"Big Data" in Green Building ,design, construction, and operation of the built environment.

The building industry often enters the “Big Data” conversation through energy efficiency, and Smart Grid and the proliferation of sensors and sub-meters. These provide unprecedented information about energy use over time and across spatial scales. This also reflects the long-standing, foundational importance of energy efficiency to the green buildingmovement. “Big Data” refers to mixtures of volume (scale of data), velocity (analysis of streaming data), variety (difference in forms of data), and veracity (uncertainty associated data). Relatively speaking, energy efficiency alone is modest by these measures. Green building changes things and brings true Big Data into play. Green building encompasses energy efficiency and adds many more dimensions of performance. LEED offers a definition of green building which includes location and transportation, energy and atmosphere, water efficiency, materials and resources, and indoor environmentalquality. This breaks out into hundreds of individual LEED credits and thousands of specific metrics. A Big Data story starts to emerge when tens of thousands of green buildings projects using thousands of metrics, generate data from tens or hundreds of thousands of automated “points”, and provide daily experiences for millions of occupants. Now, we’re talking Big Data: huge data volumes, streaming at different rates, taking a wide variety of forms, and varying dramatically in their accuracy, precision, and reliability.

Big Data Analytics in Utilities - Indian Perspective

India the world’s fifth-largest electricity generation capacity and faces acute power shortage due to the with outdated power infrastructure to meet the growing demand from residential as well as the Commercial/industries. India has suffered consistent grid failures and power black outs since independence. In 2012, the  failure of northern, eastern and north-eastern grids left many parts of the country in the dark. The country remains energy deficient despite 15 percent or more of federal funds being allocated to the power sector. what can be the reasons  the answer is according to industry experts, bankrupt state-run electricity boards, an acute shortage of coal, skewed subsidies which end up benefiting rich farmers, power theft, and under-performing private distribution agencies are to blame. The Aggregate Technical & Commercial (AT&C) losses are expected to be around 33 percent. In fact, it is estimated that the AT&C loss levels in some states are as high as 70 percent because of antiquated grids, power theft and faulty meters.

The problem of power shortage in India is persistent though we have all kinds of resources In July last year, the country faced the largest power outage in the country affecting 22 states and 620 million people. The outage was a wakeup call to modernize the power infrastructure in the country.

With huge losses of ATC , leakages and theft there is strong push for the deployment of smart meters and smart grid technologies to plug this gap. The Ministry of Power recently approved pilots for smart grids in the country and private players like Tata Power, Reliance Infrastructure are already running smart meter and smart grid led project. Recently State Pondicherry is successful in pilot implementation of Smart Grid .

The resulting transformation is generating tremendous volumes of data. The frequency and volume of data that is emerging from smart meters, grid devices and other network controls and sensors is pushing the sector to deploy analytics to accommodate the emerging opportunities around customer demand ,Distributed Generation,

Around the world, utilities are under pressure.
Financial stakeholders look for operational efficiency at a time when aging workforces and infrastructure
Regulators require compliance and detailed reporting on operations.
Operators seek action on smart grid and smart metering initiatives that add intelligence to infrastructure.
 Customers seek choice and convenience—at affordable costs.

TO overcome all the difficulties today’s utilities to re-examine every aspect of their business process, Meter to Bill and Meter to Cash scenarios.

Big Data Analytics Benefits

Utilities are rolling out smart grid and smart metering projects to address some of these challenges. These deployments are underway in India are at a nascent stage and mature in due course of time. They are creating exponentially more data for distribution companies and giving them access to information they’ve never had before. Accessing, analyzing, managing, and delivering this information can help them optimize business operations and enhance customer relationships.

They can perform continuous analytics against this data to look for anomalies, patterns and trends that might indicate an opportunity for them to make actionable decisions on both supply and demand. Integration into outage and distribution management applications allows for further development of business capabilities such as distribution, load management switching, etc. Protocols can be established to move customers to alternate feeders during times of over capacity.


Analytical information also allows utilities to look at granular use and consumption patterns for neighborhoods, districts, or cities to facilitate better supply planning and load forecasting in these service territories.

Big Data can also help distribution companies achieve and maintain the levels of satisfaction desired by customers and by regulators. For instance, by integrating advanced metering, grid devices and network management systems, they can address more proactively outages and other system conditions that exist within their territories. This allows them to be much more proactive in the provision of network condition information to customers and other stakeholders.

Big Data and Analytics can help the companies move away from “one size fits all” services. For example, at the customer premise level, they can analyze usage patterns at the meter level and provide this usage information back to consumers with the intent of developing market driven and customized pricing offers that reflect individual consumption characteristics.

Many companies also have geo-spatial data available from their equipment, diagrams and vehicles. This data can be used to deliver real-time analytics to pin-point the need for a maintenance person, when a network is down, overloaded or reaching capacity.

In mature markets like North america and Europe, Big Data solutions is also helping utility companies determine competitor strengths and weakness, enabling them to exploit competitive strongholds and target marketing programs towards specific customers or segments of customers.

Big Data and analytics can also give an impetus to the adoption of renewable sources of energy. Traditional power generation investments involve large amounts of property to build a large plant on, but newer renewable sources like wind and solar energy can be located closer to demand sources. Big Data solutions can look at all of factors of a city, from standard utility ones like load profiles and capacity to more unstructured ones from city demographics.

Traditional utility data, demographic information and new sensor data can therefore be combined to provide the optimal investment scenarios necessary to meet growing renewable energy portfolio requirements. This can then be used to make smarter investment decisions.

Scenario where data on wealth distribution in office spaces, commuter congestion and electric vehicle population history combined with current load profiles and capacity is combined to predict which buildings will have the highest growth in electric vehicles over the next two decades. This data can feed portfolio planning decisions like deciding where to invest in solar panels – to help source cheaper and cleaner local energy to charge those vehicles instead of transporting it in from a remote fossil plant at high cost.

Predictive Analytics in Big Data will Forecast customer revenue, energy consumption, maintenance costs, outages, reliability, energy procurement costs

Conclusion

An optimized power generation and distribution system with Big Data analytics can complement new additions to power generation to meet the power deficit in the country.

SAP Smart Grid and Meter Analytics

Today’s electrical distribution systems continue to evolve as new smart devices are added to the network. With each installation comes the capability to better monitor and report important data related to energy usage, outages and changes in demand. This mass influx of raw data has led to a major void that must be addressed—data is currently being generated exponentially faster than it can be analyzed. To fill this void, utility companies often find themselves in search of software solutions to help break down data from sensors and smart devices to find value hidden in a sea of raw data.

SAP Smart Grid and smart meter analytics software Insight provides utilities with advanced analytic capabilities to meet their business needs and create a stronger connection to the machines that power the grid. It applies proven analytics to make sense of big data collected from intelligent machines to better predict, manage, model and forecast potential problems that a utility’s electrical grid may face. The software strategically monitors influential data—such as electrical usage, grid performance and weather history—creating an interconnected “ecosystem” of people and machines to better equip utilities with the information and tools needed to optimize their electrical distribution systems. Utilities can then apply the information and knowledge gained through advanced analytics and visualization to ensure a more efficient and reliable energy supply to their end users.

Big Data Classifcation

Analysis type -  Real time  and Batch
Processing Methodology - Predictive Analysis, Analytics, Query and Reporting  which are used in processing of
Social Network Analysis, Location based analysis,Feature recognition,Text analytics, speech analytics etc
Data Frequency - On demand feeds, Continuous feeds, Real time feeds, Time series
Data type - Meta data, master data, Historical, Transactional
Content Format - Structured, un structured and semi structured
Data Sources - Web and social media , machine generated, human generated, internal data sources, Transaction data , Bio metric data,Data Providers
Data consumers -Human , Business process, Enterprise Applications
Hardware and Software 

Application and Data Management in Utilities – Cloud Computing

 “Cloud computing,” class of services refers to computing  resources (software or hardware) delivered as a service via the Internet. Cloud services  include the use of remote servers hosted on the Internet for accessing applications and/or storing, managing and processing data. Cloud computing offers a number of benefits. Foremost among these is the avoidance of big capital expenditures. There is no need to buy software, or the servers, racks and other hardware required to support it.

 The utility simply pays a “subscription” fee out of its operating budget. Plus, the vendor’s staff manages the solution, so you are guaranteed expert monitoring and support without additional labor costs. Early adoption of cloud computing confers other advantages, too. Many of the applications needed for integrating renewables and managing bi-directional energy flow are largely cloud based.

 As distributed generation becomes more common, utilities that adopt cloud computing will be ahead of the curve. They can also work with smart grid vendors to develop custom, cloud-based applications that address an array of needs –– from improving grid reliability to load forecasting.

Cloud computing can raise concerns over service integrity and reliability, data protection, and privacy. However, focusing on expert vendors will mitigate risks, and enable you to reap potential benefits. Seek vendors who deliver:

Visibility into their processes and controls

 Plans for disaster recovery and business continuity

 Physical security controls that are clear and auditable

Standards-compliant cyber security

Cloud Computing - Big Data needs

Business related owners are not technology or data geeks, but sometimes it feels like it would be beneficial to be one. Storing and analyzing data, keeping track o licenses and dealing with other technological hang ups can quickly leave the average businessperson feeling overwhelmed. Throw big data into the mix and the process becomes even more complex.Cloud computing technologies  can be a lifesaver when it comes to simplifying data storage and analysis.

Cloud Storage

Building up a reliable storage center is expensive and requires at least some level of expertise in that area, and small businesses are in short supply of both. Professional cloud storage, on the other hand, is highly affordable and offers a much more sophisticated option with no expertise required. Amazon’s SE storage service, for example, promises 99.9 percent monthly availability and even higher durability, meaning the service is rarely down, and there is an even smaller chance of data being lost. A small business could never hope to match that with their own resources.

Cloud Computing

Cloud resources are not limited to storage, however. It can also solve the majority of our computing needs. Depending on what your business’s needs are, cloud providers offer anything from the basic hardware to put your whole operating system on to ready-to-use applications. Big Data as a Service provides the tools to start collecting and analyzing big data without investing in Hadoop or learning how to code. Essentially, the cloud outsources your IT functions, so you no longer have to deal with setting systems up or making repairs. The cloud does all of this for you.

Scalability

A common problem business owners face is figuring out exactly how much data storage to use. After all, we don’t want to run out of space during a critical time, but it is a waste of resources to have servers sitting empty. Luckily, cloud providers offer scalability. They offer horizontal scaling, which replaces a small computing resource with a bigger one when demand requires it, as well as vertical scaling, which adds additional instances with each meeting part of the demand. This means that we no longer have to worry about resource planning or leave servers sitting empty, as the cloud service can automatically scale up or down depending on what is needed at the time.

Machine learning: analytics

Machine learning algorithms can help utilities address with a broad range of practical and strategic switch providers
meters problems, grid operations to accommodate fluctuating levels of renewable resources


MACHINE LEARNING EXAMPLE
 For instance,  some instances of energy theft identified, then you can feed into your analytics system data from those cases and have it look for similar patterns in current customer data. The system will return possible hits and offer a confidence rating for each. That confidence rating can help you decide whether you need to roll a truck to check out a certain instance of possible theft. Then, when you investigate the situation and feed the results back into your system, you fine-tune the algorithm."

Machine learning algorithms can also reveal new useful patterns in AMI data. That is, energy data can start to speak for itself, in ways that help utilities plan better.

For instance,  unnoticed patterns of momentary outages or other grid issues might help a utility better predict maintenance needs for transmission and distribution assets. "If you want to figure out which transformers will fail, you can feed in data about which ones have failed already, and the parts of the network they function within -- and then let the system reveal correlations,"

Utilities can start to capitalize on machine learning-enhanced analytics even if they haven't yet deployed smart meters or meter data management technology. 

Smart Grid in Street Lights

Street lighting is an important community service, it can consume as much as 40 percent of a city’s energy budget.  street lights are prone and costly to manage, which add to lighting costs. Consequently, street lighting has emerged as a leading smart city application.

By replacing existing street lights with LED-based lamps, utilities and other street light operators can cut energy and operations costs by 50 percent or more.

Networking those LEDs delivers an even faster return on investment (ROI), taking the payback period down to 6 vs. 8 years, as a result of features such as remote management and faster outage response.

In addition to near-term savings, a network-based lighting solution provides an ideal platform for multiple smart city services, including smart parking meters, traffic lights and traffic management systems. Municipal utilities also have the opportunity to leverage smart city infrastructure for smart grid applications such as advanced metering infrastructure (AMI), demand response (DR) and distribution automation (DA).

Understanding the operational details of networked LEDs and comparing those benefits and costs to traditional  lighting lays the foundation for building a business case to upgrade street lights. The hard dollar savings in energy and operational costs make the case for replacement, and networked LEDs provide additional community value as well.

 

 

The Advantages of Networked LEDs

Legacy high-pressure sodium and mercury street lamps are not energy efficient and typically operate 12 hours a day at full intensity,; so their energy cost is high. These lamps also have a short life span (around 5 years), resulting in unpredictable and expensive operations. Operators must replace roughly 20 percent of these lamps each year.

Currently, operators detect light outages either when a community member calls to report it or when mobile crews detect outages during periodic checks. Consequently, the time to replace a lamp can vary considerably, impacting public safety and an operator’s liability.

 New energy efficient LED-based street lights have a life span of up to 20 years, enabling lower energy and operations costs. . In order to take full advantage of this new LED technology, these street lights must be networked. Operators benefit from lower energy and operations costs, which can be reduced even further when street lights are connected to a network.

Networking gives operators remote access and advanced functionality, including the ability to dim street lights  and control their runtime by scheduling them to switch on/off as conditions (such as shorter/longer days) warrant. This network-based control yields an additional 10 to 20 percent energy savings beyond just LED replacement, along with greater operations and management savings.

For example, since LEDs burn brighter than conventional street lamps, operators can dim them to 50 percent brightness for additional energy savings with minimal compromise in light output. And, by controlling street light runtime remotely, operators also have the option to eliminate photocells for further cost reduction.

 

Benefits

Energy Savings

Low wattage , Dimming and Reduced Burn Time

Operational Savings

Long life time, Remote Monitoring and  Management , Automatic Outage detection, Proactive Maintanence

SAP Big data in Utilities

SAP Big data in Utilities

 

 

1. SAP Big Data = SAP HANA + Hadoop +..... =  Transactions + Analytics

 

SAP HANA is the key ingredient for SAP's big data solution, aided by other software like Hadoop. All the innovations in utilities relates to Real time data for Operations , Meter Data Management  and so on of HANA, together with all the benefits of technologies like Hadoop and Sybase IQ. When rest of the world thinks of big data - it is mostly along the lines of analytical applications. But SAP big data is positioned for analytical AND transactional applications.

 

2. SAP Big Data = Big Value + Big Easy

 

That is essentially the "big deal" about big data. SAP will make it easy for customers to handle Volume, velocity , variety etc of data, and give very sophisticated analytics via our SAP Hana data platform. The value of big data is the quality of insights you get from it - and that needs more sophistication than conventional BI. And we will make it easy to use - easy to administer, easy to consume, easy to extend and so on. You choose the deployment model that is right for you - keep it inhouse, or move it to Hana Enterprise Cloud.

 

3. SAP Big Data = Big Precision + Big Context

Historically, BI was focused on precision, with probably an assumption that context is provided elsewhere for the user. Big data will change that. Now you can have the great precision that our BI platform provides, and put it in a context that is useful for the people consuming the insights. Not only will you know exactlyhow much to collect from your customers for their purchases, you will also know what else is happening with that customer within your company, and in the external world.

 

4.SAP  Big Data = Right Time + Infinite growth

 

Quality of insights lead to actions only when it is delivered at the right time. In many cases, right time is real time. By combining the power of SAP Hana for lightning fast responses ( with deep predictive abilities etc), and the almost limitless ability of hadoop to store and process data - you get the best of both worlds. And it is not just Hana and Hadoop - there are other rock solid , highly scalable systemslike Sybase IQ that you can tap into using our platform.

 

5.SAP  Big Data = Real time Data

 

Conventional applications are built on predefined requirements. You figure out the queries, the data model , the ETL and so on in great detail before an app gets built. The downside of this process is that it takes a long time , and it works only for predefined questions. With big data, SAP brings the IP of the hundreds of data science projects (including things cool stuff like machine learning) that gives you a jump start on getting value out of big data initiatives

 

6. SAP Big Data = Platform + Applications + Data Science + Deployment

 

SAP provides a platform that is easy to develop on (with plenty of educational materials available) , solid integration from Hana to other big data solutions like Hadoop and IQ, top of the line prebuilt applications (which can be extended as needed ) , a large pool of the best data scientists you can find anywhere, and a choice of deployment options. And not only that - we are making all of these better in a continuous process.

 

 

Supervisory Control and Data Acquisition Systems(SCADA) with Energy Management Systems (EMS) and Distribution Management Systems (DMS)

What is SCADA/EMS/GMS and SCADA/DMS?
SCADA/EMS/GMS (supervisory control and data acquisition/Energy Management SyestemsGeneration Management System) supervises, controls, optimizes and manages generation and transmission systems. SCADA/DMS Distribution Management systems performs the same functions for power distribution networks.

Both systems enable utilities to collect, store and analyze data from hundreds of thousands of data points in national or regional networks, perform network modeling, simulate power operation, pinpoint faults, preempt outages, and participate in energy trading markets.


1960’s  SCADA/EMS/GMS systems at that time were designed exclusively for a single customer. Power systems were vulnerable, and there was a need to develop applications and tools for preventing faults from developing into large-scale outages. 

In the 1980s it became possible to model large-scale distribution networks in a standardized way. The deregulation and privatization of the power industry that began in the 1990s was the biggest structural change in the industry’s history. Specialization became increasingly common, with many utilities focusing on either generation, transmission or distribution. 

 Smart Metering and Smart Grid technologies impacts ,Network management is a prerequisite and vital for any smart grid of the future. These grids will have to incorporate and manage centralized and distributed power generation, intermittent sources of renewable energy like wind and solar power, allow consumers to become producers and export their excess power, enable multi-directional power flow from many different sources, and integrate real-time pricing and load management data. 

Digital transformation in Utilities through Analytics

Analytics in Utilites will impact  the Operational and business models over the next few years

More and more data will be generated by Grid and Customer applications,Analytics play a vital role in deriving the efficiency by various tools.

1.       Long term analytic strategy : All the current systems in Utilities are project based  . Long term strategy would be to concentrate on the fusion of Operational  technology with Information Technology by leveraging analytics . Implemetation of Analytic tools will improve the business value

2.       Customer Engagement Models and services : The current model of communication should be changed to advanced Customer communication  with Multiple channels . Enablement of Social networking channels like facebook, twitter via mobile integration in utilities will have better insights and satisfaction results . Example Outages

3.       Optimize The Grid : Utilities must evaluate the Grid and Optimize for better efficiency. Advanced Analytics strategies and tools will stream line the data for better results.

Smart Grid Enablement in India

The enablers for a smart grid in India include:

·         Supervisory Control and Data Acquisition Systems(SCADA) with Energy Management Systems (EMS)

·          Distribution Management Systems (DMS) Enterprise IT network covering all substations and field offices with reliable communication systems

·         Enterprise Resource Planning(ERP)/Asset Management Systems

·         Geographic Information Systems (GIS) – mapping of electrical network assets and consumers on geospatial maps 

·         Modernization of the substations with modern switchgear and numerical relays

·         Advanced Metering Infrastructure (AMI) with two way communication and Meter Data Management Systems(MDMS)

·         Electronic Billing Systems and Customer Care Systems

·         Distribution Automation (DA) and Substation Automation Systems

·         Outage Management Systems (OMS)

·         Mobile Crew Management Systems/Mobile Workforce Management

·         Wide Area Measurement and Control Systems

·         Forecasting, Dispatch and Settlement Tools

·         Enterprise Application Integration – SAP , Cloud Computing

·         Analytics (converting data into business intelligence)  -Big Data and Analytics