Fitting equation		None Exponential Linear
Fit data between			–
Plot data between			–
Fit	CP = exp(-924.0 + 0.4617∙YR)

Fitting equation		None Learning curve Linear
Fit data between			–		GWp
Plot data between			–		GWp
Fit	ASP = 6.235∙CP-0.3245
Learning rate		79.86%

Include	Source
	Verlinden [Ver14]
	Fraunhofer ISE [Fra13]

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This calculator evaluates the expansion of the photovoltaic (PV) industry in terms of the cumulative production and price of PV modules. It can be used to determine the 'learning rate' of the PV industry and to predict module prices in the future.

Module prices are adjusted for inflation using a yearly average index derived from the US inflation calculator. Exchange rates are a yearly average rate derived from FXTop.

A learning curve should, in theory, be derived from the cost of manufacture. Due to a lack of data on manufacturing costs, here it is defined in terms of an average sales price (ASP) by the equation ASP = a∙CPᵇ, where CP is the cumulative production. The learning rate is then calculated as R = exp[b∙ln(2)]. Read more about the learning curve here.

[Ver14]	P. Verlinden, Seminar at UNSW, "Cost analysis of current PV production and strategy for future silicon PV modules," 22-Jan-2014, Slide 10. Data compiled from Paul Maycock's PV News, Photon International, Robert Johnson, Paula Mints, Navigant Consulting, Bloomberg New Energy Finance. Yearly information is estimated from a comparison to [Fra13].
[Fra13]	Fraunhofer ISE, Photovoltaics Report, 7-Nov-2013, Slide 40. Data compiled by Navigant Consulting and representative of all bulk PV technologies.

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