Multi-scale building energy modeling

Summary:

• We want our buildings to provide comfort and protection from extreme weather conditions at a low energy cost

• Challenges

  • Global warming

  • Urban activities generate heat

  • Changing energy demand

    • Solar panels

    • Electric vehicle charging

    • Energy-intensive devices

    • Power grid is changing (e.g. duck curve: power demand peaks sharply in evening)

• US buildings compute 39% of primary energy (28% transport, 33% industry), similar for carbon emissions

  • Varies by city (San Diego is transport-heavy, Chicago and New York are building-heavy)

  • New building designs are net zero energy (hundreds are being built today)

  • Can we deploy these at scale?

• Need an Integrated Design approach: 

  • HVAC, lighting, daylighting, architecture

  • Costs may be paid back via energy efficiency

  • Occupant behavior is key (Are they closing the windows? What temperature is the thermostat set to?)

  • Need to choose energy efficient appliances

  • Public policy needs to set appropriate incentives to encourage adoption of energy efficient technologies

• Need building simulations to capture every point in the lifecycle of a building

  • Program

  • Design

  • Build

  • Operate

• EnergyPlus: Department of Energy flagship building energy simulation tool

  • 21 years old

  • > 1 million lines of C++ code 

  • Physics-based: takes into account 

    • Building structure

    • Occupant behavior

    • Ventillation

  • Model inter-building effects in the urban environment

    • Building <-> Building: radiant heat exchange

    • Atmosphere -> Building: induce requirements for heating / cooling

    • Building -> Atmosphere: release of heat / cold to environment

  • Analysis:

    • Small office buildings emit 3.7 times heat emission relative to their direct use

    • Large ones are 1.7: much more efficient

  • Suite of modeling tools from DOE based on EnergyPlus

    • Can model scales from whole countries, cities, buildings to individual rooms/floor within buildings

    • Require additional data: weather, building design, occupant behavior, etc.

• City-scale modeling

  • Requite city data

  • Some cities provide databases on their building stock

    • Land use, building permits, electrical permits, boiler permits

    • Need to simplify and standardize building info the make it usable within EnergyPlus

      • 2D, 3D box, rough building layout, detailed building CAD design

    • Have collected building records for 6 major US cities

    • CityBES: web application that enables energy analysis for whole cities, using EnergyPlus

    • Use-cases

      • Retrofit analysis for 940 office/retail buildings in San Francisco

        • LED lighting, plug load controllers, occupancy sensors, daylighting sensors, temperature setpoint, add economizer

        • Buildings can achieve 30-40% reduction in energy use

      • Centralizing heating/cooling for all buildings in a campus

        • Leverages more efficient large facilities and uncorrelated demand among different buildings

      • Modeling heat resilience of King’s neighborhood in Fresno

        • Active air conditioning: temperature can still reach 30°C

        • Power outage: 40-45°C

        • Passive heat management measures: Roof insulations, cool roof coating, window shades, etc.

      • US National building stock analysis: 80% reduction in building CO2 emissions by 2050 via a suite of retrofits and new building improvements

• Sensitivity to occupant behavior

  • Sensitive for smaller buildings where occupants have choices

  • Less so for large buildings with central HVAC control or non-openable windows

  • Can track occupants to improve model accuracy

    • E.g. via Nest sensors, wifi signals, PurpleAir sensors

• Accuracy of EnergyPlus is constrained by lack of data about building structure and occupant behavior

  • Currently working on explicitly documenting and modeling all the major error factors

  • International efforts to pool modeling resources and software and observational data

  • Technical/physical: climate, building envelope, building equipment

  • Human: Operation, maintenance, occupant behavior

• Wide range of energy data to calibrate models:

  • Surveys

  • Distribution of building energy use

  • Aggregated energy use for many buildings over time

  • Annual/monthly energy use for individual buildings

  • Smart meter data (15 mins whole building or individual users) for individual buildings

  • Calibrating models at all spatial/temporal granularities

    • Looking for error patterns (depend on season, building type, occupant type)

• Comprehensive modeling of energy in society

  • Exascale computing problem: major city is 10E6 buildings, 10E6-10E7 people, 10E6 vehicles, 10E6-10E8 sensors and devices

  • DOE has created a comprehensive simulation of city transport, buildings, etc. 

  • IM3: national models

• Use-cases for machine learning

  • Infer occupant behavior

  • Detection of faults in sensors and building equipment

  • Prediction of 

    • Heating/cooling loads, 

    • Energy production from solar panels

  • Infer building structure from street, satellite photos

• Use-cases for better weather forecasts

  • Predict solar power generation in next few hours

  • Buildings are connected to grid, so need to use power when power is cheapest

• EnergyPlus can be used for 

  • Long term: analyzing design of buildings

  • Short term: real-time to plan the operation of a building dynamically

    • Additional tools based on Modelica that provide more accuracy