Between the Poles

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.

Smart power spatial power

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 (and in not-too-distant future with Tesla batteries in the basement) and companies like Solar City (co-founded by Tesla co-founder Elon Musk) 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.

As a result, every aspect of the the electric power industry is changing. One of these changes involves the role that geospatial data and technology play in the electricity industry. In the past, geospatial has been a tactical tool — it was (and still is) used in a variety of applications — in outage management, asset management, mobile work- force management, energy density modelling, vegetation management, demand modelling, transmission line siting, substation siting and design, energy performance modelling of buildings, disaster management, and mapping renewable resources, to name just a few. However, with the changes that the industry is undergoing now, geospatial is poised to become a foundation technology for the smart grid.

The Energy Issue of Geospatial World Magazine explores the impact that this momentous change is having on the application of geospatial technology in the electric power utility sector.  Below I'm providing an overview of the material relating to electric power you'll find in this issue.

GIS has been widely used by utilities for years for automated mapping/facilities management, back office records management, asset management, transmission line siting, and more recently for design and construction, energy conservation, vegetation management, mobile workforce management (MWFM), and outage management (OMS). Now, utilities are integrating GIS with automated meter infrastructure (AMI) and supervisory control and data acquisition (SCADA) systems. Intelligent design has crossed over from the office to the field in utilities, also enabled by the capabilities of GIS, says Smith. Geospatial-related analytics (spatial analytics) is seen as one of the key aspects of success for electric utility operations in the smart grid era. Looking for patterns and correlations between different land, weather, terrain, assets, and other types of geodata will be increasingly important for utilities. Power-related analytics with geospatial components include network fault tracing, load flow analysis, Volt/VAR analysis, real-time disaster situational awareness, condition-based maintenance, and vegetation management. The smart grid is all about situation awareness and effective anticipation of and response to events that might disrupt the performance of the power grid. Since spatial data underlies everything an electric utility does, GIS is the only foundational view that can potentially link every operational activity of an electric utility, including design and construction, asset management, workforce management, and outage management as well as supervisory control and data acquisition (SCADA), distribution management systems (DMSs), renewables, and strategy planning.

Peter Batty reports on the major growth in geospatially-enabled Web and mobile applications with a special focus on the open source geospatial community and the significant impact of these technologies in the utility sector. "In general, there are a lot of geospatial open source software components available now that have the capabilities and robustness to be used in serious enterprise applications."  John McDonald, Chairman of the Smart Grid Interoperability Panel has been a firm believer for a long time that geospatial information is part of the foundational platform for smart grid.  SGIP has signed a memorandum of understanding with the Open Geospatial Consortium with the goal of incorporating more geospatial standards into SGIP standards.  Cindi Smith of Bentley goes even further and argues that  “geospatial technology is already a foundational component of electric power utilities’ IT/OT systems.  Smart grid simply brings more focus to the role it can play by virtue of the visibility of smart grid projects and processes in a utility and their need to exploit the vast amounts of data produced by the smart grid." Loek Bakker & Jan van GelderIt of Alliander, a Dutch utility company, describe how essential it has been for Alliander to integrate GIS, ERP and SCADA systems for a correct picture of its assets.  As electric utilities evolve into increasingly data-driven organisations, Jeffrey Pires and G. Ben Binger describe how GIS is fast emerging as the backbone for data management platforms. 

Cities are beginning to develop 3D models of underground infrastructure motivated by new underground remote-sensing technologies and by ROIs of up to of US$21.00 saved for every US$1.00 spent on improving the quality level of subsurface utility information.  Steve Dibenedetto, Senior Geoscientist and Technology Manager, Underground Imaging Technologies (UIT), part of Caterpillar describes new remote-sensing technology for detecting and geolocating in 3D underground utility infrastructure such as Ground Penetrating Radar (GPR) and Electromagnetic Induction (EMI). 

The Indian on-going Restructured – Accelerated Power Development and Reforms Program (R-APDRP) is one of the largest IT initiatives by electric utilities anywhere in the world — in one integrated project, all state-owned distribution utilities in India are building IT infrastructure, IT applications and automation systems.  The programme set out to create baseline data in the form of consumer indexing, GIS mapping and asset mapping. Reji Pillai & C. Amritha assess how GIS can be applied in this context.  

Integrating  geospatial and BIM is a key enabler for energy performance modeling which is a fundamental instrument for reducing the energy consumption and improving the energy performance of new and existing buildings. According to a report from Navigant Research, global zero energy buildings revenue is expected to grow from $629.3 million in 2014 to $1.4 trillion by 2035.

Wolfgang Eyrich of Entegra shares how Entegra’s primtech product, which is designed to help substation designers deliver designs based on integrated product modelling, provides a geographical context to substation designing. 

Matt Zimmerman of Schneider Electric highlights one of Schneider Electric's key techologies "graphic work design" which is integrated geospatial and engineering design (CAD or BIM).  Schneider Electric's geospatial division focuses on developing integrated, location-aware enterprise solutions such as integrated outage management (OMS), customer information system (CIS), GIS, and external weather reporting and forecasting service to help plan crew deployment during a storm.  Matt foresees that location-aware predictive analytics for electric networks is going to be one of the major development areas for utilities in the future.  Brad Williams of Oracle points out that spatial analytics is becoming a key technology for electric utilities because everything a utility does - customers, assets, and operations - involves location. 

One of the biggest challenges that utilities are experiencing is increasing volumes of structured and unstructured data (big data) that is overwhelming traditional enterprise systems.  The structured data comes from smart meters and intelligent electronic devices, and the unstructured data from social networks including Twitter, Google, Facebook and other social applications.  Consumerization of geospatial technology (we are all GPS-enabled sensors) will enable crowd-sourcing new sources of information about electric power networks most of which involves location (big spatial data).

Open source GIS for engineers

Very excited to see this course given at IIT Bombay.  We need more courses on geospatial technology for engineers and architects.

Title : GIS for Civil Engineers - A Short Term Course at IIT Bombay, India

When : 01 Dec 2014 to 05 Dec 2014

Where : IIT Bombay Website : http://www1.iitb.ac.in/~qip/QIPBrochure-Q7.pdf

Description : The short term course is intended to introduce the following themes:

- To spread the importance of GIS in Civil Engineering

- To promote the use of open source GIS softwares

- Teach the basic concepts and hands on experience on few applications so that at the end of course, participants reach such a level that they can further explore themselves according to their needs.

Officers, Engineers and Scientists working in Water Resources Engineering, Urban/town planning, Municipal agencies, Consulting companies, NGO’s as well as self-employed practitioners engaged in spatial analysis of our surrounding environment would benefit from the proposed program. As participants are expected from all over India, this course would provide an excellent opportunity for the participants to interact with one another and discuss problems and solutions of mutual interest.

Distributed processing with noSQL databases enables fast geoprocessing of big spatial data

I was lucky enough to attend the High Performance Geoprocessing Symposium in Ottawa.  It is amazing how far some very exciting geospatial projects focused on "big spatial data" under the LocationTech umbrella have come in the last few years. If you are not familiar with LocationTech, it is a working group hosted by the Eclipse Foundation with the objective of developing advanced open source geospatial technologies. Eclipse is a vendor neutral not-for-profit community collaborating on commercially-friendly open source software.

The talks today described projects that have been developed under the impetus of "big spatial data" and user demand for faster, more responsive geoprocessing.

GeoTrellis - high performance raster processing

Robert Cheetham of Azavea described how his project GeoTrellis is achieving "advanced spatial analysis on the web."  The challenges that this project strives to overcome are

• performance and scalability - performing overlays involving large rasters for multi-criteria suitability,  optimal siting analysis or simulation involves serious processing and to be able to do this in real-time with the type of response that a web user expects requires new algorithms
• large datasets - such as social media streams like Twitter, the huge volumes of data required to model a smart city, or just the volume of data require to monitor and run a modern city on a daily basis
• user interface - an ArcGIS syle menu bar with thousands of options is fine for trained GIS professionals, but an urban planner or other non-GIS professional needs something better

GeoTrellis has been designed to address these challenges.  It is open source released under an Apache licence, which means that all you need to do is indicate attribution, otherwise you can do anything you like with it.  It provides high performance raster input/output, geoprocessing, and web services using distributed processing to achieve quite amazing throught put for large raster datasets.  Robert described overlay operations with 6000 x 5000 pixel rasters, hundreds of megabytes in file size, that GeoTrellis performs in 200 milliseconds, effectively real-time from the point of view of a web user.

GeoTrellis relies on other open source libraries to achieve these phenomenal processing speeds.  It uses the Hadoop (a noSQL distributed database) file system (HDFS), but replaces Hadoop's MapReduce (MapReduce is software for  processing vast amounts of data in-parallel on large clusters of commodity hardware) with Apache Spark for distributed processing.  Robert said that one way to look at GeoTrellis is as the geospatial enabled version of Spark.

Robert described a number of applications that involve overlaying very large raster databases for analysis or simulation where different scenarios can be run in real-time so that the user can quickly assess alternatives.  For example, OpenTreeMap is a crowdsourced urban forest database where using GeoTrellis users can quickly assess the costs and benefits and optimize the planting of urban trees with different objectives, shading, air quality, reducing runoff, and so on.  This would not be possible with traditional GIS and geospatial databases.

GeoJinni - Spatial Hadoop

  Ahmed Eldawy from the University of Minnesota describes a LocationTech project he has been working on for several years that was originally called Spatial Hadoop, but has been renamed to GeoJinni.  Perhaps the best known NoSQL distributed database, Hadoop knows nothing about spatial data.  It doesn't support spatial datatypes, spatial indexes or spatial analytics. 

GeoJinni is a comprehensive extension to Hadoop that allows efficient processing of spatial data. It injects spatial awareness in the different layers and components of Hadoop to make it more efficient to store and process big spatial data.  It is a spatially-enabled Hadoop that supports spatial datatypes, spatial indexes, spatial analysis, and spatial predicates (such as "intersect").  GeoJinni can be installed as an extension to an existing Hadoop cluster which means that it can be run without the need to give up existing Hadoop installations. It is portable to run with a number of Hadoop distributions including Apache Hadoop, Cloudera, and Hortonworks.

Some of the applications where GeoJinni is currently used are modeling world traffic and querying and visualizing spatio-temporal satellite data.

Currently it uses MapReduce to distribute processing across multiple machines, but in the future the plan is to extend support to Spark.

GeoMesa - high performance geospatial (vector) analytics

Chris Eichelberger from the GeoMesa project described an approach to big spatial data that focusses on vector data and compliance with existing widely used geospatial APIs. 

The problem as outlined by Chris is if you want to find all the points that are within a certain distance from a fixed point your strategy depends on the total number of points you are dealing with.

• hundreds of points - can be handled reasonably well (but not optimally) by a full table scan with an SQL database such as Oracle or PostgreSQL.
• hundreds of thousands of points - you need PostGIS or Oracle Spatial to handle this efficiently.  The widely used GeoTools API is an open source Java library that provides tools for geospatial data management and analytics.  GeoTools supports plugins for various databases including MySQL, Oracle, SpatiaLite and SQL Server, but the most widely used database with GeoTools is PostGIS.
• hundreds of millions (typical of Twitter streams, for example) - this volume of data is in the realm of big spatial data and requires a new and different approach

NoSQL ("not only SQL") databases

I have already mentioned Hadoop.  To deal with data volumes that are too large for traditional SQL databases, beginning in 2004 Google developed "BigTable" which is a compressed, high performance, and proprietary data storage system built on the Google File System that is used by a number of Google applications including Google Maps.  Apache Accumulo is a distributed "key/value store" that is based on Google's BigTable design and is built on top of Apache Hadoop and other Apache projects.   "Key/value store" simply means that every record has a unique identifier, often a hash.  For putting geospatial data into a key/value store the key concept is that of the "geohash" converting a 2D, 3D or 4D coordinate such as a lon and lat, lon, lat and elevation or a lon, lat, elevation, and time to an integer index, such as a quadtree or R-Tree index, that can be used to order and rapidly retrieve spatial data.  Geohash means that you now can take advantage of key/value databases such as Accumulo and programs such as MapReduce - Accumulo uses MapReduce for distributed processing. GeoMesa aims to provide as much of the spatial querying and data manipulation to Accumulo as PostGIS does to Postgres.

An especially exciting thing about GeoMesa is that it is an implementation of the GeoTools API on Apache Accumulo instead of PostGIS.  If you have an application that was developed with GeoTools, you can simply replace PostGIS with GeoMesa to handle big spatial data volumes.  There is even a plug-in for GeoServer so that geospatial data in Accumulo to be shared and visualized via Open Geospatial Consortium (OGC) standard services, such as WFS.  Chris also announced that the National Geospatial-Intelligence Agency's (NGA) GeoWave project, which shares many goals with GeoMesa, will be folded into GeoMesa.

Spatio-temporal

A fairly common query when dealing with social media streams such as Twitter is to "find all the tweets from locations within 10 km and within 15 minutes of where I was in my car at 9:45 this morning."  Chris outlined how GeoMesa can handle that type of query through GeoMesa's support for an Open Geospatial Consortium standard called the web geoprocessing service (WPS).

Subsurface sensing technologies map a superhenge at Stonehenge

The objective of the remarkable four year Stonehenge project was to create a detailed archaeological map of Stonehenge and its surroundings based on a synthesis of remote sensing and subterrainean geophysical data. The  result is a digital model of the Stonehenge landscape that ties surface features together in a seamless map with underground features and structures that shows that Stonehenge proper is only a small piece of a much larger, more complex ancient structure.

The Stonehenge Hidden Landscapes Project is led by the University of Birmingham in conjunction with the Ludwig Boltzmann Institute for Archaeological Prospection and Virtual Archaeology and is a collaboration with the University of Bradford, the University of St Andrews, and the 'ORBit' Research Group of the Department of Soil Management at the University of Ghent, Belgium.

The data collection technologies include surface remote sensing methods such as aerial photography, laser scanning and airborne imaging spectroscopy. The geophysical prospection methods used ground penetrating radar (GPR), electromagnetic induction, and magnetometry.  Under suitable ground conditions GPR surveys provide detailed three dimensional information about depth, shape and location of archaeological structures at a high spatial resolution. GPR can be used to detect stone structures, interfaces caused by pits and trenches, and cavities and differences in soil humidity. New multichannel GPR arrays permit considerably increased spatial coverage with greatly improved resolution and can generate detailed 3D images of subsurface structures. Magnetometery is most suitable for mapping archaeological structures causing anomalies in the Earth's magnetic field such as prehistoric pits, trenches, postholes, walls, fire places and kilns. Magnetometer surveys result in a 2D map without direct information about the depth of the buried structures. Electromagnetic induction measurements can be used to efficiently and non-invasively to map physical properties of the soil and buried objects.

The main platform for integrating the remote-sensed and the geophyical measurements is a GIS-based archaeological information system, with additional tools for dynamic visualization and spatial analysis. The huge amount of data generated by the remote sensing and geophysical prospection measurements require appropriate data processing, aanalytical and 3D visualization tools.

The remote sensing techniques and underground geophysical surveys have discovered hundreds of new features which now form part of the most detailed archaeological digital map of the Stonehenge landscape ever produced. The results of the survey include 17 previously unknown ritual monuments dating to the same time period as Stonehenge. A massive timber building, probably used for the ritual inhumation of the dead and which was finally covered by an earthen mound has been mapped. The immense Durrington Walls 'super henge', situated a short distance from Stonehenge was also mapped.  It was found to have a circumference of more than 1.5 kilometers.  The results will be featured in a new BBC Two series titled Operation Stonehenge: What Lies Beneath.

Small utilities turning to "smart grid as a service"

Globally, smart grid technology has emerged to help utilities deal with challenges such as increased reliability, the need to reduce non-technical losses, distributed renewable generation, and electric vehicles, but for small to medium utilities, access to IT resources limits their ability to implement smart grid solutions.   Back in 2010 McKinsey was already seeing AMI vendors starting to look at options for providing AMI services using a "software as a service" model.  Now power industry IT vendors and service providers are increasingly offering managed services solutions, referred to as smart grid as a service (SGaaS).

I blogged previously about Burlington Hydro, a small utility in an affluent part of southern Ontario that is integrating into an intelligent network many aspects of what is typically included in smart grid including intelligent network devices, self healing networks, smart meters, distributed generation, electric vehicles (EV) , factory ride-through systems (enables factories to continue functioning through outages), battery-based electric storage, bidirectional communications network linking the intelligent devices to the control center, and dramatically increased volumes of real-time data.  Burlington Hydro has been working with a local IT consulting company AGSI to develop systems to manage their smart grid deployment in a real-time, big data IT environment.  But what about small utilities who don't have the in-house skills or the revenue stream to support bringing in an outside IT consulting company ?

Back in 2012 at an annual Geospatial Information and Technology (GITA) Pacific Northwest Conference, Terraspatial Technologies gave a presentation on a hosted or SaaS (software as a service) solution for small utilities.

Hosted solution

What struck me as as so unique about what Terraspatial offers and which is so valuable to small utilities is that it is a hosted solution. Basically, all the utility needs to install at its site is a browser, everything else is running in the cloud.  The most important benefit of a hosted solution like this is that it has the potential to provide a high level of IT security without the need to increase the level of IT capacity that the utility needs to maintain in house.

Terraspatial's hosted solution is called PlantWorx for electric power utilities. The design goals of the solution that Terraspatial developed are very relevant to small utilities. 

• Hosted, which means that the utility does not need to own or manage servers or software. 
• Secure because it relies on the security of a major cloud hosting provider such as Rackspace or Amazon that can provide a level of security, including protection from internal tampering, role-based access by users, protection from external  threats, the latest encryption, redundancy and back-ups, ISO certified data centers, and mirrored servers for persistent backup, in other words a much higher level of security than the average utility network is capable of.
• Accessible from the office and the field
• Integrated solution that supports staking through to accounting and reporting with interfaces to CAD, GIS, customer information systems, accounting and billing systems, materials management, and other systems

Now according to Navigant Research the growth in cloud-based services has increased the awareness of SGaaS. Offerings are available for a host of smart grid applications in several categories, including home energy management (HEM), advanced metering infrastructure (AMI), distribution and substation automation (DA and SA) communications, asset management and condition monitoring (AMCM), demand response (DR), and software solutions and analytics. 

The complexity of smart grid deployments, systems integration, spatial analytics, real-time big data and cyber security and limited internal IT capacity are some of the drivers behind a growing market for SGaaS. Navigant Research forecasts that the global SGaaS market will grow from just under $1.7 billion in 2014 to more than $11.1 billion in 2023.

Urban planning working group announced by the Open Geospatial Consortium

An Urban Planning Domain Working Group (DWG) has been chartered to define the role for Open Geospatial Consortium (OGC) standards within the urban planning discipline.  The DWG offers an open forum for the discussion and presentation of interoperability requirements, use cases, and implementations of OGC standards relevant to urban planning. 

Technologies and trends such as smart cities, smart grids, sensor webs, the Internet of Things (IoT), facilities and asset management, indoor and outdoor navigation, energy performance modeling and real-time, “big data” analytics are important for urban planners.  In these technology domains, open standards encourage the sharing of information. The OGC Urban Planning Domain Working Group intends to discover requirements for open spatial standards in information systems involved in the planning, design, use, maintenance and governance of publicly accessible spaces. Requirements identified by OGC Domain Working Groups are typically used as the basis for standards development by chartered OGC Standards Working Groups (SWGs).

Image: Sunshine Ecomap

OGC's new Urban Planning group releases draft charter

An Urban Planning Domain Working Group (DWG) is being chartered to define the role for OGC standards and related activities related to urban planning.  It is also intended to provide an open forum for the discussion of interoperability requirements, use cases, pilots, and implementations of OGC standards in this domain.  The OGC seeks comments from the public on the draft new charter. The comment period closes on  6 August 2014.

Technologies and trends such as Augmented Reality (AR), Smart Cities, Smart Grids, Sensor Webs, the Internet of Things (IoT), LBS (Location Based Services), Facilities Management, navigation (indoor and outdoor) and “Big Data” Analytics all can play important roles in informing urban planners.  In these technology domains, open standards can facilitate the development, publication, discovery, and use of information. The OGC Urban Planning DWG intends to discover requirements for open geospatial standards in information systems involved in the planning, design, use, maintenance and governance of publicly accessible spaces.

Mobile scanning interiors of buildings with automatic feature extraction for energy performance modeling

At the SPAR International conference Avideh Zakhor from the University of California Berkeley gave an enthralling presentation about her research on capturing optical and thermal point clouds of the insides of buildings using a portable backpack containing a collection of sensors.   Her team has also developed analytical software tools for generating floor plans, distinguishing rooms, identifying staircases, texture mapping surfaces, identifying windows, finding heat sources such as computers and human occupants, identifying lights, and even estimating plug load lower consumption - all automatically. The software her team has developed can generate input in the form of an IDF file for the Department of Energy's EnergyPlus energy performance analysis program. Avideh said that soon it will also be able to generate a gbXML file, which can be consumed by almost all building energy performance analysis software.

Man portable system

 

The "man portable system" her team has developed is a 32 pound backback with a collection of sensors.  There are sensors for accurately tracking the six degrees of spatial freedom which determine the location and orientation of the backpack in addition to sensors for recording optical and thermal point clouds. The operator walks through the building going into all rooms, halls, stairwells and other human accessible spaces. With the backback this is a much faster process than capturing indoor spaces with static laser scanners. For example, Avideh's team was able to scan Union Station in Washington DC in a matter of hours. A comparable static scan takes days.

 

Automated feature extraction

 

The resulting georeferenced data is then offloaded from the backpack and analyzed to generate different 3D data products including optical and thermal 3D point clouds, a colourized 3D point cloud, surface texture map,  and triangulated mesh surfaces.  Two different triangulated surfaces are generated for different applications - a very detailed surface and a much simpler model of spaces and walls. Using these data products the software automates feature extraction. It can distinguish  windows, staircases, and individual rooms and generate a floorplan.  Surface textures can be added to create a 3D photorealtistic rendering.  By analyzing both the optical and thermal point clouds the software tools can identify heat sources including computers and human occupants and can even identify lights and estimate power plug loads.

 

Building energy performance modeling

 

Usi‎ng the results of the automated feature extraction the software tools can be used to generate the input required for a building energy performance audit. Currently the tools are able to generate input in the form of an IDF file for the Department of Energy's Energyplus whole building energy simulation program.   Avideh said that soon it will be possible to generate an gbXML file, which can be consumed by most building energy performance analysis applications including IESVE and Green Building Studio.  This clearly has the potential to convert what is now a slow, manual, expensive process into a fast, repeatable, affordable process.

Using free geospatial data to reduce vegetation-induced power outages

At the NRECA TechAdvantage conference Dr. Comfort Manyame, GIS Manager at Mid-South Synergy Electric Coop, gave a very interesting presentation demonstrating how freely available geospatial data can be applied to proactively to reduce outages. 

Mid-South Synergy is a utility with 25,000 customers in Texas.  20% of its annual budget is allocated to vegetation management.  In 2011 40% of its outages were due to vegetation.  In 2012 when drought affected their service territory, 52 % of their outages were vegetation-related.  Hazard trees are trees that have a structural defect.  Dead trees or diseased trees with significant dead wood comprise most of the hazard trees.  In the past hazard trees were typically identified by several mechanisms: after they caused an outage, reported by customers, or field staff would identify them during right of way (ROW) maintenance and other activities.  80% of these trees are outside the ROW, so tree height is an important factor in determining whether a diseased or dead tree is a risk for a power line.

Concerned about the increased number of vegetation-induce outages during the drought in 2012, Mid-South Synergy decided to be proactive.  Using freely available geospatial data about

• Vegetation types
• Land use/land cover
• Existing vegetation height
• Vegetation conditions
• Soils data
• Rainfall data

they used weighted overlay analysis to generate a map showing high, medium and low risk of hazard trees.  They also classified all their conductors depending on whether they were in high, medium or low risk zones.  They then intensified their hazard tree cutting activities near cables at high risk in high risk areas.

The benefits they saw included

• Reduced outages due to vegetation. 
• Tree cutting could be planned in advance, not when an outage occurred or when customers called.  In addition they could make much more efficient use of their field resources, because instead of crews responding to customer calls which could come from anywhere in their total service territory, they could dispatch a crew to cut all the hazard trees in a contiguous area.
• Customer calls to remove hazard trees declined dramatically.

Vegetation-induced outages and vegetation management together represent one of the largest costs for many utiltiies.  Dr. Comfort Manyame has shown that significant  benefits in addressing this major challange can be realized from simply applying the available geodata, all of which was free in this case.