Forecasting optimizes energy production

innogy Poland predicts customers' electricity needs, streamlines operations, reduces costs

The core challenge for utility company innogy Poland was balancing its customers' likely next-day demands for electricity with its ability to generate or purchase power on the day-ahead market. innogy Poland was unable to go to intra-day markets for spot purchases, so making good next-day calculations were imperative to its profit and loss operations.

Improving forecasts to reduce "balancing costs" is a complex exercise that's complicated by seemingly infinite variables. How many customers will be using electricity at each moment in time? How will they be using it? How will hot or cold weather affect their demand? How will the economic environment increase or decrease usage? How many unique customers are we serving throughout the day?

innogy Poland was attempting to answer all those questions using the homegrown rule-of-thumb estimations that often reside within each separate department. The challenge was that each small digression from actual facts creates ever-larger matching and coordination problems, especially for the day-ahead contracting this utility needed. In addition, it had some data from smart meters to use for forecasting load, but other data that had to be fit in the forecast to get a holistic picture of energy demand.

Closing the forecast gap

To solve its knowledge needs, innogy Poland began to look for more statistically-defensible approaches that provide portfolio analysis to calculate standard load profiles with higher degrees of confidence. An increase in accuracy would reduce balancing costs – positively impacting the bottom line.

To accomplish this goal, innogy Poland quickly discovered that it also needed to clean, manage and structure its customer data to be useful. By first understanding its customers, the utility could get a better grasp of the variables affecting them and their use of electricity and incorporate that dynamic into its planning. Unfortunately, the data in innogy Poland's customer information systems was in bad shape and continued to degrade in quality as new, large volumes of usage data was incorporated by smart meters and automated metering infrastructure.

Data and forecasting go hand-in-hand

To do a better job of combining these inputs, innogy Poland realized that it needed a holistic approach to collecting data from these internal and external data sources and converting it into useful knowledge. Now, an analytical data repository built in SAS is a centralized source of intelligence for the organization. The repository is widely adopted throughout the organization because it was led by the business, with support from IT.

Next came the implementation of SAS® Demand-Driven Forecasting. During this phase of the project, innogy Poland used the flexible SAS® Data Integration tools to aggregate and integrate different types of data coming from multiple lines of business in order to achieve a more comprehensive vision of overall business management.

SAS Demand-Driven Forecasting provides the utility with consensus forecasting in conjunction with the sales and operations planning processes. The solution worked by providing hierarchical forecasting for hundreds of thousands of data series and also synchronizing and allocating forecasts from any level within the hierarchy.

Of special relevance to every utility's minute-by-minute demand profile, the forecasting method included time series methods such single exponential smoothing, Holt's/Brown's two parameter exponential smoothing, and Winter's three parameter exponential smoothing.

The variables involved in utility forecast are handled by causal methods such as ARIMAX (ARIMA with intervention and causal variables), lagged variables/transfer functions, dynamic multiple regression, and the Unobserved Components Model.

Using these statistical analyses, SAS provided the forecasts that reflected the contingencies of innogy Poland's business, improving its planning accuracy. The solution offered additional value because it went a step further, automatically generating reports that indicated the gaps between the financial plan and all individual, departmental and statistical baseline forecasts, with notes indicating reasons. These reports can be reviewed, changed and written back to the data model and offered a compelling ongoing corrective loop for the ever-changing dynamics involved in electricity demand and supply.

Additional benefits

In addition to achieving more accurate short-, mid- and long-term forecasts, innogy Poland was inspired by the possibilities created by its customer data cleansing effort. The utility quickly envisioned how it could use this improved data set to identify technical losses that may have been occurring in the distribution system.

Technical losses occur because of the add-on engineering processes of expanding utility systems, in this case in a fast-growing economy. The savings that could be achieved in modeling and planning to correct technical losses could be used to optimize generation operations and reduce investments needed for additional generation capacities. Non-technical losses occurring due to theft of services could also be detected as a result of the clean-up for the forecasting exercise.

The combination of improved market balancing costs through better forecasts with the ability detect technical losses allowed innogy Poland to gain a better sense of how it could invest in replacements to its aging infrastructure assets. In addition, the utility envisions using its improved customer data to create marketing campaigns that might optimize its planning and forecasts for greater margin profitability.


Need for more dynamic predictive modeling of customer demand for day-ahead portfolio planning and purchases.



  • Achieved predictive modeling of consumer behavior for better operational efficiency.
  • Reduced balancing costs associated with tomorrow's energy.
The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.

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