A man with one watch always knows what time it is...but a man with two watches is never sure. Contrary to this adage, load forecasters at electric utilities would gladly wear an armful of watches. With only one model to choose from, it is certain that some forecasts will be wrong. But with multiple models, forecasters can have confidence about periods when the forecasts agree and can focus their attention on periods when the predictions diverge. Having a second opinion is preferred, and that's one of the six classic rules for forecasters as per Dr. Tao Hong of the University of North Carolina at Charlotte. Dr. Hong is the premiere thought leader and practitioner in the field of energy forecasting. This presentation discusses Dr. Hong's six rules, how they relate to the increasingly complex problem of forecasting electricity consumption, and the role that predictive analytics plays.

Location information plays a big role in business data. Everything that happens in a business happens somewhere, whether it s sales of products in different regions or crimes that happened in a city. Business analysts typically use the historic data that they have gathered for years for analysis. One of the most important pieces of data that can help answer more questions qualitatively, is the demographic data along with the business data. An analyst can match the sales or the crimes with the population metrics like gender, age groups, family income, race, and other pieces of information, which are part of the demographic data, for better insight. This paper demonstrates how a business analyst can bring the demographic and lifestyle data from Esri into SAS^® Visual Analytics and join the data with business data. The integration of SAS Visual Analytics with Esri allows this to happen. We demonstrate different methods of accessing Esri demographic data from SAS Visual Analytics. We also demonstrate how you can use custom shape files and integrate with Esri Portal for ArcGIS.

Customer churn is an important area of concern that affects not just the growth of your company, but also the profit. Conventional survival analysis can provide a customer's likelihood to churn in the near term, but it does not take into account the lifetime value of the higher-risk churn customers you are trying to retain. Not all customers are equally important to your company. Recency, frequency, and monetary (RFM) analysis can help companies identify customers that are most important and most likely to respond to a retention offer. In this paper, we use the IML and PHREG procedures to combine the RFM analysis and survival analysis in order to determine the optimal number of higher-risk and higher-value customers to retain.

Data that are gathered in modern data collection processes are often large and contain geographic information that enables you to examine how spatial proximity affects the outcome of interest. For example, in real estate economics, the price of a housing unit is likely to depend on the prices of housing units in the same neighborhood or nearby neighborhoods, either because of their locations or because of some unobserved characteristics that these neighborhoods share. Understanding spatial relationships and being able to represent them in a compact form are vital to extracting value from big data. This paper describes how to glean analytical insights from big data and discover their big value by using spatial econometric methods in SAS/ETS^® software.

Coming off a recent smart grid implementation, OGE Energy Corp. was collecting more data than at any time in its history. This data held the potential to help the organization uncover new insights and chart new paths. Find out how OGE Energy is building a culture of data analytics by using SAS^® tools, a distributed analytics model, and an analytics center of excellence.

For all business analytics projects big or small, the results are used to support business or managerial decision-making processes, and many of them eventually lead to business actions. However, executives or decision makers are often confused and feel uninformed about contents when presented with complicated analytics steps, especially when multi-processes or environments are involved. After many years of research and experiment, a web reporting framework based on SAS^® Stored Processes was developed to smooth the communication between data analysts, researches, and business decision makers. This web reporting framework uses a storytelling style to present essential analytical steps to audiences, with dynamic HTML5 content and drill-down and drill-through functions in text, graph, table, and dashboard formats. No special skills other than SAS^® programming are needed for implementing a new report. The model-view-controller (MVC) structure in this framework significantly reduced the time needed for developing high-end web reports for audiences not familiar with SAS. Additionally, the report contents can be used to feed to tablet or smartphone users. A business analytical example is demonstrated during this session. By using this web reporting framework based on SAS Stored Processes, many existing SAS results can be delivered more effectively and persuasively on a SAS^® Enterprise BI platform.

Detection and adjustment of structural breaks are an important step in modeling time series and panel data. In some cases, such as studying the impact of a new policy or an advertising campaign, structural break analysis might even be the main goal of a data analysis project. In other cases, the adjustment of structural breaks is a necessary step to achieve other analysis objectives, such as obtaining accurate forecasts and effective seasonal adjustment. Structural breaks can occur in a variety of ways during the course of a time series. For example, a series can have an abrupt change in its trend, its seasonal pattern, or its response to a regressor. The SSM procedure in SAS/ETS^® software provides a comprehensive set of tools for modeling different types of sequential data, including univariate and multivariate time series data and panel data. These tools include options for easy detection and adjustment of a wide variety of structural breaks. This paper shows how you can use the SSM procedure to detect and adjust structural breaks in many different modeling scenarios. Several real-world data sets are used in the examples. The paper also includes a brief review of the structural break detection facilities of other SAS/ETS procedures, such as the ARIMA, AUTOREG, and UCM procedures.

Creating an environment that enables and empowers self-service and agile analytic capabilities requires a tremendous amount of working together and extensive agreements between IT and the business. Business and IT users are struggling to know what version of the data is valid, where they should get the data from, and how to combine and aggregate all the data sources to apply analytics and deliver results in a timely manner. All the while, IT is struggling to supply the business with more and more data that is becoming available through many different data sources such as the Internet, sensors, the Internet of Things, and others. In addition, once they start trying to join and aggregate all the different types of data, the manual coding can be very complicated and tedious, can demand extraneous resources and processing, and can negatively impact the overhead on the system. If IT enables agile analytics in a data lab, it can alleviate many of these issues, increase productivity, and deliver an effective self-service environment for all users. This self-service environment using SAS^® analytics in Teradata has decreased the time required to prepare the data and develop the statistical data model, and delivered faster results in minutes compared to days or even weeks. This session discusses how you can enable agile analytics in a data lab, leverage SAS analytics in Teradata to increase performance, and learn how hundreds of organizations have adopted this concept to deliver self-service capabilities in a streamlined process.

Would you like to be more confident in producing graphs and figures? Do you understand the differences between the OVERLAY, GRIDDED, LATTICE, DATAPANEL, and DATALATTICE layouts? Finally, would you like to learn the fundamental Graph Template Language methods in a relaxed environment that fosters questions? Great this topic is for you! In this hands-on workshop, you are guided through the fundamental aspects of the GTL procedure, and you can try fun and challenging SAS^® graphics exercises to enable you to more easily retain what you have learned.

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Do you need to add annotations to your graphs? Do you need to specify your own colors on the graph? Would you like to add Unicode characters to your graph, or would you like to create templates that can also be used by non-programmers to produce the required figures? Great, then this topic is for you! In this hands-on workshop, you are guided through the more advanced features of the GTL procedure. There are also fun and challenging SAS^® graphics exercises to enable you to more easily retain what you have learned.

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The traditional view is that a utility's long-term forecast must have a standard against which it is judged. Weather normalization is one of the industry-standard practices that utilities use to assess the efficacy of a forecasting solution. While recent advances in probabilistic load forecasting techniques are proving to be a methodology that brings many benefits to a forecast, many utilities still require the benchmarking process to determine the accuracy of their long-term forecasts. Due to climatological volatility and the potentially large annual variances in temperature, humidity, and other relevant weather variables, most utilities create normalized weather profiles through various processes in order to estimate what is traditionally called a weather normalized load profile. However, new research shows that due to the nonlinear response of electric demand to weather variations, a simple normal weather profile in many cases might not equate to a normal load. In this paper, we introduce a probabilistic approach to deriving normalized load profiles and monthly peak and energy in through a process we label load normalization against the effects of weather . We compare it with the traditional weather normalization process to quantify the costs and benefits of using such a process. The proposed method has been successfully deployed at utilities for their long-term operation and planning purposes, and risk management.

This presentation demonstrates that applying Fast Fourier Transformation (FFT) on smart meter data can provide enhanced customer segmentation and discovery. The FFT is a mathematical method for transforming a function of time into a function of frequency. It's vastly used in analyzing sound but is also relevant for utilities. Advanced Metering Infrastructure (AMI) refers to the full measurement and collection system that includes meters at the customer site and communication networks between the customer and the utility. With the inception of AMI, utilities experienced an explosion of data that provides vast analytical opportunities to improve reliability, customer satisfaction, and safety. However, the data explosion comes with its own challenges. The first challenge is the volume. Consider that just 20,000 customers with AMI data can reach over 300 GB of data per year. Simply aggregating the data from minutes to hours or even days can skew results and not provide accurate segmentations. The second challenge is the bad data that is being collected. Outliers caused by missing or incorrect reads, outages, or other factors must be addressed. FFT can eliminate this noise. The proposed framework is expected to identify various customer segments that could be used for demand response programs. The framework also has the potential to investigate diversion or fraud or failing meters (revenue protection), which is a big problem for many utilities.

When analyzing data with SAS^®, we often use the SAS DATA step and the SQL procedure to explore and manipulate data. Though they both are useful tools in SAS, many SAS users do not fully understand their differences, advantages, and disadvantages and thus have numerous unnecessary biased debates on them. Therefore, this paper illustrates and discusses these aspects with real work examples, which give SAS users deep insights into using them. Using the right tool for a given circumstance not only provides an easier and more convenient solution, it also saves time and work in programming, thus improving work efficiency. Furthermore, the illustrated methods and advanced programming skills can be used in a wide variety of data analysis and business analytics fields.

Every organization, from the most mature to a day-one start-up, needs to grow organically. A deep understanding of internal customer and operational data is the single biggest catalyst to develop and sustain the data. Advanced analytics and big data directly feed into this, and there are best practices that any organization (across the entire growth curve) can adopt to drive success. Analytics teams can be drivers of growth. But to be truly effective, key best practices need to be implemented. These practices include in-the-weeds details, like the approach to data hygiene, as well as strategic practices, like team structure and model governance. When executed poorly, business leadership and the analytics team are unable to communicate with each other they talk past each other and do not work together toward a common goal. When executed well, the analytics team is part of the business solution, aligned with the needs of business decision-makers, and drives the organization forward. Through our engagements, we have discovered best practices in three key areas. All three are critical to analytics team effectiveness. 1) Data Hygiene 2) Complex Statistical Modeling 3) Team Collaboration

Whether you are a current SAS^® Marketing Optimization user who wants to fine tune your scenarios, a SAS^® Marketing Automation user who wants to understand more about how SAS Marketing Optimization might improve your campaigns, or completely new to the world of marketing optimizations, this session covers ideas and insights for getting the highest strategic impact out of SAS Marketing Optimization. SAS Marketing Optimization is powerful analytical software, but like all software, what you get out is largely predicated by what you put in. Building scenarios is as much an art as it is a science, and how you build those scenarios directly impacts your results. What questions should you be asking to establish the best objectives? What suppressions should you consider? We develop and compare multiple what-if scenarios and discuss how to leverage SAS Marketing Optimization as a business decisioning tool in order to determine the best scenarios to deploy for your campaigns. We include examples from various industries including retail, financial services, telco, and utilities. The following topics are discussed in depth: establishing high-impact objectives, with an emphasis on setting objectives that impact organizational key performance indicators (KPIs); performing and interpreting sensitivity analysis; return on investment (ROI); evaluating opportunity costs; and comparing what-if scenarios.

Production forecasts that are based on data analytics are able to capture the character of the patterns that are created by past behavior of wells and reservoirs. Future trends are a reflection of past trends unless operating principles have changed. Therefore, the forecasts are more accurate than the monotonous, straight line that is provided by decline curve analysis (DCA). The patterns provide some distinct advantages: they provide a range instead of an absolute number, and the periods of high and low performance can be used for better planning. When used together with DCA, the current method of using data driven production forecasting can certainly enhance the value tremendously for the oil and gas industry, especially in times of volatility in the global oil and gas industry.

A Middle Eastern company is responsible for daily distribution of over 230 million liters of oil products. For this distribution network, a failure scenario is defined as occurring when oil transport is interrupted or slows down, and/or when product demands fluctuate outside the normal range. Under all failure scenarios, the company plans to provide additional transport capacity at minimum cost so as to meet all point-to-point product demands. Currently, the company uses a wait-and-see strategy, which carries a high operating cost and depends on the availability of third-party transportation. This paper describes the use of the OPTMODEL procedure to implement a mixed integer programming model to model and solve this problem. Experimental results are provided to demonstrate the utility of this approach. It was discovered that larger instances of the problem, with greater numbers of potential failure scenarios, can become computationally extensive. In order to efficiently handle such instances of the problem, we have also implemented a Benders decomposition algorithm in PROC OPTMODEL.

In industrial systems, vibration signals are the most important measurements for indicating asset health. Based on these measurements, an engineer with expert knowledge about the assets, industrial process, and vibration monitoring can perform spectral analysis to identify failure modes. However, this is still a manual process that heavily depends on the experience and knowledge of the engineer analyzing the vibration data. Moreover, when measurements are performed continuously, it becomes impossible to act in real time on this data. The objective of this paper is to examine using analytics to perform vibration spectral analysis in real time to predict asset failures. The first step in this approach is to translate engineering knowledge and features into analytic features in order to perform predictive modeling. This process involves converting the time signal into the frequency domain by applying a fast Fourier transform (FFT). Based on the specific design characteristics of the asset, it is possible to derive the relevant features of the vibration signal to predict asset failures. This approach is illustrated using a bearing data set available from the Prognostics Data Repository of the National Aeronautics and Space Administration (NASA). Modeling is done using R and is integrated within SAS^® Asset Performance Analytics. In essence, this approach helps the engineers to make better data-driven decisions. The approach described in this paper shows the strength of combining ex