Analytics Makes Water Conservation More Precise—and Fun!

water sprinkler
Watering sidewalks wastes water.

While human populations shift, migrate and grow, the amount of fresh water remains constant. (The only way to make large quantities of fresh water is desalination, which is energy-intensive and practical only near the ocean.) To serve more people from the same supply of water, communities increasingly are turning to conservation measures.

In times of drought, communities employ water usage restrictions such as banning car washing and limiting lawn watering to certain days of the week or month. These rules rely on observable behaviors that aren’t easily enforced unless the utility employs smart meters or water cops—or both.

Analytics provide water utilities with unique ways to encourage participation in conservation without smart meters and water cops. Here are a couple of examples.

Pinpoint water conservation opportunities

Some utilities offer financial incentives such as loans or rebates for people willing to take the conservation steps of removing their lawn. According to the U.S. Environmental Protection Agency,  landscape irrigation nationwide accounts for nearly one-third of all residential water use, totaling nearly 9 billion gallons per day.  Which customers are likely to participate?

A first step for utilities is identifying the areas where such a lawn removal program can be successful. Geo-spatial analytics identify portions of the utility service territory where home footprint is small relative to lot size, which shows customers with a potentially high percentage of irrigable land. Locating such lots in areas with less tree cover shows yards that may require more watering in hot months. Sorting a list of these houses by the size of their seasonally-adjusted water bill shows you customers who might be motivated to lower their water bills by replacing some grass with hardy, native perennial shrubs and ground covers.

You might want to filter out houses with pools and hot tubs, to prevent those large water uses from skewing your results. Also, you could focus on households without children living at home. These households might lack built-in labor for mowing and be paying a lawn care company as well as high water bills.

Make conservation a game

Gamification applies concepts such as skills, challenges, points and rankings to non-game contexts such as conservation. Gamification brings out the competitor in us and can make conservation if not fun, then at least a little more rewarding and less boring.

One simple conservation game simply shows you how your water use compares with houses geographically close to you and with households that resemble yours in terms of lot size, people in household, and number of bathrooms. Improving your standing relative to those like you earns you points and entitles you to prizes.

Another, more active game may be to progress through levels of conservation skill, knowledge and savings by accepting certain challenges. Challenges could include doing a home water audit using the utility’s online tools, installing free low-flow showerheads from the utility, or cashing in rebates on low-flow toilets. You receive points as you complete challenges and lower your consumption. Analytics show you how you compare with your neighbors who are and aren’t playing the game. Achieving certain levels in the game might entitle you to prizes such as gift cards or bill rebates.

Fairly simple analytics run behind the scenes in these games to award points and keep score. More sophisticated analytics might be used to recruit players for the game and to target the challenges and rewards to more closely match the water saving needs and opportunities of their neighborhood and household type.

(Image courtesy of ewapee / 123RF Stock Photo)


Analytics and Vegetation


vegetation blog

Electric utilities, cable operators, pipeline companies, railroad, municipalities—all will tell you that it’s a jungle out there. Vegetation has a way of interacting with and interrupting the operations of technologically sophisticated and complicated networks. Even your wireless communication networks are not immune to the impacts of vegetation.

Vegetation causes trouble in several ways:

  • Falling onto assets, such as trees falling across roads, damaging them or rendering them unusable
  • Growing into assets, such as roots growing into sewer lines, lowering their performance or making them fail
  • Making contact with assets and causing malfunctions, such as tree limbs touching power lines and causing power outages or sparking fires
  • Allowing wildlife to contact assets and cause equipment failure, such as bushes helping squirrels enter substations and disrupt power operations
  • Obstructing rights of way such as roads, bridges, tunnels and waterways, for example reeds and seaweed clogging ship channels

Similarly, the lack of vegetation can also be a problem. Slopes that have lost their vegetation due to wildfires during times of drought become prone to erosion and landslides when rains finally return. If these areas are adjacent to roads, waterways, power lines, pipelines or other assets that you own or operate, sudden ground movement from erosion or landslide could damage your equipment or block access.

An asset-intensive organization can spend millions of dollar per year on spraying, trimming, pruning, removing and replanting vegetation. Its labor intensive work with costs that add up quickly. When you experience an unplanned event related to vegetation—tree fall, land slide, brush fire—your emergency costs pile up while services are interrupted.

There are vegetation management systems available to organizations today. Maybe you use one. These mainly target the management of scheduled activities, routes and workers. They are useful, and can be augmented to be more valuable by integrating intelligence about actual and potential problems into the scheduling of trim activity. Advanced analytics will identify the areas most in need for trimming or other management and also optimize overall crew schedules so that your vegetation management processes and costs to improve reliability and safety and lower operational costs.

A situational intelligence approach to understanding your vegetation challenges and potential problems maps vegetation’s proximity to your networks, predicts how vegetation will grow and interact with those networks over time, and prioritizes the geographic locations and network sections most susceptible to vegetation problems.

Data about tree and plant species, microclimates, past and future rain fall, time of year, and other variables informs growth models that improve your vegetation management schedules. By applying analytics to this data, you can prioritize your work more effectively to address true problem areas and not just the next assignment in the vegetation management cycle.

By working differently, working smarter, you can optimize your vegetation operations budget and make your networks and assets more reliable.

Copyright: alephcomo / 123RF Stock Photo


Predicting and Managing Urban Storm Runoff to Protect Roads



It’s El Nino time in the West, which means our cities see storm water runoff from heavy winter rains. Large pools of standing water form on roadways where there’s no drainage, drains are clogged, or the storm water system overflows. So much water on and around roads leads to accidents and traffic jams. Water also degrades road surfaces and erodes road beds, creating more hazards and eventually requiring road repair work.

Viewed as a challenge for situational intelligence, storm water and traffic becomes a compelling example of what’s possible. Situational intelligence features analytics of networks and space / terrain across time. Storm water and traffic is a problem of two networks–drains and roads–during the time when heavy rain is moving through the terrain.

Analytics can pinpoint crucial aspects of storm water runoff, such as:

  • Places where roads and drains intersect
  • Road and drain segments that need repair or replacing
  • Bottlenecks in the flow of water and cars
  • Times and locations where rainfall will be heaviest
  • Locations of road and drain repair crews and police

Of course, meteorology continues to benefit from more sensors and computer power, making weather forecasts more accurate and providing earlier, more specific warnings of storm activity.

Once you have all this data and analysis in a single situational intelligence application, you can start to improve storm water operations and planning.

In operations, you could notify police and crews before the storm hits, so that they can be in place to help. You could also notify news agencies and social media outlets to announce what streets to avoid to prevent accidents and traffic jams.

In planning, you can predict areas where runoff will be heaviest. By overlaying this data with data about road and traffic conditions, you can target your storm system investments to places of highest risk for road damage and traffic problems. Taking a proactive approach helps you stretch your operations budgets further while also positioning you well for the next storm.

The West needs the rain to counter the past four years of drought. We’re not complaining. We just need a way to better manage the impact of heavy rains on our heavily traveled roads.



Water, Capital Efficiency and Situational Intelligence


Modern urban wastewater treatment plant.

In a previous post, I discussed the counter-intuitive notion that water utilities can gain more value from their tight budgets by increasing energy efficiency. A more straightforward approach to freeing up money is improving capital efficiency, of which situational intelligence plays a part.

Capital efficiency is much like energy efficiency—getting more results or value from the same resources. Capital efficiency questions a water utility might ask include

  • Where in my distribution network can I get the most leak reduction for the least amount of construction cost?
  • Considering demographics, land prices, construction budgets, and projected future demand, which site is best for a new water storage facility to cost effectively serve future demand?
  • Can I postpone a new water treatment plant for 10 more years without jeopardizing safe, reliable and affordable service?

These complex questions regarding where and when to take action regarding water infrastructure are ideally suited for analysis using situational intelligence, due to three distinguishing features of situational intelligence:

  • Data integration breaks down silos across the utility to represent the full range of capital-related challenges and considerations
  • Spatial-temporal-nodal analytics accounts for the full context of capital investment decisions
  • Intuitive visualizations present the results of analytics in ways that people across the utility can immediately grasp and act on

Capital efficiency is important because water utilities need to invest a lot of money, and soon, to maintain the services that customers expect.

In 2013, the American Society of Civil Engineers’ report card gave the United States a grade of D+ for drinking water and a D for waste water systems. They cite a 2007 report from the Environmental Protection Agency that the U.S. water system needs $334.8 billion invested over 20 years.

Saving just one percent of that amount through capital efficiency would yield $3.5 billion in savings. Those billions would go a long way to keeping water utility margins healthy and water rates affordable as capital expenditures rise to fund needed work. Situational intelligence can deliver that one percent savings by providing fast, reliable answers to specific capital efficiency questions related to repairing, refurbishing, replacing and expanding water infrastructure.

To read more about water and capital efficiency, see this article in World Water magazine.


Augmented Reality for Water Utilities: La Forge into the Future!


Let’s imagine a world where people use Augmented Reality for a moment. Pull up a bean bag chair and we will pretend we’re at PARC. First, we need a story. How about an earthquake in a big city? Hundreds of water pipes can break in a quake. Oakland, CA is a good example. Their average water pipe is 80 years old, with some pipes dating back to the 1880’s .

Enter John, a water utility superintendent in Oakland and ardent “Star Trek” fan, who is in the middle of a busy day in the field when the quake hits. The ground shakes, roads crack, bridges sway, and hundreds of John’s water pipes burst.

He drives a truck, so he can get creative in accessing the sites despite traffic congestion. Besides, he likes off-roading and this is a great excuse to use government property to do such.  He needs to decide quickly which pipes to repair first, but headquarters is without power and thus no help. Where should he direct his crews?

John pulls up his augmented reality app and accesses an interactive tree map to help solve his dispatching problem. Here is a mock-up of John’s view:

Water AR

“Wait,” you say, “treemaps in an earthquake?”

Yes! Treemaps are for real. They provide a fast, visual way to sort information into an easily scanned hierarchy. Scanning spreadsheets or tabular data is difficult and time consuming.

But will people use treemaps?

Ben Schneiderman of Perceptual Edge explores sales, product and even coffee flavor tree maps in a brief paper outlining the effectiveness of this visualization. So we know they are useful in a range of scenarios.

Back in 2010, Marcos Weskamp made a news tree map that demonstrated the ease of scanning and filtering content. He is now Head of Design at Flipboard, the successful web site and app that uses related information architecture to build treemaps for content curation.

Still not sold? Even the big guys are loving treemaps.

Microsoft is adding treemaps to Excel 2016.

At Oracle, the advanced user interfaces division recently published a paper entitled “Enterprise Network Monitoring Using Treemaps.” Study participants using treemaps performed better and were faster than those using tables when:

  • Identifying or counting items
  • Comparing using one or more criteria
  • Doing advanced comparison
  • Performing open-ended analysis

Okay, now back to the story.

John is in his truck in the middle of an earthquake and doesn’t have time to crawl through pages of tabular data when there is so much commotion around him. He needs better, more intuitive tools to help him make fast, accurate decisions. Hence, interactive tables in augmented reality.

Augmented Reality is a great way to represent non visible aspects of reality to support cognition during critical thinking. John can visually filter through the most significant water breakages to minimize the impact of the earthquake on his community.

John can quickly navigate the breakage alerts by population density, risk, electrical asset proximity and more. He filters his list by predicted water loss–Oakland’s in a drought and can’t afford to lose large amounts of water–and immediately dispatches crews to circumvent any further water loss.

But more importantly, John gets to live his “Star Trek” dream of working like Lieutenant Commander Geordi La Forge.


Water, Energy and Situational Intelligence



Although rain falls freely from the sky (except in California, but that is a subject for another post), water utilities consume a lot of energy doing things like pumping water into water towers.

According to the Alliance to Save Energy, energy is among the top three costs borne by water utilities, often coming second to labor. In developing countries where labor costs are low, energy is usually the highest utility cost.

Energy consumption related to water production, distribution and consumption include

  • Pumping from wells, streams, lakes and oceans
  • Desalinating salt water
  • Operating treatment plants
  • Pumping to and from water towers and across elevation zones
  • Maintaining proper pressure in pipes
  • Heating and chilling water for end use

Driving down energy consumption lowers costs for water utilities and their customers, plus provides the related energy utilities with additional kilowatt hours to meet other growing demands.

You can reduce costs by optimizing production and distribution according to the forecasted supplies of water and energy. Because energy prices are dynamic, varying the time that you treat and pump water makes a difference. The fact that water is much easier to store than energy also creates opportunity. You can produce, pump and store water while rates are low, and then distribute it later.

This sort of optimizing requires analysis of current and forecasted supply, demand and cost of both water and energy. Other relevant factors include the weather and the relative elevation of water production, storage and consumption sites. Pulling all these data sources together into a predictive solution that provides actionable insight requires analysis across time, territory and network, a substantial analytical task at which situational intelligence excels.

The Alliance to Save Energy presents several case studies of energy efficiency for water. One example comes from Pune, a city of more than 3.5 million people in India. By focusing on energy efficiency, the Pune Municipal Corporation reduced their energy consumption by 3.8 million kWh per year. That produced annual savings of $336,000.

Capital is often in short supply for water utilities, especially in the US, so the annual savings are certainly welcome. As a bonus, energy efficiency measures also allowed Pune to deliver 10 percent more water without adding any new water production capacity, saving further capital. For a utility, that’s almost like cash falling from the sky.



Waterway Management and Situational Intelligence



When I say “water transportation,” your mind might conjure romantic images of the canals of Venice, Italy, with gondolas and vaporettos ferrying locals and tourists down the Grand Canal. Or maybe you’re the cruise ship type.

But did you know that

Commuters, tourists, trade and cargo move along these and many other waterways around the world, which are vital parts of their surrounding economies.

Like their terrestrial highway cousins, waterways are linear assets. Linear assets pose different challenges from fixed assets, such as power transformers, and mobile assets, such as ships. Highways and waterways share a similar set of concerns about quality, environmental impact, accommodating mixed use, seasonal weather and traffic patterns, and more.

Instead of parts like a machine, linear assets have segments. The size and numbering of segments can vary widely. Characteristics of the asset can change over distance, such as water depth and rate of flow for a waterway. Different characteristics can change at different points in and across different segments.

Plus, there are relationships with parallel linear assets, such as power lines that supply energy to the fixed assets along a waterway. There’s constant interplay between the fixed, mobile and linear assets based on time of year, time of day, weather and other factors. All this change and variability means that analyzing linear assets quick gets complicated.

Situational intelligence applications, with the combination of spatial and network analytics and visualizations, work well for monitoring, managing and optimizing linear assets such as waterways. They offer the breadth and flexibility of data integration, analytics and visualization to handle the complexity of waterways and the myriad other related assets—fixed, mobile and linear. Users can analyze and visualize characteristics in real time across dynamic segments.

I apologize if I’ve ruined your romantic conceptions about water transportation.

(Image “Venice grand canal rialto bridge 2012” by chensiyuan. Licensed under GFDL via Wikimedia Commons)


How Can Utilities Maximize Their Assets?


Electric utilities today are grappling with enormous changes in the way energy is produced, distributed and consumed wrought by renewable and distributed energy sources, smart meters, empowered consumers, changing regulatory models and more.

Accommodating these changes has led to a huge investment in new utility assets that must be integrated and managed alongside a vast portfolio of legacy assets. The range of assets operated by a typical utility spans dozens of categories – from wooden poles to smart meters to high-voltage transformers. To put this in context, the volume of assets a utility needs to manage can add up to tens-of millions within a single operational territory.

To efficiently manage this ever-changing asset portfolio, utilities need insights into how they are used and this requires solutions that bridge the gap between data available via enterprise applications and physical assets in the field. This type of intelligence allows organizations to analyze the data available to know where to invest their time, money, and skills to reduce risk and operational costs.

One example of how utilities gain this type of insight into assets is the new Asset Intelligence 4.0 application. With the latest enhancements, Asset Intelligence gives utilities complete transparency of operational status across the organization, and this ultimately gives them the resources they need to manage their valuable assets and make informed decisions at a moment’s notice.

If you’re curious, read more about the new version of Asset Intelligence.




About Digital Asset Management: Answering Your Webinar Questions


I recently presented an Energy Central webinar on digital asset management, along with Bill Ernzen of Accenture. Unfortunately, the webinar took up all of the allotted time, leaving no time for Q&A with the audience.

Energy Central kindly shared with me the questions that participants submitted during the webinar. I’ll do my best to answer those questions here. I’ve adapted some of the questions to make them work as part of a blog post.


The focus of your talk is based on distribution utilities. How do concepts presented apply to the working environment of transmission utilities?

In the Asset Intelligence section of the webinar, our software demonstrated the capabilities in scoring the condition metric for large transformers and charting the dissolved gas trends over time on a Duval triangle, along with the DGA metrics. Similar capabilities are built into the software for high voltage circuit breakers, tap changers and generation step up transformers, among many other asset types.

Do you see any difference in adoption of digital asset management across gas, electricity or water companies?

All utilities that installed their asset in the 1960’s and 19070’s face a unique challenge, whether they are electric, gas or water. Though gas, water and sewer utilities operate differently than electric, the underlying pain points are very similar. Our software has accommodated their need in how it performs the analysis. The software is structured to perform analysis around business-centered needs, and method applies to electric, gas and water utilities. The method builds from asset health indices to probabilities of failure to risk scores, which are then used for maintenance prioritization, replace versus refurbish analysis and capital planning. The presentation and analysis capabilities also are very similar, except for specific regulations that change some calculations.

Is there any dollar value on what digital asset management can help avoid as ‘risk’ or prevent as ‘avoided cost’?

Putting a dollar value on avoided cost is fairly straightforward. For example, if you can successfully postpone spending money, then you can use your cost of borrowing (weighted average cost of capital) and the inflation rate to calculate the financing costs you didn’t pay because you didn’t do the project.

Putting a dollar value on avoided risk is a little more abstract. Let’s say that an aging power transformer represents an economic consequence of $10 million, should it fail. Assume that, before you start your digital asset management project, that transformer has a ten percent probability of failure. You could describe the transformer’s risk as ten percent of $10 million, or $1 million.

If digital asset management practices directed you to refurbish the transformer to reduce the probability of failure to one percent, then you lowered the risk by $9 million. If your refurbishment project cost you $4 million, then you realized a 225 percent ROI in terms of risk reduction.

How do you define criticality? Aren’t you mixing probability and consequences in your definition of criticality?

In our definition, criticality is the resulting consequence should a failure occur. Consequence can encompass lost revenue from power outage, cost to replace damaged equipment, crew wages to restore power and replace equipment, and other costs. A unique feature of our software is that it runs a connectivity analysis through a topology processor to identify upstream and downstream assets and impacts thereof.

Probability is the likelihood that failure will occur, regardless of the consequences of failure. Probability is based on the age of the asset and its condition, load factor, network relationships and other considerations.

Risk is the product of failure probability multiplied by criticality.

How precisely do you compute your risks?

Where the data for calculating the asset health index, probability of failure and criticality are quantified and precise, our calculated risk metrics are precise.

How do you take “expert feeling” into account?

Our software provides flexibility to customers to tune the asset health index, criticality and probability metrics to match their knowledge and experience by allowing them to modify the factors and weightings in algorithms.

How do you maintain temporal consistency when you have very fast data streams such as PMU and inspection reports which may be once a year?

Our software uses the most recent applicable data in calculations, regardless of its comparative frequency. Where you get into trouble is when you have outdated data, whether it’s monthly data that’s a year old or hourly data that’s a week old. This is why our software computes two additional metrics/indices called Completeness and Confidence. The Completeness index identifies if any data sets were unavailable for computation while Confidence measure if the data sample expected at a point in time was received before the indices were computed. This can be used as an indicator of data quality, data availability or a missed inspection cycle.

Is there a way you can estimate the sensitivity of the risks to your entire system?

Our criticality scores incorporate network connectivity information, and therefore reflect impact on the entire system. Let’s assume that, through an oversight in network design, you have a new, small transformer that sits at the nexus of your entire network and has no redundancy. That transformer could have a very low probability of failure, because it’s brand new, and a sky-high criticality score because it’s the linchpin of your network.


Ajay Madwesh is Vice President of the Utilities Business Unit at Space-Time Insight. He possess more than 20 years of experience in software development and technology management in Utility and Process automation environment, and has spent several years evangelizing the integration of real-time operational technologies with IT. He has previously held leadership roles at top companies such as GE, ABB and Infosys.


Smart Meter Deployment and Analytics: Begin with the End in Mind


Sixteen member states of the European Union are currently deploying smart electricity meters. Five member states are deploying smart natural gas meters.  According to a European Commission report, by 2020, 72 percent of meters across the member states will be smart meters.

2020 is still five years away, and the European Commission had originally targeted 80 percent penetration of smart meters by 2020. Shareholders and regulators don’t want to wait years before seeing a return on the investment in smart meters.

If a country is just starting to roll out smart meters, where should they put their first 20 percent of meters to start realizing benefit? Answering that question demands situational intelligence.

Situational intelligence incorporates spatial, temporal and nodal dimensions into analytics. Spatial and nodal concerns for prioritizing smart meter deployments include

  • Where in the service territory does the meter stand (including proximity to other meters to deploy at the same time (route optimization)?
  • Where on the distribution network does the meter lie (network relationship)?
  • What is the age and type of building associated with the meter?
  • What electricity or gas usage is associated with that meter and building?
  • Is the location, network relationship, building type and usage representative of a class of customer or usage that you might want to study (population sampling)?

Once deployed, a small, early subset of smart meters can provide a rich new data source for other applications such as distribution optimization, demand response, energy efficiency program design, revenue protection and more.

In deploying smart meters, situational intelligence and other analytics projects, it pays to begin with the end in mind. You’re less likely to lose your way and more likely to start realizing returns on your investment.