In this last forecasting brown bag presentation on solar load forecasting, we asked participants who had developed a long-term solar load forecast before 2013 and after 2015. As expected, very few had done a forecast before 2013 and majority put together something after 2015. During the last Vermont state forecast we did in 2014, solar wasn’t even a major topic until, of course, the month before the forecast was due. But what is a reasonable approach?

We started by collecting monthly data on installed systems and number of customers for each state starting in 2010. Then we compared saturation rates – what we found is that those states with the highest return on investment had the highest level of saturation. People make rational economic decisions after all! Well, at least some people do. Armed with this information, we estimated a regression model for Vermont that relates system saturation to system economics using a simple payback to capture system economics. And guess what? It worked. We were pleasantly surprised; when we used a cubic specification the model fit was awesome. We have used this model in several service areas – some with high saturation-levels (Nevada) and some with very low saturation (Indiana) and it seems to work, most of the time. This model approach was laid out in the brown bag presentation.

If you google “Forecasting New Technologies” you will find dozens of approaches. Most of these entail fitting an S-shaped curve to your own or like technology data set. If you have tried a Bass Diffusion model or Fisher-Pry logistic curve fit model or something else, we would love to hear about it. We all need to forecast solar generation – let’s share approaches!

Eric Fox
Director Forecasting - Itron
Eric Fox is Director, Forecasting Solutions with Itron where he directs electric and gas analytics and forecasting projects, provides regulatory support, and manages Itron’s Boston office. Fox has over 30 years of forecasting experience with expertise in financial forecasting and analysis, long-term energy and demand forecasting, and load research. Eric has a strong foundation in forecast model development, technology assessment, and statistics. Eric started his forecasting career supporting and implementing the EPRI residential and commercial end-use forecast models. He helped developed the Statistically Adjusted End-Use (SAE) modeling framework that leverages off of the Energy Information Administration National Energy Modeling System and Annual Energy Outlook. Eric provides forecast training through utility workshops and webinars and has assisted numerous utilities and system operators with implementing SAE models for long-term energy and demand forecasting. Eric has a B.A. and M.A. in Economics from San Diego State University.