Improving your energy sales forecast with end-use data
This author no longer working for DNV GL.
Why does the Energy Sales Forecast matter?
Gone are the days when economic and load growth were sufficient to reasonably buffer volatility in cost and to justify error in the energy sales forecasts. Insight into revenue drivers is an increasing imperative in the context of flat or even declining load in some sectors and is key to maintaining an accurate forecast. Add stagnant economic conditions and an increasingly contentious regulatory environment to the mix, and you will find many utility CFOs strategizing on just how to thread reliability and investments in technology and customer operations, along with a 3-5% annual labor and benefits escalation, into a 1% at best year-over-year growth needle, all the while working to ensure targeted returns on equity are achieved and an earnings per share that is in line with or exceeding Wall Street’s quarterly estimates. Forecast inaccuracy (notwithstanding cost management) will only add to the potential implications of unforeseen cash flow volatility on Rating Agencies’ financial ratio calculations. One can easily understand the need to develop more robust, repeatable and sustainable load and revenue forecasting strategies.
Can You Explain the Recent Trends in Household Usage?
Load forecasters are having difficulty explaining why certain trends in energy consumption are occurring – many are struggling with much lower (or even negative) load growth in certain key sectors than in the past. Others are seeing total energy and peak demand diverging. Why? What is causing these trends? We have been through a major economic downturn, there have been more strict codes and standards implemented in buildings and appliances, and energy efficiency initiatives are all contributing to the downward trend in energy usage. But what has been particularly difficult for energy forecasters is to estimate by how much each of these components parts are actually causing some customers to use less energy. On the flip side, now consumers have a multitude of gadgets throughout their homes and more televisions per household. Given the additional appliances along with new technologies like electric vehicles, one would assume at least the residential class would be using more electricity – but that is not the case for many regions across the U.S. Why do we need to know what is driving these trends in energy usage? Because knowing the reasons helps drive better forward-looking projections.
Does Your Energy Sales Forecast Incorporate End-Use Information?
With end-use data, the veil that cloaks the behavior of electricity demand is lifted. When this behavior is understood, predicted load under various consumption scenarios can be leveraged and an optimal forecast obtained.
Your load forecaster can apply end-use data to isolate and measure weather-driven changes in load, separated from other non-weather-sensitive equipment categories among other significant distinctions. Specifically, there are several important benefits of end-use data in the energy sales forecast including:
- Understanding changes in load growth over time – Forecasting your end uses individually is difficult even with significant historical end-use data. However, forecasting your total (composite) load is more accurate when changes in end use are identified and accounted for over time.
- Incorporating accurate energy efficiency implications and trends in consumption. These trends can be used to assess the impacts of changes in customer load resulting from:
- Efficiency: How does downstream changes in standards (e.g. continued improvements in the efficiency of HVAC equipment)
- Participation: Increases in future program participation (e.g. increasing weatherization incentives result in accelerated participation)
- Saturation: Account for different adoption rates among residential, commercial and industrial building types
- Technology: change-outs (e.g. replacement of conventional water heating by heat-pump water heaters)
- Building stock: How does new construction (+) or an economic downturn (-) change your top line energy sales?
- Better understanding your peak – End-use level data will help explain why and when your peak occurs and its major drivers.
- Choosing to track load at a finer level of detail, rather than an obscure system level. Leverage information across an entire customer or rate class across micro-climates, market segments and pinpoint the geography that makes up the transmission and distribution system.
- Giving your forecasts the flexibility to identify, account and adjust for for changes in competing fuels (e.g. natural gas vs. electricity for heating).
How can DNV GL Help You Develop End-Use Data?
There are many ways to collect and develop data, from statistical disaggregation and modeling approaches to integrated sampling using conventional or advanced metering techniques. Each approach has its own set of pros and cons. Given that end-use data provides many valuable benefits to the utility business enterprise (and not just forecasting), we believe that the most effective strategy is to match the approach with your overall objectives, linking several strategies in an integrated framework.
DNV GL can help assess the whole enterprise by providing subject matter experts for all of the key business areas that use end-use information including energy efficiency program design and evaluation, customer analytics and market research, load research, and distribution planning.
Once your information needs are identified, our team of statisticians, engineers, market researchers, and data scientists have the experience and expertise to help you develop meaningful end-use data. The strategies can include quick fixes to the implementation of long-term (multi-year) end-use metering studies of the residential, multi-family and commercial business sectors.
DNV GL is an exhibitor at Utility Analytics Week in New Orleans October 28-30, so please stop by our booth to discuss this blog or how we can help you develop and utilize end-use data in your business. Visit our website to learn more.
Jonathan Farland, senior analyst in DNV GL’s Policy, Advisory, and Research group, also contributed significantly to this blog.