This document summarizes EverBright’s energy efficiency modeling, which calculates the usage and cost savings of various types of energy efficiency projects.
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Overview
A homeowner’s typical usage is pulled based on the address. For every EE each energy efficiency product added to the project, a percent reduction is calculated based on the percent better than the standard. The percent reduction is pulled directly from EnergyStar data on a per appliance basis. The percent reduction is applied directly to the end use consumption for the usage category directly connected to the EE product. Applying the reduction gives the post installation usage, and impact is directly shown in the usage/production charts.
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You can find the full list of EnergyStar products here. Sighen also allows the creation of your own EverBright also supports adding custom energy efficiency equipment. |
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Given a home with 20,000 kWh of annual usage, let’s say the with 20% of the usage comes from appliances and 5% of the appliance usage comes from the Refrigeratorrefrigerator. Upgrading the refrigerator to a 25% more efficient version:
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The post installation usage = 1950 kWhRelated Information
Basic home information:
size – square footage
number of stories
age
number of occupants
times mostly occupied (e.g., evenings/weekends)
vacation times – monthly, seasonally, annually
insulation level
window pane information
EV information
make and model
miles driven per day
Natural gas consumption (if applicable) – therms
Applicable energy efficiency incentives to be included in savings projections
Note: not all EnergyStar datasets contain information about the american standard of consumption/usage.
Calculation Methods
For each hour at a specific location, we want to estimate how much energy is used by the homeowner for an equipment type. Then we want to estimate the reduction in usage the homeowner would see from upgrading their equipment.
Typical Load Breakdown
Get the typical hourly shape of usage by type (AC, Heating, Lighting, etc.) as a percentage of all usage
Explore other datasets (PSM3/NSRB??)
What are the exact equipment/usage categories we need?
This dataset might not have them
If they dont we need to find typical profiles somewhere else or generate them ourselves
Store data in Solar DB for each station
Equipment Specific Reduction
Get estimated % reduction in equipment type specific usage from upgrading to specific equipment (i.e. A typical older model uses 1250kWh/yr and with the LG 2018 29.7 Cu Ft fridge you would now use 500kWh/yr, so the reduction for that piece of equipment is 60%.
Example entries
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ENERGY STAR Unique ID
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Brand Name
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Model Number
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Annual Energy Use (kWh/yr)
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US Federal Standard (kWh/yr)
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% Better than US Federal Standard (kWh/yr)
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Energy Factor (EF)
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2253929
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Beko
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DUT28430*
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225
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307
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27
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2302468
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Bertazzoni
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DW18PR
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234
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307
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24
Use % Better than US Federal Standard (kWh/yr) (or Energy Factor?)
Stored on Equipment with other data in django on Efficiency Equipment model (webapp.apps.equipment.models)
Allows us to calculate the new category specific usage by scaling the 8760 category-specific usage profile using the % reduction from the typical equipment.
Similarly, we will store the usage amount, so we have more flexibility to alter out baseline, etc. in our calculations
DETAILS
Required Inputs:
8760 usage profile
Energy efficient product
Homeowner persona
Steps:
Usage profiled pulled via Genability
Monthly and Annual values can be updated by the user
Consumption products added to represent newly added or planned usage
Will not be ready for SPI
Persona to modify usage curve
EE products selected
Each product has its own specific data to calculate usage reduction
Solar system design
Calculation:
Total Offset Usage = Usage + Consumption - Solar System Production - EE Product Reduction
Usage: Genability + Manual Inputs
Solar system product: Production calculator or PVWatts
EE Products: Typical Usage pulled by region from EIA.gov data source
HVAC
Lighting
Appliances
Washer
Dryer
Refrigerator
Dishwasher
Water Heaters
Other EE Products:
Roofing
Cooking/Stove
Windows
Not Implementing:
Misc - the usage of miscellaneous products explicitly: TVs, Microwaves, Cell Phones, etc will not be reduced by specific items. It will be reduced by impacts to the overall usage curve, such as roofing and windows.
Product Selection
Windows - Manual override for % or kWh reduction
Roofing - Manual override for % or kWh reduction
HVAC - Manual override for % or kWh reduction
Lighting
For upgrade to LEDs, 90% decrease in usage for indoor and outdoor lighting
For upgrade to CFLs, 70% decrease in usage for indoor and outdoor
Appliances - Manual override for % or kWh reduction
Selected appliances only reduce the average usage of that specific appliance type, within the full category/8760 of appliances
Water Heaters - Manual override for % or kWh reduction
Calculate average typical usage of selected equipment from stored data to get 8760 profile for selected EE product, for each selected EE product
Calculate reduction using 8760 profile of EE product
A specific model gives the ‘precise’ reduction
A manufacturer allows us to calculate based on the average of all EE products for that specific manufacturer of a specific product type
A manual override is factored in directly as input, given the persona
Apply reduction based on % or kWh
Calculate 8760 usage profile post installation
Calculate daily, monthly, quarterly, and yearly usage profiles
Calculate bill savings, usage savings, incentives, etc.
Advanced Calculations - for future consideration
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Windows - default % reduction based on climate, window U-factor
Consider number of rooms, square footage, construction year
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Roofing - default % reduction based on climate, existing roof: solar absorption and emissivity
Consider number of rooms, square footage, construction year, to set defaults
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HVAC - default % reduction based on climate,
Requires primary energy consumption input: gas or electric
Consider number of rooms, square footage, construction year, heating source (furnace, boiler, heat pump), heating type (forced air, steam radiant, radiant heating, hot water base boards, electric baseboards), seasonality, heating / cooling source, persona
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Lighting
Consider number of rooms, square footage, construction year, indoor vs outdoor, persona
Appliances
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Selected appliances only reduce the average usage of that specific appliance type, within the full category/8760 of appliances
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Usage Modeling
To calculate the usage impact of retrofits, we model hourly usage categorized into heating, cooling, lighting, water heater, appliance, EV, and pool usage. We model typical usage based on zipcode using baseline data. This can be edited based on monthly or annual inputs for usage or bills (or from a CSV of interval data). This usage can be altered with projected usage to model changes from current usage due to the addition of a pool, EV, etc, or other significant changes to usage (e.g. change in occupancy). Usage personas can also be applied to adjust the daily usage profile by hour using template personas (e.g. 9-5 workers, daytime user) or custom 24 hour inputs.
With hourly usage modeled, we separate the usage into the categories mentioned above using TMY datasets which estimate the percentage of usage in each category by hour. This allows us to capture the interdependence of the timing of usage and what category. For example, if you adjusted usage to be primarily during the day, you would see cooling usage as a percentage of total usage increase. Appliance usage is also broken down into subcategories (washer, dryer, cooking, dishwasher, and refrigerator based on regional usage patterns.
Efficiency Equipment
We have a catalog of efficiency retrofits of many categories including furnaces, water pumps, boilers, air conditioning, fans, roofing, windows, insulation, thermostat, air sealing, duct sealing, light, water heating, solar thermal, washers, dryers, cooking, dishwashers, refrigerators, and smart home monitoring. Many of these upgrades have default reduction percentages based on the percent better than the standard according to Energy Star data. EverBright also allows the creation of your own energy efficiency equipment with custom default reduction percentages.
Reduction Modeling
The usage savings are modeled by applying the percentage reduction to the retrofit’s corresponding usage category(ies) hourly usage or to the total hourly usage depending on the user’s input. This calculation is applied before any solar or storage modeling because they will not influence the customers consumption.
Savings Analysis
Bill savings are calculated using a third-party integration with Genability, our utility rate provider. Using their integration, we calculate pre and post project bills from a user’s inputs for pre, and post project utility rates, and utility bill escalation as well as the hourly usage before and after the energy efficiency retrofit.