# How well do experience curves predict technological progress? A method for making distributional forecasts

## TL;DR

This paper develops a method to assess the accuracy of experience curve forecasts for technological costs, demonstrating its effectiveness with real data and applying it to solar photovoltaic prices.

## Contribution

It introduces a novel framework for making distributional forecasts of experience curve predictions, clarifying their relation to exponential cost decline assumptions.

## Key findings

- The method performs reasonably well on a diverse dataset of 51 technologies.
- It explains why experience curves often resemble exponential cost reductions.
- Applied to solar PV, it provides a distributional forecast of future prices.

## Abstract

Experience curves are widely used to predict the cost benefits of increasing the deployment of a technology. But how good are such forecasts? Can one predict their accuracy a priori? In this paper we answer these questions by developing a method to make distributional forecasts for experience curves. We test our method using a dataset with proxies for cost and experience for 51 products and technologies and show that it works reasonably well. The framework that we develop helps clarify why the experience curve method often gives similar results to simply assuming that costs decrease exponentially. To illustrate our method we make a distributional forecast for prices of solar photovoltaic modules.