Better Weather Data Equals Better Results: The Proof is in EE and DR! www.weatherbughome.com
Today s Speakers: Amena Ali SVP and General Manager WeatherBug Home Michael Siemann, PhD Senior Research Scientist Dave Oberholzer VP, Bus. and Partner Development
Earth Networks: WeatherBug Products Unmatched data intelligence from IoT sensors to ensure safety, comfort, efficiency & savings Large IoT Sensor Network Global weather observations & danger alerting sensors, Connected home devices & sensors Rapidly expanding IoT partner ecosystem Device manufactures and service providers Unique data collection & signal processing capabilities Clear signals in the noise of big data Big data processing 25+ terabytes real-time data daily Loyal WeatherBug Consumer Base 20+ million monthly users Daily consumer engagement Washington DC. New York. San Jose 3
Weather Matters: Weather is the Biggest Driver of Home Energy Use 4
Consumer Engagement with Weather & WeatherBug Weather is the #1 Mobile Content Category Mobile Subscriber Penetration (%) among content sites 5
Energy Intelligence for the Connected Home 6
WeatherBug Home Solutions Intelligent Demand Side Management 7
WeatherBug Home Results Intelligent Demand Side Management 8
WeatherBug Home Partner Ecosystem * 9
Energy Intelligence for the Connected Home 10
Building Energy Models White box: Need to know details of the building including: construction materials, orientation, local environment Very costly to be accurate, slow to execute simulation Black box: Energy data correlated with weather in training phase Quick to execute simulations once model is solved for No physical meaning of functional equations Poor at extrapolating Grey box Based on simplified physical model Needs training phase to identify building parameters Quick and more accurate extrapolating
Thermostat Grey Box Model Correlate indoor to outdoor conditions Unique for every house Use minimal customer data
Thermostat Grey Box Model Unknown Building Parameters (Coefficients): Wall Thermal Conductivity Wall Thermal Capacitance Natural Convection Forced Convection Wall Solar Loading Window Solar Loading Interior Wall to Air HTC Interior Wall to Mass HTC Air to Mass HTC Dynamic Pressure Infiltration Buoyancy Infiltration Air Thermal Capacity HVAC Capacity Internal Heat Generation Thermal Mass Conductivity Thermal Mass Capacitance Minimize temperature error
National Weather Service [NWS] 1200 Historical Climate Site Weather Stations 20min 1hour Temperature Wind Speed Relative Humidity
Earth Networks: WeatherBug [WB] ~10,000 Professional Weather Stations Real time Temperature Wind Speed Relative Humidity Solar Insolation
Does Data Make A Difference? Several instances of +5 C delta WeatherBug data outperforms NWS Denser network & more relevant -> neighborhood vs airport More data measurements relevant to buildings -> +solar insolation Higher temporal resolution -> seconds vs hours 16
Energy Modeling Study Details Build models for the same thermostat data from each weather data source 500 thermostats Four device manufacturers Ecobee Emerson Honeywell Radio Thermostat of America 217 unique postal codes 5/8 ASHRAE Climate Zones Three evaluation months February = Winter August = Summer October = Shoulder Average distance to WB = 5.97 km Average distance to NWS = 23.03 km Thermostat Location 17
Example Model Example model [Houston, TX] highlighting temperature fit Modeling system minimizes difference between historical thermostat and model predicted temperature Temperature Mean Absolute Percentage Error = 0.72 18
NWS Example Model Example model [Dallas, TX] highlighting performance when solar data is unavailable (built using NWS data) TMAPE = 1.21 Poor fit Similar outdoor temperatures in the early morning, but model temp significantly off 19
Model Accuracy Improved Due to Solar Data Example model [Dallas, TX] highlighting performance when solar data is available (built using WB data) Solar insolation reduction TMAPE = 1.01 improvement Model improves on the second morning due to knowing that solar load drastically declined 20
WeatherBug Model 26% More Accurate Than NWS Summary Statistics Avg Absolute Daily Runtime Error (%) Temperature Mean Absolute % Error Feb Aug Oct Feb Aug Oct NWS 12.03 15.62 18.50 1.16 1.54 1.39 WB 8.63 12.08 13.40 1.00 1.46 1.30 Runtime Prediction Improvement: 26% Temp Improvement: 8% 21
WeatherBug Model Accuracy Drives EE / DR Results Energy models using NWS and WB weather data Energy models built from WB data outperform those when using NWS data Results in higher accuracy predicting HVAC load and indoor air temperatures (occupant comfort) Needed to run precise control of thermostat setpoints for energy efficiency and smart demand response 22
Incremental EE: Texas & Massachusetts 8% Incremental Energy Savings 11.5% Incremental Energy Savings In addition to connected thermostat savings! 23
Texas Study Demonstrates 8% Energy Savings: Consumers Saved $75-100/year Approx. 8% HVAC savings with WeatherBug Home Optimization Set points were not changed, timing of the set points were modified daily based on weather and consumption correlation May thru Sept 2013 in Houston, TX Translates to ~4% whole house electric consumption savings 24
National Grid Study in MA and RI 11.4% Increased savings w/ WBH In partnership with Ecobee and WeatherBug Home, National Grid conducted a study Demonstrate the functionality of the Ecobee Smart Thermostat and integrated energy meter Evaluate the impact of WeatherBug Home s HVAC optimization control software and DR events on residential customers natural gas and electric usage RESULT? 16.5% energy savings w/ WBH, compared to 5.1% without WBH 0.1808 kwh/sqft w/ WBH, compared to 0.0358 kwh/sqft without WBH 25
WeatherBug Home EE Saved 3X More 16.5% HVAC Savings from Connected Thermostat + WBH (vs. Non-Connected) 18% 16% 14% 12% 10% 8% 6% 4% 2% 0% 11.4% 5.1% EE Savings ecobee + home energy monitoring HVAC savings from WBH From connected tstat + WBH
WeatherBug Home: 3x More Comfort Typical deadband is between 0.5 and 1.0 27
WeatherBug Home EE Not Deployed for Winter Season Gas EE optimization not available at the time in MA NatGrid study No appreciable difference proves test and control groups well matched 28
Texas DR Results: Proof is in the Pudding 1.76kW/home 1.5kW/home 13% more capacity per home or 1.76kW/home compared to 1.5 kw/home market participant average 37MW peak curtailed load Removed 294MWh of peak energy 29
Average WeatherBug Home Load Reduction per House 2.0 1.8 1.6 1.4 Capacity [kw per house] 1.2 1.0 0.8 0.6 CenterPoint Energy ERCOT Oncor 0.4 0.2 0.0 2012 2013 2014 2015 Load reduction is a function of climate and optimization strategies deployed 30
Intelligent Demand Response Intelligent Demand Response system designed to curve peak-load while minimizing customer discomfort Incorporates real-time weather and forecast, TOU and our thermodynamic models Integrates and controls a variety of connected thermostats and other critical plug loads (such as connected appliances) Standard DR Call Everything At Same Time Better DR Staggering Start Times Intelligent DR Using Thermodynamic Model
Home Energy ScoreCard The WeatherBug Home ScoreCard is a Virtual Energy Audit that educates the Consumer on: HOW weather impacts their energy use WHY they are using more energy than peer houses WHAT they can do with home specific tips to reduce their energy consumption 32
Tremendous Feedback on the ScoreCard They Like It: 84% easy to understand 83% presentation/layout is pleasing 71% like the ScoreCard They See Value in It: 56% helped them save energy 67% helped them understand HOW they use energy 54% helped them understand WHY they use energy 67% are more mindful of their energy use because of the ScoreCard They Like Their Service Provider More Because of It: 59% say they are more likely to recommend their service provider because of the ScoreCard 1 5,282 score 2 3,870 score 3 3,750 score n = 1,617 respondents 33
173 Hour Reduction in One Month! July: Flat Schedule / August: Setback Schedule 34
81 Hour Reduction! Bigger setbacks 35
Standalone ScoreCard Delivers EE Benefit from Behavioral Modification 2% EE benefit with WBH ScoreCard 2% Measured 1491 houses who received scorecards Aug-Oct Control group of 1950 matched using propensity score matching 36
Streamlined Implementation 37
WeatherBug Home Solutions Intelligent Demand Side Management 38
Thank You! To request a copy of our studies or to learn more how WeatherBug Home can help your organization exceed its EE and DR goals, please email us at: Energy@WeatherBug.com Download the WeatherBug App for FREE! Search: WeatherBug 39