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Analytics Magazine

Utilities dust off the forecasting playbook

July/August 2013

Smart grid data brings challenges and opportunities for power industry.

Tao Hong (left) and Alyssa FarrellBy Tao Hong (left) and Alyssa Farrell

The age-old business of forecasting is once again a hot topic of conversation at utilities. As the business needs shift to a more proactive management style, analytics that give insight into the future – whether customer adoption of electric vehicles (EVs) over the next five years or tomorrow’s wind power generation – are in demand.

For load forecasters specifically, the scrutiny is intensifying. Previously, utilities didn’t get many questions about the accuracy of their load forecasts during the regulatory rate case approval process. But now, new rate cases are harder and harder to approve. In this environment, utilities need more defensible forecasts to secure regulatory approval.

Under pressure to demonstrate a return on smart grid investments, utilities are using the data they collect from smart meters and other smart grid devices to better understand customers, design demand response (DR) programs, make buying and selling decisions on the energy market, and increase the reliability of the grid. Forecasting plays a key role in each of these areas, from modeling future load growth to predicting the impact of DR.

Forecasting is also becoming more critical to the operations of a utility because of the increasing penetration of distributed energy resources, EVs and energy-efficient appliances. Previously, when forecasting electricity demand, utilities didn’t have to worry about electric vehicles or solar panels on rooftops or wind farms because these technologies were not present in significant enough numbers to have any real effect. Now, however, they’re increasing in prevalence and therefore increasing the challenge of accurately forecasting electricity demand.

Figure 1: Ten years of hourly electric load of a U.S. utility at the corporate level. As millions of smart meters are being installed, utilities will see more and more hourly or even sub-hourly load series at the household level. The data brings both challenges and opportunities to the utility industry.
Figure 1: Ten years of hourly electric load of a U.S. utility at the corporate level. As millions of smart meters are being installed, utilities will see more and more hourly or even sub-hourly load series at the household level. The data brings both challenges and opportunities to the utility industry.

Advanced Metering Infrastructure (AMI) is the primary technology that offers forecasters more timely and granular data for load analysis and forecasting. With AMI, the utility has two-way communication with the meter (electricity, water or gas), and it gets readings back in an automated fashion in real time, which means that all the data about energy consumption, down to the meter level, can be more granular than ever before.

Increased use of solar panels makes forecasting electricity demand more difficult.
Increased use of solar panels makes forecasting electricity demand more difficult.

An Expanding Role for Utility Forecasters

For the vast majority of the electricity grid, energy consumption is mainly driven by weather, human activities and the interactions among those variables. In the past, if utilities could predict temperature and properly model seasonal behaviors, they would arrive at a pretty decent forecast. Now, utilities with renewable generation resources may need to forecast cloud cover or wind speed.

For example, as cloud cover increases, solar photovoltaic output goes down. This means the net demand on the remaining system will increase under the same loading condition. The opposite is true for wind. As wind speed increases in a region, the output from wind farms increases and net demand on the system is reduced. Unfortunately, making predictions about cloud cover and wind speed and direction is significantly more challenging than predicting temperature. The high volatility of wind and solar makes today’s load forecasting much more complicated than before.

In addition, EV charging is quite difficult to model. If EV owners regularly charge their batteries in the evening hours then that would be a predictable load to forecast, but human behavior is erratic. We come home early some days, stay late, go out for dinner, work from home, etc. The volatility in demand that is introduced by these new technologies is putting new pressures on utility forecasters.

Smart grid infographic takes into consideration emerging energy sources.
Smart grid infographic takes into consideration emerging energy sources.

Bridging the Cultural and Technical Divide

As a fundamental problem in the utility industry, forecasting finds its applications across several departments of a utility, such as planning, operations, marketing and customer services. Many utility forecasting teams are siloed, sitting in different departments. Some utilities have an analytics center of excellence that serves multiple business needs. When they are centralized, these resources communicate better with each other and build collaborative forecasts that tend to have higher overall accuracy. If they are siloed, the consistency and quality of the data is sometimes sporadic. Siloed forecasting teams may use different data, customized tools and have access to less computing power than if they were centralized.

Just like organizational differences, the business pressures faced by each utility are also unique. Large utilities tend to feel the pain of renewable and distributed energy resources more than smaller utilities. For example, several utilities in California provide power to urban areas that have high penetrations of renewable energy. On the other hand, municipals and co-ops also care about improving their forecasting processes because many of them deployed smart meters even before the larger investor-owned utilities.

Because municipal utilities and cooperatives are city- or member-owned, they have the incentive to understand their customers better so that they can more accurately contract the right amount of power to meet demand. When they do this well, they can pass on the savings directly to their customers. Old Dominion Electric Cooperative (ODEC) credits advanced forecasting capabilities with enabling four rate decreases in just one year [1].

Figure 2: One week of solar generation (kW) at five-minute intervals. There is no solar generation at night. During the daytime, solar generation can be very volatile and difficult to predict. The utility industry needs advanced forecasting and optimization techniques to operate the power grid under reliability, economic and environmental constraints.
Figure 2: One week of solar generation (kW) at five-minute intervals. There is no solar generation at night. During the daytime, solar generation can be very volatile and difficult to predict. The utility industry needs advanced forecasting and optimization techniques to operate the power grid under reliability, economic and environmental constraints.

Utility Forecasting Keys to Success

Because forecasts are having an increasingly significant impact on business decisions, it is important to highlight several keys to success. One of the authors (Tao Hong) discussed three skills of the ideal energy forecaster in his blog [2]. First, forecasters need to maintain a close relationship with the business. The forecast provides no value unless people on the business side know how to use it. In addition, forecasters need broad analytical skills to understand basic statistics, and they need technical skills to master the tool set available to them. Finally, but most importantly, forecasters need to be honest and true to their forecasting methodology and not allow themselves to be swayed by internal politics. Forecasting results should be data-driven, not tweaked to meet some personal agenda.

To improve energy forecasting in the future, each utility needs centralized forecasting teams that provide analytical services for most of the business units across the utility. A team consists of people with diverse backgrounds, including electrical engineers, economists, statisticians, meteorologists, social scientists, operations research specialists, information management specialists, software programmers and business liaisons. Ideally, these people with their diverse skill sets all have access to quality data and are not technology constrained so they can perform complex calculations across many models in a very short time frame. They have rigorous, traceable forecasts, and comprehensive documentation. By working closely with the business side, the liaisons within the forecasting team help them improve data-driven decision-making.

Tao Hong is the head of Energy Forecasting at SAS, where he oversees research and development, consulting, education, marketing and sales of SAS solutions for energy forecasting. He is the author of the blog Energy Forecasting (http://blog.drhongtao.com). He is also the chair of IEEE Working Group on Energy Forecasting, general chair of the Global Energy Forecasting Competition and an adjunct instructor at the Institute of Advanced Analytics at North Carolina State University.

Alyssa Farrell leads global industry marketing for SAS’ business within the energy sector, including utilities, oil and gas. She also has responsibility for SAS’ Sustainability Solutions and works with customers around the world to understand best practices and solutions for managing their business with environmental responsibility in mind. She participates in the Green Tech Council of the North Carolina Technology Association.

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