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Hidden opportunities: managing demand for a more sustainable energy infrastructure.

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Hidden opportunities: managing demand for a more sustainable energy infrastructure.

  1. 1. Hidden opportunities: managing demand for a more sustainable energy infrastructure 9 August, 2017 The YARD, Charter Hall, L20 No.1 Martin Place, Sydney
  2. 2. 2 Welcome Phil Senn, Manager Technical Services, Charter Hall Office
  3. 3. 3 Introduction Craig Roussac, CEO, Buildings Alive
  4. 4. 4 Agenda 1. Context – Hugh Saddler 2. Challenges and limitations of demand forecasting techniques – Cameron Roach 3. Insights from the 2016/17 summer – Craig Roussac 4. How forecasts are being improved – Hao Huang 5. Opportunities and advances for summer 2017/18 – Baden Hughes
  5. 5. 5 Why is Peak Demand important? Capacity utilisation curves for 20 Sydney buildings* * All office buildings >10,000m2, randomly selected from Buildings Alive database.
  6. 6. 6 Why is Peak Demand important? Capacity utilisation curves for 20 Sydney buildings
  7. 7. 7 Why is Peak Demand important? Capacity utilisation curves for 20 Sydney buildings
  8. 8. 8 Why is Peak Demand important?
  9. 9. 9 Why is Peak Demand important?
  10. 10. 10 Why is Peak Demand important?
  11. 11. 11 Why is Peak Demand important?
  12. 12. 12 Why is Peak Demand important?
  13. 13. 13 Why is Peak Demand important?
  14. 14. 14 Context Hugh Saddler Honorary Associate Professor Crawford School of Public Policy, Australian national University
  15. 15. The relative importance of network and energy costs is changing 0% 10% 20% 30% 40% 50% 60% 70% 80% Energy Network "Environment" Contribution of major components to a small sample of monthly commercial electricity bills
  16. 16. 16 Changes in network revenue per MWh supplied Absolute Relative 0 50 100 150 2006 2008 2010 2012 2014 2016 $/MWh Year Ausgrid Endeavour Energy Energex ActewAGL Citipower SA Power Networks 0.0 1.0 2.0 3.0 4.0 2006 2008 2010 2012 2014 2016 Index,2006=1 Year Ausgrid Endeavour Energy Energex ActewAGL Citipower SA Power Networks
  17. 17. Net additions to regulated asset base 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 2006 2007 2008 2009 2010 2011 2012 2013 2014 Index,2006=1 Ausgrid Endeavour Energex Citipower SA Power Networks
  18. 18. 18 Network revenue and electricity supplied (1) Ausgrid Energex 0.0 0.5 1.0 1.5 2.0 2.5 3.0 2006 2008 2010 2012 2014 2016 Index,2006=1 Year Revenue Energy delivered 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 2006 2008 2010 2012 2014 2016 Index,2006=1 Year Revenue Energy delivered
  19. 19. 19 Network revenue and electricity supplied (2) Citipower SA Power Networks 0.0 0.5 1.0 1.5 2.0 2.5 3.0 2006 2008 2010 2012 2014 2016 Index,2006=1 Year Revenue Energy delivered 0.0 0.5 1.0 1.5 2.0 2.5 3.0 2006 2008 2010 2012 2014 2016 Index,2006=1 Year Revenue Energy delivered
  20. 20. Volume weighted average annual NEM prices in mainland regions, $2016
  21. 21. Average monthly NEM spot prices, August 2015 to August 2017 0 50 100 150 200 250 300 Aug-15 Nov-15 Feb-16 May-16 Aug-16 Nov-16 Feb-17 May-17 Aug-17 $/MWh NSW Qld SA Vic
  22. 22. Average weekly NEM spot prices, October 2016 to July 2017 0 50 100 150 200 250 300 350 400 450 500 1 5 9 13 17 21 25 29 33 37 41 45 $/MWh NSW Qld SA Vic
  23. 23. How wind generation affects spot prices: SA May 2016 0 50 100 150 200 250 300 0 250 500 750 1,000 1,250 1,500 1-May 8-May 15-May 22-May 29-May Price($/MWh) Windgenration(MW) Windgeneration(LH axis) Poolprice (RH axis)
  24. 24. 24 How solar generation affects spot price: Queensland, 26 September 2016 Demand Demand and price 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 0 3 6 9 12 15 18 21 Generationanddemadn(MW) Grid demand Demand incl. rooftop solar 0 10 20 30 40 50 60 70 80 90 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 0 3 6 9 12 15 18 21 $/MWh) Generationanddemadn(MW) Solar generation (LH axis) Grid demand (LH axis) Poolprice (RH axis)
  25. 25. How extreme demand affects spot price: SA and NSW, 8, 9, 10 February 2017 $0 $2,000 $4,000 $6,000 $8,000 $10,000 $12,000 $14,000 $16,000 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 0 6 12 18 24 30 36 42 48 54 60 66 Price($/MWh) Griddemadn(MW) NSW demand (LH axis) SA demand (LH axis) SA price (RH axis) NSW price (RH axis)
  26. 26. Wind generation share of region (state) generation by FY (source: AER)
  27. 27. 27 Where might prices go? Base futures contract prices as at various dates NSW Victoria 0 20 40 60 80 100 120 140 160 Q1 2014 Q1 2015 Q1 2016 Q1 2017 Q1 2018 Q1 2019 Q1 2020 $/MWh Periodof contract Feb-14 Feb-15 Feb-16 Feb-17 Mar-17 Actual 0 20 40 60 80 100 120 140 160 Q1 2014 Q1 2015 Q1 2016 Q1 2017 Q1 2018 Q1 2019 Q1 2020 $/MWh Periodof contract Feb-14 Feb-15 Feb-16 Feb-17 Mar-17 Actual
  28. 28. The system is certain to change: Generation capacity by age, 2017 0 20 40 60 80 100 120 140 160 0 10 20 30 40 50 60 Sentoutgeneration(TWh) Age (years) Coal Gas
  29. 29. 29 Challenges and limitations of demand forecasting techniques Cameron Roach, Research Analyst, Buildings Alive
  30. 30. 30 Challenges and limitations of demand forecasting techniques Current methodology • Produces warnings 1 and 3 days in advance • Identifies like-weather days from appropriate training period and extrapolates to predict for extreme weather conditions. Challenges can be broken into three main components: • Data cleaning • Modelling • Validation
  31. 31. 31 Challenges and limitations of demand forecasting techniques Data issues • Outliers • Sparsity of data during extreme weather days • Human activity on extreme weather days. Was there a response? • How reliable is our input data, e.g., weather forecasts? Demonstration
  32. 32. 32 Challenges and limitations of demand forecasting techniques
  33. 33. 33 Challenges and limitations of demand forecasting techniques What type of model do we want to use? • Neural-networks • Support vector machines • Gradient boosting • Random forests • And many more... What data do we want to use? • Recent history • Feature selection – temperature isn't the only driver • How do we engineer features to account for known phenomena, e.g., Monday/post Monday/post holiday effects?
  34. 34. 34 Challenges and limitations of demand forecasting techniques Validation • Need to be careful to avoid overfitting • Cross-validation, train/validation/test datasets • Assess peak demand forecasts with appropriate accuracy measures • Point forecasts: MASE, MAPE, RMSE, etc. • Probabilistic forecasts: Pinball-loss function, Continuous Ranked Probability Probability Score (CRPS)
  35. 35. 35 Insights from the 2016/17 summer Craig Roussac, CEO, Buildings Alive
  36. 36. 36 Peak Demand Action Plan, an example: Three days prior to the peak event: • Ensure AC to all vacant floors is disabled and heating is locked out • Ensure OA sensors are reading accurately • Ensure outside air dampers are in auto and not overridden. Day before the peak event: • Schedule a night purge. • Schedule a one-off early start time and use cool-down cycle. • Encourage tenants to use window blinds. • Lower global temperature setpoints for morning cool-down cycle. Just prior to, and during, the peak time: • Raise global temperature setpoints to summer setting. • Turn down AHU pressure setpoints. • Turn off non-essential lights, pumps and fans • Lock out a lift if capacity allows • Raise CHW temperature setpoints.
  37. 37. 37 Warning three days ahead Peak demand warning for Friday 10 February 2017. Extreme electricity demand is expected to occur between 7:45am - 9:15am.
  38. 38. 38 Two days ahead Peak demand warning for Friday 10 February 2017. Extreme electricity demand is expected to occur between 7:45am - 9:45am.
  39. 39. 39 Critical electricity demand warning for today (Friday 10 February 2017) between 7:45am - 9:30am. Critical warning
  40. 40. 40 Great news, yesterday XXXXX's 30 minute maximum peak demand was 65kVA lower than the peak in the past year. If the peak demand is kept below 492 kVA for the next 12 months, electricity savings of $1443 per month can be achieved. Summary message the day after the event
  41. 41. 41 Insights from the 2016/17 summer 23 24 25 26 27 28 29 30 December January February Mean maximum temperature 2014-15 summer 2015-16 summer 2016-17 summer Long-term
  42. 42. 42 Insights from the 2016/17 summer 16 17 18 19 20 21 22 23 December January February Mean minimum temperature 2014-15 summer 2015-16 summer 2016-17 summer Long-term
  43. 43. 43 Insights from the 2016/17 summer -60% -50% -40% -30% -20% -10% 0% 10% 20% Percentagechangeinpeakdemand Building ID Change in Peak Electricity Demand (from 2015-16 to 2016-17)
  44. 44. 44 Building performance improvement also reduces demand costs
  45. 45. 45 Building performance improvement reduces demand costs
  46. 46. 46 How forecasts are being improved Hao Huang, R&D Engineer, Buildings Alive
  47. 47. 47 Weather forecast Building envelope Calendar effect Building operation change • Timely • Accurate • Reliable • Comprehensive What makes a good peak demand model
  48. 48. 48 Monday prediction: why it is important? Average daily temperature VS Maximum daily demand in kVA Average daily temperature Actualdemandin(kVA)
  49. 49. 49 Monday prediction: why it is important? Average daily temperature Actualdemandin(kVA) Average daily temperature VS Maximum daily demand in kVA
  50. 50. 50 Monday prediction: how do we improve it? Number of day Maximumdailydemandin(kVA) Top 20 highest demand from 2016 to 2017
  51. 51. 51 Dealing with a building’s baseline performance change
  52. 52. 52 Incorporating IEQ data into demand management Precooling? Energy demand Peak demand hour 12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM Thermal comfort index
  53. 53. 53 Opportunities and advances for summer 2017/18 Baden Hughes, COO/CTO, Buildings Alive
  54. 54. 54
  55. 55. 55 Adapted from Harvey Stern, 2017. https://ams.confex.com/ams/97Annual/webprogram/Paper308241.html A: BA’s 16-17 peak demand warning trigger date B: Somewhere between Day 6 and 7, <50% of variance of forecast and observation can be explained. C: Lorenz’s 15-day limit to day-to-day predictability. BA C As forecasts improve, new peak prediction opportunities emerge …
  56. 56. 56 Precision weather data is important …
  57. 57. 57 General thinking about building energy performance prediction …
  58. 58. 58 Business value ? Takeaways … New horizon Business value ? New horizon Business value ? New horizon Higher reliability longer range weather forecasts Earlier peak demand warnings, increased preparation time Higher accuracy prediction and performance analysis Finer grained weather forecasts and observations Generalized building energy performance prediction Pre-emptive strikes and proactive savings capture
  59. 59. 59 Discussion
  60. 60. Level 1, 283-285 Clarence Street Sydney NSW 2000 Australia info@buildingsalive.com www.buildingsalive.com

Editor's Notes

  • Thanks very much Phil. For those who don’t know me I’m co-founder and CEO of Buildings Alive and it’s a great pleasure to welcome so many industry leaders and doers to this forum. Within this room we have almost 3% of Australia’s electricity consumption represented. That might not sound like a lot (or it might sound enormous, depending on your world-view), but the exciting thing for me is everyone here was personally invited because each has the capacity to make a tangible difference to the operation of Australia’s electricity networks.
  • We have an action packed agenda. After a few introductory words from me we’ll hear from Hugh Saddler. Then we’ll have 4 quick-fire 8-minute presentations, then about 20 minutes for some discussion and perhaps debate with the formal proceedings wrapped up by 3:30. That will give us ½ hour for those able to stick around for informal discussions. Sound OK?
  • This is what a “Capacity Utilisation Curve” looks like. In fact, it’s what CUC’s from 20 randomly selected Sydney office buildings looked like in 2013/14 before Buildings Alive started working on some of the methods and technologies we’re going to be introducing today.

    There are 8,760 hours in a year. Peak demand is measured in ½ hour blocks, so those blocks are sorted from highest demand to lowest and divided by the floor area of each building to give a common base for comparison.

    You’ll see a dip at about 2,800 hours which corresponds to the typical annual service hours of a typical office building. Different building types have different Capacity Utilisation Curves, I’m just showing office buildings to keep it simple. Labs, hospitals, supermarkets and the like have much higher demand per m2 and in many cases they have significantly larger savings opportunities.

    Notice the sharp point sticking up at the extreme left. We almost always see something like this, no matter what type of building and no matter where it is.
  • Let’s zoom into the typical service period: 2,800 hours / year. That hocky stick is still clearly evident.
  • And here are just the 500 hours with highest demand. Looking a lot flatter, as you would expect. But lets cut a slice through and separate the 10, 5 and 2 highest demand hours. That is, let’s look at what it would mean for the cost of operating an office building if the demand for the 10 worst hours in a year could be restrained to no higher than the level measure at the 11th, if the top 5 were no higher than the 6th worst, and if the top two hours were no higher than the 3rd highest for the year. Bear in mind with all of this that I’m just looking at some relatively well and carefully managed office buildings. If you’re thinking about hospitals, or labs, or supermarkets, or office buildings that don’t have high-calibre full-time dedicated management, you should be thinking considerably bigger.
  • 9.5% of electricity supply capacity for these 20 buildings is called on for less than 10 hours per year. That is to say, almost 10% of the average building’s demand occurs for 0.1% of the year. As you can see, for a few buildings that 0.1% of the year is what demands 20% of the entire capacity they require. We have seen it as high as 30%
  • The capacity needed for just the worst 5 hours is just under 7%. I.e. 7% of the capacity, and hence 7% of the typical office building’s capacity charge is needed for just 5 hours per year or 0.05% of the time.
  • So what does it mean in dollars? If a facilities manager was able to keep demand for those 10 hours at the same level as the 11th highest-demand hour for the year—that is to say still at a very high level—then that building’s net income would improve by $65c/m2. Some buildings would save more than a dollar and in one case… 2 dollars per square metre! For a fairly typical office building you’re talking about a huge amount of money. And bear in mind, we’re not talking about energy efficiency savings here—the thing Buildings Alive mostly helps with—just Capacity Charges. (Happy to talk about the virtues of energy efficiency any time – just not today )
  • But let’s say that’s too hard. How about if the building just trimmed the five worst hours down to the demand level of the 6th worst hour for the year. The 6th highest demand hour is higher than 99.95% of the year… 46c/m2 saving! Building 2, assuming it’s about 25,000m2 which I think it is, would cut operating costs by almost $40,000/year! We’re not talking about turning things off – we’re just talking about avoiding going over the level set on the 6th worst hour of the year – keeping it within the 99.95%.
  • But lets say we don’t want to do that! Even if we just shaved the 2 highest demand hours back so they were the same as the 3rd highest demand hour for the year – the average saving would be $0.28/m2. Almost $10,000 year on a largish office building or shopping centre. $10k to avoid a highly-avoidable mistake. As I said before, the savings in higher intensity buildings like labs, hospitals and supermarkets… it would likely be much higher.
  • And whilst it’s true the opportunity for many is quite small at the 2 hours / year mark, look at these ones in blue. In the case of building 10 – those 2 hours, if they could be just shaved back to the level of demand on the 3rd worst hour… Building 10 would save about $30,000!

    Who is aware of these opportunities? When do they occur? How can you anticipate them? If you could anticipate them, what would you do? Would you be ready? And if you achieved a saving, how would you know? How can you measure and quantify your success in avoiding something that never actually happened – because you actually stopped it from happening?!?

    Obviously this stuff is not easy – or the opportunities would not exist. We’ve invested something equivalent to 6 PhDs for a year so people running buildings can know what they need to do … and when … to stop this waste. So they can make the decisions for themselves.
  • Enough of my crass reduction to financial terms of what is actually one of the most important issues facing our community and our economy. Obviously there are enormous financial returns sitting untouched by building owners and operators. They shouldn’t be wasted. But blackouts kill people and they grind the economy to a halt. Infrastructure comes at a massive cost and we need our infrastructure, like our buildings, to get more agile – not more of the same.

    Dr Hugh Saddler is honorary associate professor at ANU’s Crawford School of Public Policy and director of consulting firm, Energy Strategies.
    In the early 80’s he literally wrote the book on Energy in Australia called, quite logically … Energy in Australia … and his ability to see and articulate clearly what others fail to notice is what’s made him one Australia’s most highly respected energy analysts and commentators ever since.

    Hugh has been all over the media in recent months with the community hungry for explanations for how our energy infrastructure got into the mess it’s in, why our energy prices are so high, and what’s in store over the years ahead. So it’s a great privilege to welcome Hugh to provide us the broader context for this discussion about peak demand management opportunities. He is a walking embodiment of someone who could not be more committed to a more sustainable use of energy and we are very grateful he could be here.
  • Next up, Cameron Roach. Cameron is a research analyst with Buildings Alive but actually spends most of his time based out of Monash University where he is completing his PhD in applied statistics. From our point of view, this offers the best of both worlds – he works on incredibly challenging real world forecasting problems and is guided not only by us, but also by world-renowned experts including his principal supervisor Professor Rob Hyndman. Prior to joining Buildings Alive, Cameron worked as a forecasting analyst with the Australian Energy Market Operator (AEMO). Cameron.
  • Between Cameron’s short overview of the complex challenges involved in forecasting peak demand, and Hao’s demonstration of how some of them are being overcome … you have me again for a quick summary of what we and some of our clients did last summer, and how it went.

    Our peak demand service has three components: 1) planning, 2) warning, and 3) M&V (or evaluation)

    The planning part involves our engineers sitting down with the building FM well in advance of the hot weather striking and helping to develop a peak demand action plan. This plan is what’s put in place when a warning arrives.

    The warning, which I’ll illustrate in a moment, comes by way of a series of emails: the first a general ‘heads up’ a few days in advance that demand is likely to be within the top 10 peaks recorded over the past 12 months, and hence could potentially reset the building’s capacity charge if not managed carefully. This is then followed each day in the lead-up culminating in a critical peak warning on the morning of the day in question.

    Then the day after the event we issue a summary of what happened. The data behind this is what we use to continuously refine what we do.

    I’ll show you a sequence of messages for a typical building on one of last summer’s peak days.
  • Every building’s peak demand action plan is going to be a little different. In some cases, such as Charter Hall’s, the plans are actually written into the BMS control. Here are a few examples of the kinds of things we commonly see in office buildings. Opportunities are different in retail, hospitals, labs and the like. I won’t go into details, but often there will be upwards of 20 things identified.
  • Summer 2016-17 was the warmest on record for Sydney Observatory Hill with the mean daily maximum temperature 2 °C higher than the previous year.
  • Summer 2016-17 was the warmest on record for Sydney Observatory Hill with the mean daily maximum temperature 2 °C higher than the previous year.
  • Half of Sydney office towers we’ve been helping to manage demand have achieved lower peaks than they recorded last year.

    The active response rate was 35% and the average response was 1.8VA/m2 over ~2 hours.
  • FM managed to reduce its peak demand from 678kVA at the start of REF service, to its current peak of 466kVA.  This equates to savings of almost $2,300/month !
  • FM managed to reduce its peak demand from 678kVA at the start of REF service, to its current peak of 466kVA.  This equates to savings of almost $2,300/month !  
  • Next up, Hao Huang. At Buildings Alive, Hao is both a building systems engineer working directly with facilities managers to help them optimise resource efficiency on a daily basis, and also an R&D engineer grappling with some bleeding edge data science. This is the new model for our R&D investment because we find it helps us ground our research in really practical challenges. Hao completed his PhD at the University of Adelaide where he developed model-based intelligent control technologies for optimising the operation of buildings. He has heaps of other qualifications which we don’t have time to go into. Hao.

  • General speaking, a good peak demand model should be timely, accurate, reliable and comprehensive. A timely prediction ensures accurate warnings can be sent out in time, so that our clients have enough time take corresponding actions. It should of course be accurate, but not looks accurate on one day but less accurate on other days It should be flexible and comprehensive enough to handle different types of uncertainties (not a black box).

    Building envelop is a complicate process to model, because of thermal mass are different from one building to another. In the last four years, we have built very sophisticated technology to model the correlation between weather condition and building envelop. The peak demand forecast accuracy has also greatly benefitted from continuous improving weather forecast accuracy. The day-ahead prediction

    The main challenges of modelling building comes from the change of building performance and various operational schedules. These factors have big impacts to the modelling results but are less straightforward to be modelled with. For example, how would weekend and post-holiday effect the prediction accuracy? How to guarantee a good prediction accuracy after buildings’ performance has been improved? We are currently developing new modelling framework to address the issues and expected to observe big improvement in prediction accuracy after it is finished.


  • From the second figure here, we can see that most of these days are Mondays. We all know that due to thermal inertial, buildings accumulate heat load during periods when HVAC systems are not operating. This load can greatly increase the total cooling demand for the following workdays. The stored heat load effect becomes more significant after weekends and holidays.

    This suggests more accurate prediction of the stored heat load effect may introduce benefits for building owners by helping them to better manage electricity demand. This is very important, because missing one day prediction could end up resetting the demand costs, and Mondays with are the days that could be ignored. We are developing better models with ability to predict peak demand events on days following warm weekends or holidays with a much better accuracy.
  • From the second figure here, we can see that most of these days are Mondays. We all know that due to thermal inertial, buildings accumulate heat load during periods when HVAC systems are not operating. This load can greatly increase the total cooling demand for the following workdays. The stored heat load effect becomes more significant after weekends and holidays.

    This suggests more accurate prediction of the stored heat load effect may introduce benefits for building owners by helping them to better manage electricity demand. This is very important, because missing one day prediction could end up resetting the demand costs, and Mondays with are the days that could be ignored. We are developing better models with ability to predict peak demand events on days following warm weekends or holidays with a much better accuracy.
  • From our analysis of the demand profiles of over a hundred large Australian commercial office buildings, we note that commercial buildings reset their peak capacity charges more often on Mondays than any other day of the week. From the figures, we can see the high demand does not only happens on high temperature days, but more commonly happens on Mondays, when the day time temperature was not that extreme.

    This suggests more accurate prediction of the stored heat load effect may introduce benefits for building owners by helping them to better manage electricity demand. This is very important, because missing one day prediction could end up resetting the demand costs, and Mondays with are the days that could be ignored. We are developing better models with ability to predict peak demand events on days following warm weekends or holidays with a much better accuracy.
  • Another important factor to consider is the constantly changing buildings’ energy performance. For example, the building in this graph has a big chiller upgrade which has greatly improved the building’s energy performance. So the model built before the updating becomes less accurate so that it cannot be used to perform demand forecast.

    In building’s alive have created a adaptive model with rolling window as an indicator of “most recent day performance”, and then find the relationships between weather conditions and energy profile of these recent days. This means we keep learning building’s performance change and make sure the model reflects the most recent status of the building. Further, we have recently developed a hybrid modelling approach, which can greatly improve the model’s robustness by taking into account the like-day performance. In this example graph here, we could see that the hybrid model can predict the maximum demand in a much better way.
  • After receiving peak demand warning, one most common technology is pre-cooling. However, the precooling may cause cold complaint in the morning, and hot complaint in the afternoon.

    It is possible to use the information of IEQ data to reduce the demand costs and maintain the thermal comfort index within comfortable range.
  • Next up, Baden Hughes. Baden is a co-founder of Buildings Alive and among other things serves as our Chief Technology Officer which means he has strategic oversight of our software/technology, research & development, and service delivery. Baden has “architected” (if that is a word) our core Rapid Efficiency Feedback—“REF” for short—platform from its inception more than 5 years ago. He knows everything there is to know about a whole lot of complicated computer stuff I can’t even pronounce and at one stage was the University of Melbourne’s Chief Information Architect. Baden’s going to share some of what’s coming up for our clients heading into this summer and hopefully that leads us to a bit of a discussion about what it all means in application. Baden.
  • Thanks for staying with us so far!
  • Most of us simply take it for granted that weather can be forecast with some accuracy several days ahead.

    But how many days can make a difference to what can be done with the information.

    The state of the art for predictions of maximum temperature are shown here in work adapted from Harvey Stern (Australia’s leading forecasting meteorologist).

    We can see that there is potential opportunity to realise additional value by using longer range forecasts.
    In 16-17 (and earlier seasons), BA’s peak demand warnings were triggered on the basis of an hourly forecast available up to 72 hours in advance.
    But there’s arguably reliable enough forecast data to be able to make these warnings much earlier – even 6-7 days in advance.

    From talking to a number of building operators, the difference in getting a ‘heads up’ between 3 and 5-7 days in advance is significant.

    Why is the extra lead time important ? It allows:

    Programmatic and managed response, rather than quick reaction.
    Cumulative preparation over multiple days, rather than “whatever we can do 24-48 hours in advance”.
    Greater potential to engage tenants, not just BMS/engineering contractors.

    In 16-17 we experimented with a simple early warning system that focused on identifying “peak potential” conditions 5-7 days in advance based on longer range forecasts.
    We found a strong positive correlation between our ability to predict a peak demand event 5-7 days out, and the peak demand warnings being generated 1-3 days out.

    In 17-18 we are extending this in conjunction with a general consumption prediction capability, which will allow us to project building energy consumption (whether peak or not) up to a week in advance, based on forecasts.
    Our intention is to link these predictions to a new form of year-round, ongoing feedback to building operators, moving from hindsight (what happened and how did it compare to what we thought was reasonable) to foresight (what do we expect to happen, and how can we influence those expectations). That is, moving from a peak demand specific service to more general demand management capability.
  • The precision of weather data is critical in accurate peak demand prediction and also in general performance analysis.

    Consider the case of 10 February 2017, the hottest day in Sydney in the 16-17 summer when it reached around 43 degrees.

    The graphic shows the track in the forecast and observed temperature for three Bureau of Meteorology stations in Sydney – Sydney Airport, Observation Hill and Olympic Park.
    It also shows the Outside Air Temperature (OAT), as observed by a local sensor attached to the BMS at 3 CBD buildings where Buildings Alive delivers services.

    You’ll notice there’s quite some differences both between forecast and observation at the BoM stations, as well as a significant difference between the BoM Station observations and the BMS OAT observations.

    So what are we doing about this ? I’m glad you asked …

    First, we’re experimenting with the use of finer grained temperature forecasts in terms of time. We’ll continue to run our hourly forecast peak demand prediction algorithms this summer, but in parallel we will also look at the differences in peak prediction (and actual performance post-event) when using a 15 minute forecast.

    Second, we’re experimenting with the use of closer proximity temperature forecasts and observations. We’ve got access to forecast and observation data down to 500m grids Australia wide (rather than variable 2-10km distance from a BoM station). And we’re looking at better informing peak demand (and general consumption prediction) using a very local source – either the BMS OAT, or potentially a local weather observation device on top of a building.

    Third, we’re correlating inside and outside conditions, against energy consumption. The reason were are interested in thinking about inside conditions is because tenant comfort is a significant factor (as many of you realise, there’s a tradeoff between energy costs and consumption rates and tenant complaints – you can save lots of money at the cost of dealing with many complaints about floor temperatures!).

    Where do we expect to find new value ?

    The closer we can access weather data (either forecast or observations) to the location that energy is actually consumed, the more accurate our peak demand (and general consumption) prediction will be.
    And the more accurate our performance analysis will be in hindsight.

    For our building owner and operator partners this will translate to greater confidence, both in taking actions to avoid peak demand charges, as well as in verifying the benefits of changes and investments.


  • Buildings Alive’s analytical services have been based primarily on comparing “recent history” (ie yesterday) against “older history” (ie the last year or more) in terms of understanding building energy performance.

    Hindsight has only so much value though.

    Foresight – identifying what we think is going to happen, before it does, and understanding what options there are to change that in advance – offers a different opportunity to create value.

    Our peak demand service, using forecast data, is just one example.

    Over 17-18, we’re taking what we have learned in peak demand prediction, and building a more general building energy consumption forecasting capability.

    This will underpin a new form of year-round, ongoing feedback to building operators, moving from hindsight (what happened and how did it compare to what we thought was reasonable) to foresight (what do we expect to happen, and how can we influence those expectations). The opportunity to make a pre-emptive strike in preparing a building’s energy management infrastructure against adverse conditions, or to capture a savings opportunity, we believe will create further value for our customers. Combined with our Measurement and Verification capability to audit and attest savings, we believe this will offer a compelling new service for building owners and operators.
  • So in summary, during summer 17-18 we’re actively working not only on  delivering an improved peak demand service, but looking at a wider range of opportunities.

    Higher reliability, longer range weather forecasts will deliver earlier peak demand warnings, with increased preparation time for building operators.

    Finer grained weather forecasts and observations will allow for higher accuracy prediction and performance analysis of building energy consumption.

    Generalised building energy performance prediction will inform owners and operators of opportunities to be proactive to avoid costs and capture savings.
  • Good building manager can improve 1 star

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