Between the Poles

Automated feature recognition helps democratize utility mapping for producing infrastructure digital twins

In jurisdictions worldwide the low quality of underground location data has been identified as a top cause of underground utility damage. Existing underground utility data, most of which is stored in network owners' records, is often inaccurate, out of date or simply missing.  In North America in a survey of 402 locate professionals better facility maps received nearly unanimous agreement as a top strategy for improving the efficiency and quality of locates.  Singapore's Digital Underground project is unequivocal about the economic and social impact of low quality underground location data stating that "virtually all stakeholders are aware that much of the available information is unreliable and that this has repeatedly led to losses of time, money and opportunities."

Challenges facing the utility mapping industry

The root cause of the low quality of underground utility data is how utility mapping is structured as an industry. The primary activities of the utility mapping industry are data collection and how we keep and share this data with other parties.

Data collection

The way we collect is slowly changing, but predominantly we're still using traditional survey and locate methods. The survey methodology we are using hasn't changed in a hundred years. It's a very time and materials based, manual and analog process. Survey processes are slow and expensive to execute and require specialized equipment and skill sets, and safety is a big concern.

Locating underground utilities is one of the biggest causes of delays in construction schedules, service disruptions are a common occurrence, safety is always a major concern. and locate operations are slow to execute. Accuracy and timeliness are major issues. Locate operations require specialized equipment and skill sets which restricts locators to a small number of industry insiders.  A major shortfall is that underground utility location data is rarely available during the crucial planning and design stages of civil engineering projects.

Democratization of utility mapping

The utility mapping industry needs some fundamental changes. We need to move away from the monopoly that exists right now. There is just a handful of companies that are able to go out and collect underground utility data. We need to democratize data collection so that many more people can collect this data, targeting a crowd sourced approach and enabling multiple creators or collectors providing this data. This distributes the responsibility for the data as well. The more distributed the responsibilities, the lower risk and the lower the cost of failures will be.

We also need to change the skill level required. With current technology, we should be able to automate much of routine locating and thus enable many more people to do it. Using inexpensive technology will lower the cost of collecting and aggregating locate data. As data becomes lower cost to capture, data retention will become less expensive which will encourage people to retain the location data they acquire rather than simply painting/marking the ground and not capturing the data digitally.

Democratization is the real future of 3D infrastructure mapping. Right now data is still very siloed. We'll always have the traditional mapping companies, survey companies, line locating companies and asset owners going out and collecting their own data. But through crowdsourcing data from routine day to day locating and mapping, infrastructure projects, and other sources, aggregating that data and distributing access to that data is how utility mapping will really come to the forefront.  It should be easy to upload data into a central register where it can be made accessible to multiple stakeholders. Compliance with Open Geospatial Consortium (OGC) standards provides a basis for full interoperability enabling open access to every single conduit, cable, pipe, or other piece of infrastructure in real time from your phone, your tablet, your laptop or other applications.

Infrastructure digital twin

We think the best paradigm for producing and capturing asset data is through collecting it into an infrastructure digital twin. Digital twins have made a lot of progress in a variety of industries. Most importantly, for the utility mapping industry, it should be noted that creating a digital twin is much easier than it was even just a few years ago. We think the time is right for an infrastructure digital twin and it can be just at the asset level for one particular company, but we believe it should be at a regional level. Cities, municipalities and regional governments around the world are beginning to develop comprehensive digital twins of all of their physical infrastructure; buildings, roads, trees, power lines, and subsurface utilities and they're pouring resources into it.

And we think that Canada and the United States should start doing the same thing. And for us in the utility industry, data collection costs are lower than they ever have been. There's clearly interest among stakeholders and governments to start dealing with infrastructure and utility mapping problems, particularly to lower the number and costs of strikes, injuries and the loss of life. We now know a lot more about how to create data from other data sets, using different types of analysis and artificial intelligence to piece disparate data sets together. Using AI machine learning in the cloud is much less inexpensive compared to just a few years ago. And nowadays one of the things that makes infrastructure digital twins readily accessible to the utility industry is the fact that storing huge volumes of data in the cloud has become very inexpensive.

What is infrastructure digital twin ? First of all, a digital twin needs to result in effective action. It's not just enough say we have a digital twin philosophy The purpose of having a digital twin is to result in effective action that improves your business. It needs to use real time and historical data, which is paramount for us in the utility mapping industry, and it's going to be motivated by outcomes. For us in the utility industry, it is to reduce and even eliminate utility strikes during construction and the associated risks of injuries and fatalities.

The infrastructure digital twin has to be stored and managed in a relational database so it can be queried, analyzed and improved on through time, and related to other data sets. Processes for collecting data and maintaining data must be simple so that we can break the barrier of restricting underground utility mapping to a specialized group of people. If we truly want to democratize and compile data from disparate data sets, we need very simple processes to get data into the database, analyzed and maintained.

An infrastructure digital twin should be captured and maintained in 3D, even though most utilities in the ground are within just a couple of meters of the top of the ground. The 3D component is vital, because part of analyzing a data set is that much ancillary data is 3D such as overhead power lines that come down the side of a pole into conduits below the ground,

Imagery that's taken looking straight down at the ground, like traditional satellite imagery, can be visualized on a map and incorporated directly into a GIS database. But other types of imagery such as oblique, 360-degree, street-side, and inspection imagery need to be treated differently.  Oriented Imagery has a range of possible applications. For example, a construction company might use it to review asset inventory photos taken by field workers at a construction site. A telecom company or electric utility would use it to examine close range inspection images of electrical and telecom towers from many different angles. Oriented imagery applications enable you to see exactly where the image is on the map as you pan or zoom in the image. There are spatial navigation tools designed to work specifically with oriented imagery. You can query oriented images using spatial and temporal filters.

Finally, it is very important to have standardized formats, including storage formats and cloud formats.  It improves our ability to inexpensively use machine learning to do associative analyses of disparate data sets to make a larger, more conflated data sets.

Automated feature extraction

Maintaining accurate, up-to-date, comprehensive data about assets, above ground and below ground, has been a challenge for utilities for decades. Now new technologies including machine learning are being applied to automate real-time maintenance of utility network information and provide a foundation for infrastructure digital twins.

Automated feature extraction has advanced dramatically in the last few years.  This is an example where high density LiDAR and ultra high resolution imagery was used on a very low, very small diameter gas distribution install from an open trench. You can clearly see the ease with which the LiDAR produces a 3D representation of all of the local utility infrastructure.

Much of the the advancement in in automated 3D mapping have derived from the autonomous vehicle industry which have trickled down to other parts of the GIS industry.  The hardware and software are vastly easier to use and produce vastly better data. You can see from these ortho images collected from our system and the LiDAR overlaid on it. Feature extraction was used to automatically define top of pipe, top of ditch and actual joints in the pipe. Automated classification of LiDAR removes the need for someone to go out there with a Pogo stick and find the top of pipe. It makes it possible to simply drive alongside and scan with a mobile LiDAR system an open trench containing a newly installed pipe to produce a detailed 3D model with centimetre accuracy without having a person present. It is much safer - no boots on the pavement - and it drastically reduces the cost.

We prefer mobile mapping systems as opposed to handhelds because mobile mapping systems produce a tremendous amount more data and its registration tends to be vastly superior. It is much safer because it eliminates a need for someone to get out of a vehicle - so no boots on the pavement. Anytime you're still getting out, whether you're using handhold laser handhold mapping system, or a Pogo stick, you still have a person outside the vehicle walking around a street. With a mobile system you eliminate that risk, and you also drastically decrease the time required to do the collection. Here is another example where automated feature extraction was used to identify the electro fused coupling. You can see the butt joints, you can see the Ts and so forth. All this can be done very simply now with automated feature extraction.

Data registers

While surveyors may have had experience with a provincial or other types of survey register, utility location data identified during routine locate (one call) operations in general is only used to mark the ground and is rarely recorded, digitally or otherwise. Practically this means locators going back to the same location they were at just a few weeks ago to repeat the locate operation for another project. People in the locate industry are very familiar with this and the  amount of schedule and cost expense it causes. Exposing people to relocate or remap an area that was just done not too long ago is doubling their risk. Secondly, because the data is not retained or kept in easily accessible ways, we can't analyze it.  For example, we might want to confirm that we have correctly identified the type of utility, the accuracy of its location, or determine if there have been changes in its location, type, or current status since we last detected it. And because the data is not being retained and centrally managed at a global level or at a large scale level, there's no real opportunity for organic data enhancements or improvements.

The CGA Next Practices Report to Industry has made the case that a comprehensive national GIS map of buried infrastructure is among the opportunities for systemic improvement with the greatest ROI for industry. The benefits of a national register are greater safety, enhanced construction efficiency; reduced design, construction, and maintenance costs; greater regulatory compliance; improved locate efficiency; fewer project delays and budget overruns; fewer service disruptions to the public, less traffic disruptions and loss of custom and other inconveniences for the public.

Sharing maps of underground infrastructure is not totally new to Canada. For example, the City of Calgary's Joint Utility Mapping Project (JUMP) system, the ICI Society in British Columbia, the City of Toronto's Digital Map Owners Group (DMOG) and others have been sharing underground utility information for years.

The Canadian Underground Infrastructure Register (CUIR) is a new national initiative started at the beginning of the year. The CUIR initiative is intended to enhance current practices in a way that enables all stakeholder groups to benefit from the initiative; construction contractors, network owners, government transportation agencies, design engineers, subsurface utility engineering (SUE) engineers, surveyors, locators, workers in the field and the public. The CUIR initiative has been inspired by the National Underground Asset Register (NUAR) project currently being implemented in the UK. The objective of NUAR is to develop a register of location data for all underground infrastructure in England, Wales and Northern Ireland that is accessible to all network owners and government agencies.

This post is based on Joseph Hlady's (Lux Modus) talk at Subsurface Utility Mapping Strategy Forum (SUMSF).

Eliminating the as-built backlog by automating field utility data capture

The as-built update backlog is the time it takes for as-builts submitted from the field after completion of construction for newly installed network equipment to get entered into your utility GIS.  It is generally measured in months which is appalling today when we have real-time or near real-time data about so many aspects of the real world from traffic to weather.

Field sourcing

The key to solving this problem is to enable crowdsourcing of updates from the people who actually do the construction, the field workers, so construction crews can make those updates where and when the work happens. In order to do that, we really need data capture tools that are much faster and simpler, in other words, more automated than today's solutions.

There are a number of enabling technologies that can change the way that we approach this problem; computer vision, machine learning, augmented reality, voice interaction, and reality capture can all play a role. A lot of these things are being driven by other industries outside the geospatial technology industry. Part of the challenge in bringing together a solution for the as-builting process is looking broadly at these different types of technologies and where they're applied and bringing them together into a solution for utilities. You need to enable the people doing the construction to capture accurate location and attribute data quickly and efficiently. Anyone should be able to use the system with minimal or no training and it should use inexpensive commodity equipment.

Automating feature recognition

As an example, we're in a new subdivision construction site and we're going to do some as-built data capture of some electric equipment using an iPad. We point our mobile device at a piece of utility equipment and the application automates the process of detecting what the equipment is. The user just taps the screen to add the feature, an electrical pedestal. Next, we point our mobile device at the neighbouring electric meter. And again, it's automatically recognized and we tap to confirm that we want to add it.  We can then scan the meter to add more detailed attributes according to rules that we've set up.

Now we want to add a cable. If the trench was still open so the cable was visible, we could automatically recognize it like we did with the other features, but since it's hidden, we select it from a list and then just tap to add points along the path of the cable. As we walk, you can see the cumulative distance of the cable path being shown at the top. Lastly, we'll add an electric cabinet in the same way and scan its attributes, including the transformer number and manufacturer on the front side. Then the KVA from the yellow 50 on the side. We zoom in more closely to get additional attributes like the serial number, work order number from the smaller label.

Once we have completed scanning the various objects, the data gets pushed automatically to the GIS with no user intervention needed. We can see the features that we just captured on a map with their attributes plus photos. The newly captured data about the equipment is immediately available in any GIS client. For example, we can see the newly captured equipment data on a handheld mobile device or it can be viewed on any web browser. The data can then be pushed on from the GIS to real-time systems like an ADMs outage management system or network management system.

The app is able to automatically recognize features and attributes using machine learning based image recognition, to tell what type of feature it is, read barcodes and QR codes, and use text recognition to capture detailed attributes. The additional attribute information also reinforces the feature recognition in some cases. YYou can also use voice recognition as a backup if there's a scenario where you have to correct something that was misrecognized or something that can't be identified correctly.

Lastly is an example of capturing very detailed attributes quickly. We captured 22 attributes in just over a second from an electric meter.

Location accuracy

Location accuracy is particularly important for underground assets. AR gives much greater relative accuracy than consumer GPS and phones do. It measures accurate distances and angles in 3D for recording of relative asset layout. In a range of tests we found AR is accurate to about 1%. As a simple example. we measured a 10 foot distance next to a tape measure. We've added a virtual pipe and you can see the measured distance in the augmented reality app is 10 inches and it's within an inch of the 10 foot mark or within about 1%. Tests of up to about 100 feet have found the same 1% accuracy. AR provides much better accuracy than you could get with anything except the very highest precision GPS.

Visual positioning system

Apple's GeoAnchors is their implementation of the AR Cloud. The idea is that as you're moving your mobile phone around among 3D objects captured in a street view scene, it uses the camera view to match what the camera is seeing against the highly accurate street view type imagery, which Apple calls look around imagery and that refines the position.

Conclusion

Field sourcing of data is key.. You need to enable the people doing the construction to capture the data, and these new technologies really open up some very exciting new ways that we can provide systems to the construction workers to enable them to capture accurate location data. We foresee that visual positioning systems are very likely going to supersede high precision GPS in a lot of scenarios.

This post is based on Peter Batty's (SSP Innovations) talk at the Subsurface Utility Mapping Strategy Forum (SUMSF).

Avoiding serial road excavation work with Waze

Waze, now part of Google, is a crowd sourced handheld app used by 100 million drivers worldwide. It is supported by 500,000 volunteer map editors and 600 partners. Its connected partners program includes 40% of top cities and 60 % of state departments of transportation (DoTs). It is very interesting that Waze has developed a way for governments to report planned and actual road construction which can inform drivers in planning routes ahead of time. This offers a way to provide a single database that could be used to avoid the problem of the same stretch of road being dug up multiple times by different utilities sometimes with a few weeks of each other.

Several years ago I blogged about the online web-based system Koordination im öffentlichen Raum (Spatial, temporal and financial co-ordination of city planning, traffic change, and construction projects on public land) of the City of Bern, Switzerland.  This is a system for coordinating street works that achieves some of the same goals as the 2004 Traffic Management Act in the U.K. It is location aware and enables planners to avoid serial excavations of the same stretch of street or road by coordinating all construction from multiple agencies including Federal, Kantonal and city street and highway agencies, water/wastewater, gas, and electricity utilities, and telecommunications companies so that a section of street or road is not dug up more than once every five years.  In its first year of operation it was estimated that the coordination system saved SFR 7 million.

At GIS in the Rockies Saila Hanninen of Waze described some of the remarkable ways that Waze data and network are being used for purposes beyond what it was initially intended for.

• During and after Hurricane Sandy in Oct 2012 the White house called Waze for help to determine which gas stations were open in the areas hit by the strom.
• For the Rio de Janeiro Olympics, Waze helped to determine where to place three rapid transit lanes that resulted in a 27 % reduction in congestion.
• Waze helped Ghent redesign its city centre to reduce traffic by 40 % which also resulted in fewer accidents and increased bicycle and transit usage.
• Boston asked Waze to help with signal timing that resulted in 18% reduction in congestion just by changing timing.
• During the Pope's visit to Philadelphia, Waze was responsible for a 20% reduction in congestion.
• 63% of callers to 911 don't know their location - Waze can provide accurate locaction information.
• 40% of accidents are reported faster on waze which results in an average of 5 minutes faster response time.

Waze intends to get more proactive.  It now has a crisis response team, that can push messages to Waze users in areas affected by a disaster to collect information about road closures, the state of the roads, which gas stations are open, shelter availability, and safe routes to shelter.

Waze has a vision to eliminate traffic.  Altogether.   To do that requires not only information about the current state of congestion on roads, but also anything that could potentially affect traffic on roads in the future.  Knowing when road and major excavations near roads are planned is essential.  If the Waze partner program included everyone planning excavations on or near roads, it would have developed a database that would provide all the information required to coordinate excavations by utilities, telecoms, and governments.  Similarly to what happened in Bern, this could mean that roads would not be dug up more often than once in five years which would reduce traffic congestion and save money.

Kids use Humanitarian OpenStreetMap to help eliminate malaria

Nearly half of the world’s population is at risk of malaria. In 2015, there were roughly 212 million malaria cases and an estimated 429 000 malaria deaths. 

On Thursday, March 3rd 2016, a world-record humanitarian mapathon took place at Politecnico di Milano in northern Italy.  Two-hundred and twelve 10-year old children from nine classes at six elementary schools in Milan province had the unique opportunity of meeting with researchers from GEOlab (Geomatics and Earth Observation laboratory) and HOClab (Hypermedia Open Center laboratory) of Politecnico di Milano, who introduced them to humanitarian mapping.  In cooperation with Humanitarian OpenStreetMap Team (HOT) and Missing Maps, the team led by Prof. Maria Antonia Brovelli, Dr. Marco Minghini, Dr. Monia Molinari and Dr. Aldo Torrebruno showed children how to map buildings in the northernmost part of Swaziland in a project for malaria elimination.  The results of the mapathon have been incredible: more than 40,000 edits to the map and more than 5,000 buildings mapped in only a few hours of work. Knowing that their maps will be used to help local people in Swaziland, children were simply fantastic in performing the mapping task in a unique, passionate, and enthusiastic way. Some of the young mappers did not want to return to class because they had remaining buildings to map in their task areas.

Humanitarian OpenStreetMap Team is calling all mappers to help HOT and our partners eliminate malaria with its biggest project to date. HOT is working on two projects, mapping over 500,000 square kilometers in 7 countries. This project collective is part of the Missing Maps initiative in conjunction with DigitalGlobe, the Clinton Health Access Initiative, the Gates Foundation, PATH and MapBox.

Help HOT map against malaria!

Crowdsourcing outage reporting

Everytime I attend Distributech I come across something that simply knocks my socks off.  Alan McMorran of Open Grid Systems has been working with Scottish and Southern Electricity Networks (SSEN) and Electricity North West (ENW) on a "Grid Reporter" application that crowdsources information from users with smart phones about outages. 

 

When someone encounters a line down, no power, or damaged equipment situation, they can report the problem from a smart phone with a minimum of hassle - no logging in or entering a location or address.  The application uses the GPS on the smart phone to determine location.  It allows the person reporting the problem to take a picture.  It is able to record which direction (heading) the phone was directed when the picture was taken. 

On the server side the application takes the location and heading and does a geospatial lookup to identify nearby candidates for the specific piece or pieces of equipment the user has identified.  If the user's phone is on a wireless network, it will even suggest which facilities may be the problem and request clarification from the user.  Based on the information provided by the user and the user's smart phone, the application generates an outage report with the details including the specific CIM data element needed by the utility's outage folks to respond.  The server-side requires less than 100 milliseconds of processing to respond to the user.

The server side application resides in the cloud and is scalable so that if this is a storm situation and many calls are being fielded, it can rapidly scale up the necessary computing resources.

 

Another feature is that it is able to push outage status messages to the users reporting problem so that they are kept aware of any danger and progress in resolving the problem. 

 

This strikes me as a way to dramatically improve the collection of outage information by broadening the user base of potential reporters at very low cost.  It also enhances the customer-utility interaction and because it provides feedback it encourages customers and other users to report problems.  In the UK there is another important driver and that is because of the nature of how the electric power industry is organized.  Customers often do not know who to call when they encounter a problem, the network operator or the retailer they buy power from.  With this application they don't need to know because the application automatically connects to the right folks.

 

From an implementation point of view the server-side application assumes that it has access to a network model that uses the Common Information Model (CIM) electric power distribution model standard.  Any utility that has a network model that is CIM-compliant should be able to integrate the application without a great deal of difficulty.

OpenGridMap: open, crowdsourced project to map power grid infrastructure

Earlier this year on a Sunday renewable power supplied almost all of Germany’s power demand, a major milestone in Germany's “Energiewende” policy.  The transformation of the grid (called by some a revolution) is being driven by distributed energy (DER) and smart devices.  It requires profound changes to how we monitor and manage the power grid and to be able to understand the impact of  the integration of more distributed intermittent sources and new consumer, industrial, and commercial electronics requires data and tools that only exist partially today.

If you are familiar with OpenStreetMap (OSM), then the OpenGridMap project is not that different except that it focusses on mapping electric power infrastructure instead of transportation.  I had heard rumours about an OSM-like project in Germany to map electric power infrastructure several years ago, but was unable to find more information about it.  OpenGridMap is an new open community that crowdsources power grid geospatial location and related data to be used for research purposes.  The goal is to collect reliable grid data to enable researchers to conduct simulation studies. OpenGridMap is intended to support the entire process from data collection to making grid data available for various applications. An OpenGridMap app for Android for people interested in helping to collect grid infrastructure data is available on Playstore.

Using OpenGridMap data analytics developed at the Technical University (TU) Munich generates a power grid model in the form of a Common Information Model (CIM) description file which provides the basis for a power grid simulation model.  Experiments with OpenGridMap data have demonstrated the effectiveness of this approach for grid modeling and simulation.  It is expected that as more data is collected it will be possible to generate power grid simulations of larger size, more variety and more accuracy than the currently available state-of-the-art test power grids.

The project was initiated by Professor Hans-Arno Jacobsen, Chair of Business Information Systems, and José Rivera at the Technical University of Munich.  It is supported by a German Federal Ministry of Education and Research grant and the Alexander von Humbolt Foundation.  Collaborators include Siemens AG, World Bank's ESMAP, the Technical University Berlin's VEREDELE project and Universität Erlangen-Nürnberg (FAU) KOSiNeK project.

Related links

OpenGridMap: An Open Platform for Inferring Power Grids with Crowdsourced Data, José Rivera , Christoph Goebel, David Sardari, Hans-Arno Jacobsen, Energy Informatics, Volume 9424 of the series Lecture Notes in Computer Science pp 179-191, 06 January 2016

OpenGridMap: towards automatic power grid simulation model generation from crowdsourced data Rivera, J., Leimhofer, J. & Jacobsen, HA. Comput Sci Res Dev (2016). doi:10.1007/s00450-016-0317-4

New journal for open geospatial data, standards and interoperability

A new open journal Open Geospatial Data, Software and Standards provides an advanced forum for the science and technology of open data, crowdsourced information, and sensor web through the publication of reviews and regular research papers. The journal publishes articles that address issues related, but not limited to, the analysis and processing of open geo-data, standardization and interoperability of open geo-data and services, as well as applications based on open geo-data. The journal is also meant to be a space for theories, methods and applications related to crowdsourcing, volunteered geographic information, as well as sensor web and related topics.

INSPIRE-Geospatial World Forum 2015 program released

INSPIRE-Geospatial World Forum 2015, a joint conference organized by the European Commission and Geospatial Media and Communications, has made available its full conference program. Almost 500 presentations are scheduled on topics including building information modelling (BIM), open data, big data analytics, open standards, linked data, cloud computing, crowdsourcing, Earth observation, indoor positioning, land information systems for smart cities, urban resilience and sustainability, health, agriculture and others.  Some 2000 delegates are expected to attend from more than 80 countries.  Top sponsors include Trimble, Topcon, ESRI, Digital Globe, Oracle and Bentley.

The theme of the conference is CONVERGENCE: Policies + Practices + Processes via PPP with a focus on improving coordination among policy-makers, technology providers and users.  Including geospatial data and technology in construction, agriculture, health and other industry workflows is an enabler for more successful public–private partnerships (PPP) by facilitating more informed decision making among the stakeholders.

Predictions of the future of the utility business 2015-2018

For the first time in a hundred years, the electric power utility industry is undergoing a momentous change. Distributed renewable power generation, especially solar photovoltaics (PV), is introducing competition into an industry that has been managed as regulated monopolies. Consumers with solar PV panels on their roofs are fundamentally changing the traditional utility business model. A recent report from the Edison Electric Institute (EEI) report refers to disruptive challenges that threaten to force electric power utilities to change or adapt the business model that has been in place since the first half of the 20th century.

Most utilities are in the midst of deploying smart grids, which basically amounts to applying the internet to the electric power grid to link intelligent electronic devices, sensors and grid control applications to enable data-driven decision making.  One of the most important changes driven by the implementation of smart grid is the much greater importance of location. Geospatial technology (location, geospatial data management and spatial analytics) is seen as foundational techology for the smart grid.

The other major global change in energy is the shift in energy demand from the world's advanced economies to emerging economies.   Energy demand from OECD countries has hit a plateau.  Currently China is driving world energy demand. The International Energy Agency's (IEA) World Energy Outlook 2014 projects that in the future  as demand slows from China, world energy demand will be driven by India, the Middle East, and Africa and Latin America.

Recently IDC Energy Insights released a report IDC FutureScape: Worldwide Utilities 2015 Predictions with predictions for the future of the utility business.  Some of these are startling, suggesting that the utility industry is going to experience fundamental changes in how they do business over the next few years.

New business models

IDC predicts that utilities will be looking less at generation as a source of revenue.  IDC predicts that by 2018 45% of new data traffic in utilities' control systems will originate from distributed energy resources that are not owned by the utility.

To make up for this loss of generation revenue IDC predicts that utilities will be looking for new business opportunities such as services.  Specifically, IDC predicts that utilities will derive at least 40% of their earnings from new business models by 2017.

Technology

• Cloud - By 2018 cloud services will make up half of the IT portfolio for over 60% of utilities.
• Integration - In 2015 utilities will invest over a quarter of their IT budgets on integrating new technologies with legacy enterprise systems.
• Analytics - By 2017 45% of utilities' new investment in analytics will be used in operations and maintenance of plant and network infrastructure.
• Mobility - 60% of utilities will focus on transitioning enterprise mobility to capitalize on the consumer mobility wave.
• Smart systems - By 2018 cognitive systems will penetrate utilities' customer operation to improve service and reduce costs.

Some  of the important drivers for these trends include the global redistribution of energy demand from the world's advanced to the emerging economies, the rapid emergence of cloud-based provisioning and services, increasing regulatory pressure responding to customer demand to improve energy market transparency and competitiveness, cross-industry competition for technical, especially IT skills, smart analytics, and virtual and augmented reality beginning to be applied in business.

Top 10 technology trends

In March 2014 Gartner, Inc. identified the top ten technology trends which it saw impacting the global energy and utility markets.  There is considerable overlap between IDC's business predictions and the technology trends identified by Gartner, Inc.

Social Media

Social media are beginning to be used as a customer acquisition and retention medium, as a consumer engagement channel to drive customer participation in energy efficiency programs, a source of information about outages, and as the emerging area of crowd-sourcing distributed energy resources coordination. Social media are also being used by utilities for communicating information about outages with customers.

Big Data

Smart grid will increase the quantity of data that utilities have to manage by a factor of about 10,000 according to a recent estimate.  This trend is driven by intelligent devices, sensors, social networks, and new IT and OT applications such as advanced metering infrastructure (AMI), synchrophasors, smart appliances, microgrids, advanced distribution management, remote asset monitoring, and self-healing networks.  The type of data that utilities will need to manage will change: for example, real-time data from sensors and intelligent devices including smart phones and unstructured data from social networks will play a much greater role for utilities in the future.

Mobile and Location-Aware Technology

Mobile and location-aware technology which includes hardware (laptops and smartphones), communication products (GPS-based navigation, routing and tracking technologies), social networks (Twitter,Facebook and others) and services (WiFi, satellites, and packet switched networks) are transforming all industries.  Utilities for the most part have been slow to adopt consumer mobile technology, but  this is changing.

Cloud Computing

Acccording to Gartner, areas such as smart meter, big data analytics, demand response coordination and GIS are driving utilities to adopt cloud-based solutions.  Early adopters of cloud technologies include small utilities with limited in-house IT skills and budgets, organizations which provide application and data services to multiple utilities, such as cooperative associations and transmission system operators,  and investor-owned utilities (IoUs) conducting short-term smart grid pilots.

Sensor Technology

Sensors, which are being applied extensively throughout the entire supply, transmission and distribution domains of utilities, provide a stream of real-time information from which a real-time state of the grid can be derived.

IT and OT Convergence

Virtually all new technology projects in utilities will require a combination of IT and OT investment and planning, such as AMI or advanced distribution management systems (ADMSs).  This will be a challenge for many utilities, especially smaller ones, which don't have in-house IT skills.

Advanced Metering Infrastructure

AMI provides a communication backbone aimed at improving distribution asset utilization and facilitating consumer inclusion in energy markets.

Internet of Things

Sensors and actuators embedded in physical objects are linked through wired and wireless networks, using the same Internet Protocol (IP) that connects the Internet.  When intelligent objects can both sense the environment and communicate, they become tools for understanding utility grids and responding to changes in near real-time.  Following McKinsey there are two key benefits arising from the Internet of Things for utilities

• Enhanced situational awareness - acheiving real-time awareness of physical environment, in this case, the grid
• Sensor-driven decision analytics - assisting human decision making through deep analysis and data visualization

Asset performance management

Traditional asset management approaches are too limiting for today’s performance-based, data-driven utility environment. Asset performance management solutions need to deliver real-time equipment performance, reliability, maintenance and decision support for effective resource management so that operations and maintenance teams are empowered with real-time decision support information, providing the right information to the right people at the right time and in the right context.  The result is improved operational performance and better asset availability and utilization.

Business Intelligence and Advanced Analytics

Analytics will become essential as the volume of data generated by intelligent devices and sensors, mobile devices (the Internet of Things) and social media increases and huge pools of structured and unstructured data need to be analyzed to extract actionable information.  Analytics will become embedded everywhere, often invisibly.

Crowdsourcing geospatial data for authoritative government databases and NSDI

Since Google Earth's release in June 2005, national mapping agencies (NMCAs) and other government organizations involved with geospatial data and technology have been reassessing their role.  At the Geospatial World Forum (GWF) in Amsterdam two years ago, Paul Cheung of the United Nations Initiative on Global Geospatial Information Management (GGIM) voiced the concern about their role that many national mapping agencies and other government organizations with responsibility for geospatial information have had since the advent of Google Earth, private data companies like Digital Globe, crowd-sourced geospatial data like OpenStreetMap, and open data policies adopted by many governments around the world.

The challenge for NMCAs is that in parts of the world, Google. Bing, OpenStreetMaps, or a local crowd-sourced alternative like Malsingmaps provides more comprehensive, accurate, current and accessible data with less restrictive licensing and at a lower cost than the NMCA.  A report from the GGIM argues that "governments have a key role to play in bringing all actors together to ensure that our future society is a sustainable, location-enabled one, underpinned by the sustainable provision and effective management of reliable and trusted geospatial information."  In other words governments and NMCAs have a unique critical role in developing and maintaining authoritative national databases.   To improve the quality and timeliness of these authoritative databases many have argued that NMCAs need to find ways to incorporate volunteered or crowdsourced geospatial data in these databases.

In the United States the National Map is a significant contribution to the National Spatial Data Infrastructure (NSDI).  It is a collaborative effort among the US Geological Survey (USGS) and other Federal, State, and local partners to deliver topographic information for purposes that range from recreation to scientific analysis to emergency response. The geographic information available from The National Map includes orthoimagery, elevation, geographic names, hydrography, boundaries, transportation, structures, and land cover.

In addition to being an important contribution to the NSDI, the National Map is a foundation for the Department of the Interior (DOI) Geospatial Modernization Blueprint and its mission to protect America's treasures for future generations, provide access to America's natural and cultural heritage, offer recreation opportunities, honor trust responsibilities, conduct scientific research, provide wise stewardship of energy and mineral resources, foster sound use of land and water resources, and conserve and protect fish and wildlife.

The USGS's National Map Corps (TNMCorps) was initated a year ago as a nationwide program with the goal of using volunteered or crowdsourced data for the National Map.   TNMCorps' Volunteered Geographic Information (VGI) project engages citizen scientists to collect manmade structures data including: schools, hospitals, post offices, police stations and other important public buildings. In the past year, civilian volunteers in 50 U.S. states have begun providing accurate geospatial data to the The National Map,

• 1,422 – volunteers
• 50,696 - total number of edits
• 50  - U.S. states

According to the Director of the National Geospatial Technical Operations Center in the U.S."This project has proven that we can count on volunteers to provide quality information to be included in authoritative government databases".

If you are interested in becoming a volunteer for TNMCorps, go to the National Map Corps project site to sign up.

Thanks to Dave Smith for pointing me to this.