Forecasting
Who is Forecasting Long-Term Solar Generation?
July 20, 2016
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!
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!