How does the Sighten Energy Efficiency Modeling work?

Overview

A homeowner’s typical usage is pulled based on the address.  For every EE 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.  

EXAMPLE

Given a home with 20,000 kWh of annual usage, let’s say the 20% of the usage comes from appliances and 5% of the appliance usage comes from the Refrigerator.  Upgrading the refrigerator to a 25% more efficient version:

20000 kWh * .2 * .05 = Refrigerator usage pre install = 200 kWh

200 kWh * (1-.25) = Refrigerator usage post install = 150 kWh

So, the overall percent reduction = 50/2000 kWh = .025 or 2.5%

The post installation usage = 1950 kWh

Related 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

• TMY Typical

• 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%.

• Energy Star Data

• Example entries

• 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

• Windows - default % reduction based on climate, window U-factor

  • Consider number of rooms, square footage, construction year

• Roofing - default % reduction based on climate, existing roof: solar absorption and emissivity

  • Consider number of rooms, square footage, construction year, to set defaults

• 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

• Lighting 

  • Consider number of rooms, square footage, construction year, indoor vs outdoor, persona

• Appliances

  • Selected appliances only reduce the average usage of that specific appliance type, within the full category/8760 of appliances

  • Consider number of rooms, square footage, construction year, persona