Study guide
3.3 Baseline Energy Data, Utility Bill Disaggregation, and ECM Identification
AEE Certified Energy Manager CEM
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1 Chapter 1: Energy and Sustainability Policies, Codes, and Standards
1.1 Federal and State Energy Policies, Decarbonization Targets, and ESG Reporting 1.2 Building Energy Codes: ASHRAE Standard 90.1, IECC, and Compliance Paths 1.3 Energy Management Systems: ISO 50001, ENERGY STAR Benchmarking, and Portfolio Manager
2 Chapter 2: Energy Rates, Tariffs, and Supply Options
2.1 Utility Rate Structures: Volumetric vs. Demand Charges, Time-of-Use (TOU), and Real-Time Pricing 2.2 Demand Charges, Ratchets, Peak Shaving, and Power Factor Penalties 2.3 Energy Procurement, Deregulated Markets, Supply Contracts, and Green Tariffs 2.4 Demand-Side Management, Load Curtailment, and Transportation Energy Efficiency
3 Chapter 3: Energy Audits and Instrumentation
3.1 Energy Audit Methodologies: ASHRAE Level I, II, and III Audit Scopes 3.2 Audit Instrumentation, Data Loggers, Metering Accuracy, and Safety (PPE) 3.3 Baseline Energy Data, Utility Bill Disaggregation, and ECM Identification 3.4 Energy Modeling, Simulation, Digital Tools, and Performance Indicators
4 Chapter 4: Energy Accounting and Economics
4.1 Energy & Power Units (kWh, kW, Therms, MMBtu, Joules) and Conversion Calculations 4.2 Financial Metrics: Simple Payback Period, Return on Investment (ROI), and LCCA 4.3 Time Value of Money: Net Present Value (NPV), Internal Rate of Return (IRR), and Escalation 4.4 Advanced Financial Evaluation: Annual Worth, SIR, Depreciation, and Interest Formulas
5 Chapter 5: Electrical Power Systems and Motors
5.1 Electrical Fundamentals: Single/Three-Phase Power, Apparent/Real/Reactive Power, and Power Factor 5.2 Electric Motors: Efficiency Classes, Loading Calculations, NEMA Premium, and Losses 5.3 Variable Frequency Drives (VFDs), Affinity Laws, and Electrical Harmonics
6 Chapter 6: Lighting Systems
6.1 Lighting Fundamentals: Photometric Quantities (Lumens, Lux, Foot-Candles, CRI, CCT) and Efficacy 6.2 Lighting Technologies & Retrofits: LED Systems, Fluorescent/HID Replacement, and LPD 6.3 Lighting Controls: Occupancy/Vacancy Sensors, Daylight Harvesting, Dimming, and Task Tuning
7 Chapter 7: HVAC Systems and Building Envelope
7.1 HVAC Fundamentals & Psychrometrics: Dry-Bulb, Wet-Bulb, Dew Point, Relative Humidity, and Enthalpy 7.2 Cooling and Heating Equipment: Chillers (COP, kW/ton, EER, SEER), Heat Pumps, and Boilers 7.3 Air and Water Distribution: AHUs, VAV vs. CAV, Pumping Systems, Fans, and Economizers 7.4 Building Envelope: Heat Transfer (U-factor, R-value, SHGC), Infiltration, and Thermal Insulation 7.5 Building Envelope Calculations: Degree-Days, Solar Heat Gain, and Passive Design 7.6 Refrigerants, Cooling Towers, and Specialized HVAC Technologies (Absorption, VRF, Chilled Beams)
8 Chapter 8: Building Automation, Controls, and Artificial Intelligence Systems
8.1 Building Automation Systems (BAS) Architecture, BACnet, Modbus, and DDC Loops 8.2 Energy Control Strategies: Temperature/Pressure Resets, Night Setback, and Deadbands 8.3 Fault Detection & Diagnostics (FDD), Energy Analytics, and AI Optimization 8.4 IoT, Cloud-Based BAS, Expert Systems, and Cyber-Security
9 Chapter 9: Energy Storage Systems
9.1 Battery Energy Storage Systems (BESS): Technologies, Capacity, Efficiency, and Safety 9.2 Thermal Energy Storage (TES): Chilled Water, Ice Storage, and Phase Change Materials 9.3 Storage Dispatch Strategies, Tariff Integration, Peak Shaving, and Microgrid Integration
10 Chapter 10: Boiler and Steam Systems
10.1 Boiler Fundamentals & Efficiency: Combustion Efficiency, Excess Air, and Stack Losses 10.2 Steam Distribution & Traps: Steam Trap Types, Failure Modes, Testing, and Insulation 10.3 Condensate Recovery, Boiler Blowdown Control, and Waste Heat Recovery
11 Chapter 11: Distributed Generation and Renewable Energy Systems
11.1 Solar Photovoltaic (PV) and Wind Energy Systems: Design, Inverters, and Capacity Factor 11.2 Combined Heat and Power (CHP) / Cogeneration: Thermal Efficiency and System Sizing 11.3 Grid Interconnection, Net Metering, Islanding, and Microgrid Resilience 11.4 Biomass, Geothermal, Hydropower, Solar Thermal, and Waste-to-Energy Systems
12 Chapter 12: Industrial Systems
12.1 Compressed Air Systems: Efficiency, Pressure Drop, Leak Management, and Receiver Storage 12.2 Industrial Process Heating, Cooling Towers, and Industrial Refrigeration 12.3 Production-Normalized Energy Metrics, Pumping System Optimization, and Process Integration
13 Chapter 13: Operations, Maintenance, and Commissioning
13.1 Preventive, Predictive, and Reliability-Centered Maintenance Strategies 13.2 Building Commissioning: Existing Building Commissioning (EBCx) & Retro-Commissioning (RCx) 13.3 Savings Persistence, Continuous Commissioning, Sensor Calibration, and Alarms 13.4 Quantifying O&M Losses: Compressed Air Leaks, Steam Traps, Boiler Scale, and Group Relamping 13.5 CMMS, Water Treatment, and Human Behavior in Energy Management
14 Chapter 14: Energy Savings Performance Contracting and Measurement & Verification
14.1 Energy Savings Performance Contracting (ESPC), ESCO Roles, and Financing Models 14.2 Measurement and Verification (M&V): IPMVP Options A, B, C, and D 14.3 Baselines, Weather Normalization, Non-Routine Adjustments, and M&V Reporting
3.3 Baseline Energy Data, Utility Bill Disaggregation, and ECM Identification
Key Takeaways Explain This Topic AI
A baseline energy profile establishes a historical reference point against which future energy savings are measured and verified.
Utility bill disaggregation involves separating base load (weather-independent) energy use from weather-dependent heating and cooling loads.
Degree days (HDD and CDD) are essential metrics used to normalize energy consumption data against varying weather conditions.
Energy Conservation Measures (ECMs) are identified by analyzing the discrepancy between current operations and optimal efficiency.
The Load Factor is the ratio of average demand to peak demand; a low load factor indicates poor demand management.
Last updated: July 2026
Before you can accurately claim energy savings, you must know where you started. Establishing a robust baseline energy profile is a critical step in the energy auditing process. The baseline serves as the benchmark against which all proposed Energy Conservation Measures (ECMs) are evaluated and, post-implementation, how actual savings are verified via Measurement and Verification (M&V) protocols.
Establishing the Baseline
The baseline is typically derived from 12 to 36 months of historical utility billing data. This data includes electrical consumption (kWh), electrical demand (kW), natural gas usage (Therms or CCF), and other fuels. However, raw utility data alone is insufficient; it must be correlated with independent variables that drive energy consumption.
The most common independent variables are:
Weather: Evaluated using Heating Degree Days (HDD) and Cooling Degree Days (CDD).
Production: In industrial settings, energy use is often tied to units of product manufactured (e.g., tons of steel, gallons of beverage).
Occupancy: For hotels or hospitals, occupancy rates significantly impact energy use.
For the CEM exam, you must understand how to normalize energy data. If a facility used 10% less natural gas this year compared to last year, you cannot automatically claim the new boiler is saving energy. If this winter was 15% milder (fewer HDD) than the previous winter, the facility actually performed worse relative to the weather conditions.
Utility Bill Disaggregation
Utility bill disaggregation is the process of breaking down total utility consumption into distinct end-use categories. The most fundamental disaggregation separates Base Load from Seasonal Load .
Base Load (Weather-Independent)
Base load represents the energy consumed by equipment that operates regardless of outdoor temperature.
Examples: Interior lighting, plug loads (computers, servers), process equipment, domestic hot water, and base ventilation.
Identification: In a commercial building, the base load for electricity can often be estimated by looking at the utility bills during the 'shoulder months' (typically spring and fall: e.g., April, May, October) when neither heating nor cooling systems are running heavily. The lowest monthly consumption often approximates the monthly base load.
Seasonal Load (Weather-Dependent)
Seasonal load is the energy consumed by the HVAC systems to maintain indoor comfort as outdoor temperatures fluctuate.
Examples: Chillers, cooling towers, DX rooftop units, boilers, and furnaces.
Identification: The seasonal load is calculated by subtracting the estimated base load from the total consumption during summer or winter months. For electricity, the summer peak minus the base load estimates the cooling energy. For natural gas, the winter peak minus the summer base load (usually domestic hot water) estimates the heating energy.
Worked Example: Disaggregation
A facility's monthly electrical consumption is:
- May (Shoulder Month): 100,000 kWh
- July (Peak Summer Month): 250,000 kWh
Calculation:
- Estimated Base Load = 100,000 kWh/month
- Estimated Cooling Load in July = Total July Load - Base Load
Estimated Cooling Load in July = 250,000 kWh - 100,000 kWh = 150,000 kWh.
This simple analysis tells the auditor that in July, 60% (150k/250k) of the electrical energy is dedicated to cooling, directing their focus toward chiller efficiency and building envelope improvements.
Load Factor Analysis
When analyzing electrical data, the Load Factor (LF) is a crucial metric indicating how efficiently a facility utilizes its peak demand.
Load Factor Formula: Load Factor = (Total kWh consumed in period) / (Peak kW demand * hours in period)
A high load factor (approaching 1.0 or 100%) means the facility uses energy at a constant rate, which is efficient and minimizes demand charges. A low load factor (e.g., < 0.3 or 30%) indicates large demand spikes relative to overall consumption. A low load factor is a red flag for the auditor, signaling opportunities for Demand Shifting (moving loads to off-peak hours) or Peak Shaving (using generators or batteries during peak times to reduce utility demand charges).
Identifying Energy Conservation Measures (ECMs)
Once the baseline is established and the data disaggregated, the auditor systematically identifies ECMs. ECM identification bridges the gap between the facility's current operational state and its theoretical optimal efficiency.
Common ECM Categories:
Operations and Maintenance (O&M): These are often low-cost/no-cost measures identified in a Level I audit.
Examples: Adjusting HVAC setpoints, repairing compressed air leaks, fixing stuck outside air dampers, turning off lights in unoccupied areas.
Lighting: One of the most common and easily calculated ECMs.
Examples: Upgrading fluorescent fixtures to LEDs, installing occupancy sensors, implementing daylight harvesting controls.
HVAC Systems: Often the largest source of savings in commercial buildings.
Examples: Installing Variable Frequency Drives (VFDs) on fans and pumps, upgrading to high-efficiency chillers, implementing air-side economizers, optimizing chiller plant staging.
Building Envelope:
Examples: Adding roof insulation, upgrading windows, sealing weatherstripping to reduce infiltration.
Process and Industrial:
Examples: Recovering waste heat from air compressors or boiler stacks to preheat water, optimizing motors and drive systems.
For each identified ECM, the auditor must calculate the proposed energy savings (kWh, Therms), the proposed demand savings (kW), the estimated implementation cost, and the financial viability (Simple Payback, ROI) to present a prioritized action plan to the facility owner.