Looking for use cases for analytics to derive value at an electrical utility? We have identified over 125 ways you can use analytics to improve the business processes at an electric utility. I recently posted a series of blog posts discussing four different use cases.

Now I'd like to share list of papers from the last eight years written by analysts, data scientists and other employees and utility companies.

I've included the title and a quick excerpt from each one below, with topics ranging from visualizing smart meter data to predicting risk from weather data. I hope these give you some new ideas on how you can use analytics at your utility:

  • Distribution Circuit Load Forecasting Using Advanced Metering Infrastructure Data
    "The ability to perform very short to very long-term forecasts of distribution circuit loads at intermediate distribution circuit locations between customer meters and substation feeder buses using AMI (Advanced Metering Infrastructure) data provides significant advantages to distribution system planners and operators in a number of areas."
  • Improving Financial Reporting Accuracy Using Smart Meter Data
    "Since financial reporting requires accurate and consistent methods for estimating revenue, utilities should consider using daily consumption data from AMI or AMR."
  • Managing Real-Time Data Streams to High-Performance Analytics Engines
    "This paper explains how to manage real-time streaming data in a batch processing analytics engine.
    You will learn how to store and manage streaming data in such a way as to: guarantee the analytics engine has only current information, limit interruptions to data access, avoid duplication of data, and maintain a historical record of events."
  • Agile BI: How Eandis is using SAS® Visual Analytics for Energy Grid Management                            
    "Eandis, a Belgian energy distribution grid operator, is using Visual Analytics to explore data from various sources, including SAP Business Warehouse, to better plan maintenance and investments in the grid. To achieve this, a new agile way of thinking about Business Intelligence was needed."
  • Meter Data Analytics--Enabling Actionable Decisions to Derive Business Value from Smart Meter Data
    "Thirty thousand meters collecting fifteen-minute-interval data with forty variables equates to 1.2 billion rows of data. Using SAS®
    Visual Analytics, we provide examples of leveraging smart meter data to address business around revenue protection, meter operations, and customer analysis."
  • Using SAS/OR® to Optimize the Layout of Wind Farm Turbines
    "The basic goal of wind farm layout optimization is to find the best layout for a wind farm in order to maximize the total net power of its wind turbines. This paper presents a hybrid approach to solving this problem."
  • Improving the Thermal Efficiency of Coal-Fired Power Plants: A Data Mining Approach
    This study demonstrates that data mining based approaches can be used to assess predictor variables
    influencing the stack opacity emission and heat rate in the energy generation process. As opposed to the traditional descriptive statistical analysis methods or the approaches adopting only expert-selected variables, the employment of regression, decision trees, or neural network models provide an interesting factors to understand the variation in both heat rate and opacity emission generated.
  • Customer Perception and Reality: Unraveling the Energy Customer Equation
    "In order to survive in a competitive business environment it is crucial for companies to be customer-centric; one expression of this centricity is having a clear understanding of how well the products and services they provide meet customer expectations. Such understanding simply can’t be acquired unless customers’ perceptions of products and services are measured, analyzed, and acted upon in a timely fashion."
  • Residential Energy Efficiency and the Principal-Agent Problem
    "Investments in residential energy-efficiency are associated with both positive externalities (reduced greenhouse gas emissions, reduced need for new power-generating capacity) and private cost savings. However, these investments lag far behind even those levels predicted by conventional cost-benefit analysis. This paper explores one potential explanation for this “efficiency gap”-the principal-agent (PA) problem as it applies to rental housing."
  • Smarter Grid Operations with SAS/OR
    ""Between the time electricity leaves the utility generators and reaches your home or business, 7% of the energy has been dissipated as heat. For the average utility, this represents a total loss of more than $75 million each year. This paper discusses the use of SAS/OR to control device switching to optimize the operations of the electrical distribution system."
  • SAS® for Green Energy Solutions in Smart Electric Grid Systems
    This research paper proposes a data-analytic approach for making optimum utilization of solar energy generated by solar photovoltaic panels to reduce peak demand on advanced electric grid systems."
  • Optimal Data Management for Utility AMI: Smart Grid Data
    This presentation will discuss the need for proper data storage techniques to achieve optimal results for high value analytics involved in AMI/Smart Grid data in a utility setting. This paper will highlight data collection, data quality, analytics design and data delivery."
  • Quantifying Energy Risk Exposure Using SAS: Analyzing the Impact of the Weather and the Economy during Volatile Time
    Even small improvements can have a significant impact on overall profitability. For example, accounting for the overall economics, a 1% improvement in either revenue forecasting or risk management can translate into an increase in overall profitability worth millions of dollars."

I hope these papers prove that you can’t have a smart grid without analytics. For more about SAS in utilities, visit our utilities analytics page. 


About Author

David Pope

Technical Leader, Senior Manager US Energy

David leads the pre-sales technical team for SAS US Energy which solves business problems in the Oil & Gas and Utilities industries using advanced analytics. He earned a BS in Industry Engineering and a Computer Programming Certificate from North Carolina State University. Furthermore, he has over 27 years of business experience working with SAS across R&D, IT, Sales and Marketing in the Americas and Europe. He is an expert in working with data and producing insights through the use of analytics. David has presented at SAS Global Forum, the 2012 SAS Government Leadership Summit, IBM’s Information on Demand(IOD), EMC World, CTO Summit Conferences, is the author of the book: "Big Data Analytics with SAS", and he currently holds 11 patents for SAS in several countries: US, CA, Norway, UK, China, and Hong Kong.

Leave A Reply

Back to Top