Analytics Forecasting Wind and Wildfire Damage in San Diego


Thirty terabytes of data per day, every day–that’s what San Diego Gas & Electric (SDG&E) collects and analyzes to understand weather across their service territory.

As described in this Intelligent Utility article, SDG&E correlates temperature, humidity, wind speed, and solar radiation data with customer and grid operational data to prevent wildfires, promote public safety, and proactively deploy crews to areas at risk for wind or fire damage.

This correlation of IT, operations, and external data is a clear example of situational intelligence in action. Acting on data-driven insights to proactively deploy crews allows SDG&E to keep customers and infrastructure safe, prevent outages, and reduce the duration of outages that do occur.

Understanding weather is only one area where SDG&E is deploying analytics to make sense of big data in real time. “We expect that analytics will play a role in most or all of our business operations,” says Hanan Eisenman, SDG&E communications manager, quoted in the article. Other areas include IT, security and customer service.


Smarter Capital Spending for Utilities


Between 2003 and 2013, capital expenditures (CAPEX) for utilities in the United States nearly doubled, from $43 billion per year to more than $82 billion per year.

Physical assets form the bulk of CAPEX spending for utilities. To plan these expenditures and deploy resulting assets in a more effective way, regulators, shareholders, utility executives, and ratepayers have increased their attention on CAPEX.

CapEx chart

This means that justifying, planning, and allocating CAPEX more effectively will become an even more crucial management function. Even a one- to five-percent improvement can lead to significant gains in return on investment and the overall effectiveness of CAPEX.

While CAPEX is rising, there is downward pressure on utility rates and revenues, resulting in reduced margins. Energy efficiency, demand response and distributed generation take away load from central generation and distribution, which decreases utility revenues. To keep expenses in line with revenues, utilities need to become more analytical and predictive in choosing how to spend CAPEX. Rather than rely on static, historical analysis to guide CAPEX spending, utilities need to dedicate expenditures to either mitigating expensive risks or taking advantage of revenue-enhancing opportunities.

To manage the complexity of and hence the risk to their operations, utilities need continuous visibility into the asset function with contextual analytics that include normalized metrics. Situational intelligence can deliver this capability for utilities.

Once a utility has situational intelligence applications in place, predictive analytics can guide asset decisions by analyzing the decisions’ effects on CAPEX spending and asset criticality. Knowing not just what conditions are today, but what conditions might be tomorrow, gives a better understanding of the overall risk to the fleet of assets. That, in turn, can guide where and how to best invest capital dollars to improve utility safety, reliability, and efficiency.

For more information on smarter capital spending for utilities, see this white paper.


GIS Day 2014






Happy GIS Day!

November 19, 1999, marked the first GIS Day, established as a way to demonstrate to people how geospatial intelligence improves their quality of life.

You can visit the GIS Day website to find an event near you. True to form, the website features a geospatial map showing the location of events around the world.

GIS is more than just showing information on a map, and can play a key role in situational intelligence by providing  physical location and other data crucial to analyzing and visualizing a situation.

GIS is often necessary, but never sufficient, to providing true situational intelligence.  GIS makes visual the spatial dimension of a problem, but situational intelligence requires analytics of the temporal and network dimensions as well, to provide full understanding of a situation.  For everything visually inspiring and useful that GIS provides, we’re happy to celebrate GIS Day and invite you to join the fun.


The Nature of Asset Risk


It’s a small, simple word: risk. Just four letters. It can be an ominous word, as well—the possibility of bad things to come. Without quantification, it can be a vague word. You might feel that there’s an impending risk, but what exactly is it?

The ISO 31000 standard for risk management defines risk as, “the effect of uncertainty upon objectives where an effect is a deviation from the expected – positive or negative.” This characterization is accurate, but broad enough to be overwhelming. People don’t have the energy and resources to examine every possible deviation. We tend to focus on identifying and mitigating the negative.

For asset-intensive industries, risk can be reduced to two negative components: the probability of asset failure, and the consequences should failure occur.  Both probability and consequence are influenced by such forces as asset age, location, usage, operating condition, adjacent devices and facilities, even weather and the time of day or year. These multiple forces can be in constant fluctuation.

You can think about the duality of risk in terms of a car’s reliability. An old car may be more at risk to quit running without proper maintenance, especially under frequent usage, heavy driving habits and in extreme environmental conditions.  However, an older car failing to run is not comparable to the risk of driving recklessly in a new sports car. Breaking down on the side of the road likely has fewer severe consequences than crashing.

Analyzing multiple, fluctuating forces to uncover and visualize insights is a core deliverable of situational intelligence. When applied to asset risk, situational intelligence provides an approach to quickly and accurately analyze and visualize possible asset failures and their resulting consequences.

Scoring the probability and consequence of failure, either separately or combined into a single risk index, gives asset-intensive industries an objective standard for making decisions and plans that will mitigate and lower risk.

If only you received similar feedback from your car.


New Situational Intelligence Working Group for Utilities


The Utility Analytics Institute recently announced the formation of the Geospatial &Situational Intelligence working group.

With executive sponsorship from CenterPoint Energy and group leadership from PG&E, the new group will tackle four key aspects:

  • Data capture, quality and application
  • Technology
  • Business
  • ROI for geospatial and situational intelligence solutions

SI World 2014 Highlights


SI WorldThe third annual SI World conference wrapped up recently in Newport Beach, California. Representatives from utilities, logistics companies, technology providers, and system integrators from around the world attended to learn more about applying situational intelligence to reduce costs, improve service, and increase safety.

Conference presentations covered a wide spectrum of industries and use cases:

  • Increasing power transmission security in the Czech Republic
  • Optimizing package delivery worldwide
  • Evolving asset management standards in the UK
  • Improving wind turbine maintenance in Texas
  • Analyzing asset risk in Canada

Canadian utility Hydro One took home the Innovation in Visual Analytics award.

The conference was hosted by Space-Time Insight.  Event sponsors NEC, SAP and Unicorn Systems helped make the event possible, including a networking reception for conference attendees.

Save space on your 2015 calendar for SI World!


The Evolution of Analytics at Sacramento Municipal Utility District


Intelligent Utility recently posted an article about the evolution of analytics at Sacramento Municipal Utility District (SMUD).

The article includes a wonderfully concise statement of some of the benefits SMUD has realized from adopting situational intelligence:

At SMUD, as part of our Smart Sacramento initiative, we deployed a situational awareness and visual intelligence software solution that correlates, analyzes and visualizes data in smart grid, distribution and outage systems to improve the decision making across our grid planning and operations department. The real-time data provides SMUD with the ability to collaborate as one team to respond rapidly to emergency situations and outages, and more readily understand the real-time impact of weather, fires and emergencies on our daily operations.

You can read more about SMUD’s approach to situational intelligence, and see a video of their application in action.



Why Situational Intelligence Matters


Situational intelligence plays a role in every phase of a business’s operations, from strategic planning through day-to-day execution to crisis response. Some departments use it to better understand the current or expected performance of assets and resources under normal operating conditions, or to quickly determine when and why abnormal conditions occur. Other disciplines might use situational intelligence to respond more quickly to real-time events to avoid or reduce service disruptions.

The need for situational intelligence has evolved because earlier generations of software were not designed to meet current needs such as these:

  • To be highly responsive to customers and remain competitive, businesses need to consider not only what happened in the past, but more importantly what’s happening right now and might happen in the future
  • The consolidation of data from IT (business), OT (operational) and XT (external) sources is needed to ensure decisions are based on a complete understanding of what has or might occur, and not just on the (potentially incomplete) data from a single system
  • The Internet of Things is driving a massive increase in the volume, variety and velocity of data every day.  New approaches are needed to correlate, analyze and visualize this big data in terms of its scale and complexity
  • Determining that a situation exists or might exist is only part of the challenge; understanding what actions to take, and how and when to take them, must be an integral part of responding to a situation (as opposed to a disconnected set of unproductive and error-prone processes)

Here are just a few examples of how situational intelligence is being used today:

  • Anticipating and responding rapidly to the infrastructural impact of a severe storm to reduce service disruptions
  • Balancing the variability of customer demand with supply in real-time to optimize asset and resource utilization
  • Identifying performance issues over time and taking proactive steps to maintain assets or rectify situational threats
  • Correlating machine data with external systems and events (such as fire, weather and vegetation) to identify system failures before they occur
  • Performing root-cause analysis to determine why assets malfunctioned and triggering remedial actions as a result
  • Prioritizing tasks based on greatest financial impact or risk reduction
  • Predicting the extent of service outages and feeding that information proactively to customers to reduce service inquiries
  • Optimizing service delivery routes and times to reduce operational costs and improve customer service
  • Understanding the downstream or upstream network and service level impacts of an equipment failure

Future posts will examine these examples in more detail.