This year we’re going to give an update on what’s going on with energy analytics tools & take a deeper dive into some tools Capabilities vary widely including:Yr over yr comparisonsBenchmarkingEnd-use disaggregationAnomaly detectionRetrofit suggestionsCreating normalized baselinesEnergy & demand predictionWhat all of these products have in common is that they are designed to accept data regarding multiple end-uses within buildings, and process that data in order to inform efforts to improve energy performance. Point out that they can be a part of a BAS or can rely solely on utility data (or other data)
This is a conceptual representation of how they workAnalytical engine accepts all kinds of data regarding:Total energy useSub system energy useWeatherOccupancyProductionShipping schedulesStatistical analysis (regressions) is used to arrive at normalized baselineSome tools use regression to mathematically disaggregate energy consumption by end-useOthers use submetered data to show sub system level energy useOther capabilities include:Benchmarking (compared to other similar buildings in vendor database, compared to other buildings within your portfolio, using Energy Star benchmarking process)Could also use energy modeling software (eQUEST, Energy + to create a “baseline” to compare energy use to, but no one we know of is doing this yet)Benchmarking of disaggregated data to find which system(s) are most inefficient/use the most energyComparing building energy use to:Energy use of same building from previous yearsPredicted energy use of same building (using baseline)Predict savings from EE measures and do economic analysisAnomaly detection including controls/scheduling probs (ie lights on at night)
- In fact, I’m using Pinterest to keep track of them all
The research being done on these systems can help you to understand The differences between various offerings (LBNL matrix)The energy savings associated with particular offerings (in specific implementations – case studies)Future work from LBNL will help you assess cost/benefit ROI of some of these tools
Vendors claim up to 30% energy reductionsNot unreasonable considering that retro-commissioning can save 10% – 20% in existing buildingsYou’ll notice that lighting retrofit is listed as an “action” resulting from EIS implementation in UC Berkeley case studySo 30% reduction in energy use includes retrofitConfusion in the industry about whether retrofit savings should be attributed to EISSomething to think about/ask about/watch out for when considering EISs & their ROI (lighting retrofit obviously had an additional cost)
Cascade Energy has worked with the likes of PacifiCorp, Portland General Electric, Northwest Energy Efficiency Alliance (NEEA), Sysco, BPASENSEI has standard features includingEnergy consumption /demand visualizationCreation of a normalized baselineCarbon accountingTrack savings from specific project implementationsCreate alarms/Anomaly detectionRoll up data for multiple facilitiesCan contract with Cascade Energy to have their engineers analyze data and find energy saving opportunitiesCurrent users includeKrogerBen E. Keith Co. (distributor of Anheuser-Busch products)
Cold storage facility example?Here you see daily energy consumption profile for a manufacturing facilitySENSEI is capable of taking into account a large number of variables when normalizing the baseline energy use model as you’ll seeI’ll note that their regression methodology is compliant with ASHRAE guidelines & thatBonneville Power Administration & Energy Trust of Oregon both accept baseline for purposes of evaluating savings numbers
- Here you see daily energy consumption with production overlaid. Production (widgets produced per hour) is accounted for in the normalization.
- Of course weather affects energy use. Here you see the daily energy consumption with the daily outside temperature fluctuations overlaid. This is another variable they take into account.
- Shipping schedules can have an effect too.
- Finally they arrive at a normalized baseline – you can see that model (in red) fits pretty well.
What is that baseline used for you might ask?To track energy savings from specific actionsHere vertical lines represent “events” – major facility tune-up, equipment retrofit, etc. An energy manager puts these events into the tools.The numbers represent “actions” – changing the suction pressure for a freezer, the temperature setting for a walk in cooler, adjusting fans, repairing valves, etc., even behavioral things such as starting a campaign for employees to use the stairs instead of the elevatorsYou do this with the hope that you can achieve…
This!Here you see that cumulative savings are increasing over time – cumulative savings getting larger over time and amount saved per year (slope of line) is increasing as well.
Another feature (among many) is the ability to track energy usage by subsystem (using submeter data) versus budget
Grid Navigator is a different kind of a company – core competency in controls with some analytics modulesThey offer:EMS (metering & controls system with fully BACnet integration)Looche Lighting System is LED based dimmable lighting system (hardware & controls) – integrates with EMS for indoor & outdoor applicationsSunmeter – shows solar (& other renewable energy system) production & can also forecast production – integrates with EMS – can design load shed events around predicted production. Also include fault detection, benchmarking & auditing of systemEnergy & water dashboard solution is called MeterBook – uses utility data - $745 (looks like a one time fee, but that seems too good to be true)Smart thermostats – integrate with EMS systemForecasting & analytics including:Real-time benchmark alertsEnergy consumption /demand visualizationRoll up data for multiple facilitiesCreation of normalized baseline (which is what they use to forecast demand)Track savings from specific project implementationsCarbon accountingSimilar to Cascade Energy, they also offer facility management servicesThey claim up to 20% savings from EMS/Analytics & paybacks of 18 – 24 monthsCurrent users Edmonds community collegeGM Auto dealershipsWashington State Department of TransportationAlgorithm developers came from the financial world where they’re always creating regressions to predict future They claim 97.2% accuracy with demand forecasts
Algorithm developers came from the financial world where they’re always creating regressions to predict future They claim 97.2% accuracy with demand forecasts
Transcript of "Building Analytics: Energy Information Systems"