grant access to:
  • Meter data management system (interval consumption data)
  • Building management system (HVAC schedules, setpoints, equipment data)
  • Weather API (historical and forecast weather data)
  • Energy efficiency standards knowledge space
  • Building profiles knowledge space
  • Facility and Meter objects, EUI and Cost per Square Foot metrics
4

Add skills

Trigger: User asks for an energy breakdown of a specific facility or group of facilities.
  1. Retrieve the facility profile from the building profiles knowledge space (square footage, type, occupancy schedule).
  2. Pull interval consumption data from the meter data management system for the requested period.
  3. Break down consumption by end-use category (HVAC, lighting, plug loads, process).
  4. Calculate EUI and cost per square foot for the facility.
  5. Compare against the applicable ENERGY STAR or ASHRAE benchmark from the energy efficiency standards knowledge space.
  6. Return a consumption profile with total usage, end-use breakdown, EUI, benchmark comparison, and month-over-month trend.
Trigger: User asks the agent to identify energy waste or anomalies.
  1. Pull 90 days of interval consumption data for the specified facility.
  2. Retrieve the occupancy schedule and HVAC setpoints from the building management system.
  3. Identify after-hours consumption that exceeds the baseload threshold.
  4. Detect weather-inconsistent patterns (e.g., cooling load increasing when outdoor temperature drops).
  5. Estimate the annual cost of each identified waste pattern.
  6. Return a waste analysis with each pattern, its frequency, cost impact, and recommended corrective action.
Trigger: User asks how a facility compares to peers or standards.
  1. Calculate the EUI for the specified facility.
  2. Retrieve the ENERGY STAR benchmark for the facility type from the energy efficiency standards knowledge space.
  3. Compare against other facilities in the portfolio of the same building type.
  4. Rank the facility within the portfolio by EUI performance.
  5. Return a benchmarking report with the facility’s EUI, benchmark target, portfolio ranking, and the gap to the median and best performer.

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Automation

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Playbook: Weekly energy efficiency report

1

Set the trigger

Set the trigger to Schedule — Weekly on Monday at 7:00 AM to give facility managers an efficiency update at the start of each week.
2

Build the workflow

The workflow analyzes consumption across all facilities and surfaces the highest-impact savings opportunities:
  1. Query the meter data management system for the prior week’s interval consumption data across all monitored facilities.
  2. Query the weather API for the corresponding week’s actual weather conditions for normalization.
  3. Python code block — compute weather-normalized EUI, cost per square foot, and after-hours consumption for each facility. The code normalizes consumption using heating and cooling degree days, calculates the delta from the prior week and the same week last year, and flags facilities with consumption increases exceeding 10% after normalization.
The consumption analysis step uses a Python code block to perform weather normalization using degree-day calculations and detect anomalous consumption patterns. You can customize the normalization method, comparison periods, and anomaly thresholds.
  1. Condition — if any facility has a weather-normalized consumption increase exceeding 10%, flag it for investigation and include it in a priority list.
  2. Loop — for each flagged facility, generate a brief analysis identifying the likely cause (HVAC schedule change, equipment fault, occupancy spike).
  3. Delivery — distribute the weekly efficiency report.
3

Configure delivery

  • Email — send each facility manager their building’s weekly efficiency report with benchmarking and flagged items
  • Slack — post a portfolio-wide summary to #facilities with the top 5 facilities needing attention and the estimated weekly savings opportunity
4

Test and activate

Click Run now to test with live data, then toggle to Active.

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What’s next

Asset health monitoring

Track equipment condition scores from sensor data and catch degradation before failures occur.

All Energy & Utilities use cases

See the full list.