In my last blog, I wrote about the forward-looking questions utilities can ask their backward-looking data and improve upon it with real-time information sources.
To some degree, all these sorts of analytic progressions already occur at most utilities, but suspicions about the levels of accuracy often dissuade executives from taking decisive action.
Suspicions about accuracy have dogged decisive action for quite a while now. Consider a 1982 study conducted by the US General Accounting Office for the U.S. Congressí Subcommittee on Energy Conservation and Power Committee titled " Analysis of Electric Utility Load Forecasting."
(As an aside, I absolutely loved reading this report as a piece of utility history. Think about it. The report was written before computing power was dispersed on desktops, when state-of-the-art data storage meant large rooms filled with tape machines. Even the typeface font used in the report is quaint, probably coming right off an IBM Selectric, to my non-expert, but age-experienced eye.)
The report is fascinating because it summarizes the utility energy forecasting methodologies that were considered cutting edge at the time:
Trend Forecasting "predicts future power demand by assuming that the factors that influenced demand in the past will continue to do so in the same way in the future." Certainty was affected by the fact that past results donít guarantee identical future outcomes.
"Econometric forecasting uses mathematical equations based on the relationship between past demand and economic and demographic conditions to forecast future demand." The assumption that the relationship will continue into the future made this method weak.
End-use forecasting breaks electricity consumption into the residential, commercial, and industrial demand profiles. While the data for this type of forecasting was considered in 1982 to be "expensive and time consuming to collect and maintain," the advantage was that it readily reflects changes in consumer tastes, increased efficiency of energy-using products, and changes in the economy, particularly technological shifts in our industrial base. But the ability to utilize this sort of approach was questionable given the technologies of the time.
A sum-of-the-utilities forecast gives regional or national perspective on utility demand as a combination of individual utility forecasts. "[T]his approach, because of its aggregated nature, is of limited use to individual utilities in planning new resources because the service areas are significantly smaller than the area covered by the forecast." Enough said.
So weíve looked at limitations of forecasting as perceived in 1982. Letís compare that to the forecasting capabilities that are considered important 31 years later, and the confidences asserted in their likely accuracy, as contained in a recent press release from SAS, the sponsor of this site, about its new energy forecasting solution for utilities. Here are a few of the productís selling points along with my commentary:
"[The new solution] helps utilities operate more efficiently and effectively by capitalizing on new interval data being returned from smart meters." Smart meters werenít included in the 1982 report. But the commission understood certain modern appliances had energy usage profiles that could be discovered, albeit with expense and time. My how smart meters and the development of energy usage profiles for every appliance have increased the possibility for confidence in energy forecasting.
"Unlike other load forecasting software, SAS Energy Forecasting supports multiple planning horizons -- from the next hour to the next 50 years. Utilities can leverage big data from smart meters, power plants and other sources to produce accurate and timely forecasts of short- and long-term load and demand. This helps the utilities better trade energy on the open market, while optimally managing power plants, generators and other assets." Ah, yes, big data -- a term and concept not even created in 1982. But it addresses so many of the factors that contributed to the low levels of confidence that utility manager once had.
"Utilities have successfully used forecasting in the past. Todayís new challenges, including the added complexity of wind and solar power generation, require even greater attention to the data sets and models that feed those forecasts." Once again, to be effective today, forecasting must include consideration of volatile wind and solar sources, as part of a US priority to include more renewable power sources in its mix.
How times have changed. Each of the three new energy forecasting considerations endeavors to increase confidence in methodology and capability to address modern challenges. I wonder how a similar "Analysis of Electric Utility Load Forecasting" report might be written today. Any ideas?
I was just reading about how the use of visual analytics is coming into play for preventive asset management of big manufacturing systems or really anything big with structure -- aircraft, ships, trains, buildings, bridges, and so on. Sensors are delivering vast amounts of data, as is the case in weather systems. I wonder if being able to explore the data visually will help researchers in finding patterns and understanding results in energy fields as well. I would think the application would be perfect in the energy industry as you describe it.
My thought is "No, alternative energy data sets are not readily enough available." It's my opinion that the data histories for weather are so colossal that the industry is struggling with its Big Data conundrums. Weather science algorithms are incredibly complex and its even possible that major factors, like sun spot influences, aren't even being considered by many scientists over the past decade or so to the degree that some scientists believe they should. The use of analytics to deliver data analysis with regard to alternative energy, in bite-sized chunks, will be a ongoing exercise of incredible value -- and difficulty.
Joe, interesting stuff here. We've come a long way in 30 years, that's for sure. And while I agree with you that new forecasting considerations are boosting confidence and capabilities, I have to wonder if we've come far enough. I'm especially wondering whether alternative energy data sets are readily enough evailable and being taken advantage of in energy modeling. Your thoughts?
2015 Visual Analytics Interactive RoadshowSAS(r) experts are coming to a city near you in a series of live, interactive workshops focused on SAS Visual Analytics, including how to prepare your data for VA, the integration of VA with Office Analytics and a Visual Statistics demo.
January 22: King of Prussia, PA
February 24: Austin, TX
March 26: Redwood City, CA
April 22: NYC, NY (1st of 2 stops)
May 13: Seattle, WA
June 18: Minneapolis, MN
July 21: Rockville, MD
August 18: Chicago, IL
September 24: Irvine, CA
October 9: Cary, NC (during SAS Championship)
October 21: NYC, NY (2nd of 2 stops)
November 17: Orlando, FL
December 8: Atlanta, GA
LEADERS FROM THE BUSINESS AND IT COMMUNITIES DUEL OVER CRITICAL TECHNOLOGY ISSUES
The Current Discussion
Visual Analytics: Who Carries the Onus? The Issue: Data visualization is an up-and-coming technology for businesses that want to deliver analytical results in a visual way, enabling analysts the ability to spot patterns more easily and business users to absorb the insight at a glance and better understand what questions to ask of the data. But does it make more sense to train everybody to handle the visualization mandate or bring on visualization expertise? Our experts are divided on the question. The Speakers: Hyoun Park, Principal Analyst, Nucleus Research; Jonathan Schwabish, US Economist & Data Visualizer
The hospitality industry gathers massive amounts of customer data, and mining that data effectively can yield tremendous results in terms of improved CRM, better-targeted marketing spend, and more efficient back-end processes. Roger Ares, vice president of analytics at Hyatt Corp., discusses the ways he and his staff use big data.
Charged with keeping track of travel assets, including employees, iJET International relies on data management best-practices and advanced analytics to keep its clients in the know on current and potential world events affecting travel, Rich Murnane, Director of Enterprise Data Operations & Data Architect, told All Analytics in an interview from the 2014 SAS Global Forum Executive Conference.
Jason Dorsey, chief strategy officer for the Center for Generational Kinetics and keynote speaker at last month's SAS Global Forum 2014, describes how Gen Y professionals are enhancing the makeup of multigenerational analytics organizations.
From analytics talent development to the power of visual analytics, All Analytics found a variety of common themes circulating throughout the exhibition floor and session discussions at the 2014 SAS Global Forum and SAS Global Forum Executive Conference events held last month in Washington, DC.
Talking with All Analytics live from the 2014 SAS Global Forum Executive Conference, Eric Helmer, senior manager of campaign design and execution for T-Mobile, discussed the importance of customer data -- starting internally -- in devising the mobile operator's marketing plans.
The big-data analytics market can be a confusing place. Among the vendors vying for your dollars are traditional database management providers, Hadoop startup services, and IT giants. In this video, All Analytics editors Beth Schultz and Michael Steinhart sit down in a Google+ Hangout on Air with Doug Henschen, executive editor of InformationWeek. Henschen discusses use cases for big-data analytics, purchase considerations, and his recent roundup of the top 16 big-data analytics platforms.
At the National Retail Federation BIG Show last month, All Analytics executive editor Michael Steinhart noted a host of solutions for tracking and analyzing customer activity in retail stores. From Bluetooth beacons to RFID tags to NFC connections to video analytics, retailers must find the right combination of tools to help optimize the shopper experience, streamline operations, and boost revenues.
The days when historical shipment trends and gut feelings were enough to forecast retail demand accurately are long over. SAS chief industry consultant Charles Chase outlines the benefits of pulling real-time sales information from point-of-sale and product scanner systems, then flowing that data into dynamic forecasting tools from SAS.
With today's advanced visual analytics tools, you can stream data into memory for real-time processing, provide users the ability to explore and manipulate the data, and bring your data to life for the business.
Dynamic data visualizations let analysts and business users interact with the data, changing variables or drilling down into data points, and see results in a flash. Advance your use of data visualization with tools that support features like auto-charting, explanatory pop-ups, and mobile sharing.
No doubt your enterprise is amassing loads of data for fact-based decision-making. Hand in hand with all that data comes big computational requirements. Can traditional IT infrastructure handle the increasing number and complexity of your analytical work? Probably not, which is why you need a backend rethink. Big data calls for a high-performance analytics infrastructure, as Fern Halper, a partner at the IT consulting and research firm, Hurwitz & Associates, discusses here.
Redbox's bright-red DVD kiosks are all but ubiquitous these days, located in more than 28,000 spots across the country. Jayson Tipp, Redbox VP of Analytics and CRM, provides an insider's look at how the company has accomplished its phenomenal nine-year growth.
InterContinental Hotels Group (IHG), a seven-brand global hotelier, has woven analytics into the fabric of its operations. David Schmitt, director of performance strategy and planning, shares IHG's analytics story and his lessons learned.