Sustainable energy systems require deployment of new technologies to help tackle the challenges of climate change and ensuring energy supplies. Future sources of energy are less economically competitive than conventional technologies, but there is the potential for cost reduction. Tools for modelling technological change are important for assessing the deployment potential of early-stage technologies. Learning curves are a tool for assessing and forecasting cost reduction of a product achieved through experience from cumulative production. They are often used to assess technological improvements, but have a number of limitations for emerging energy technologies. Learning curves are aggregate in nature, representing overall cost reduction gained from learning-by-doing. However, they do not identify the actual factors behind the cost reduction. Using the case study of onshore wind energy, this PhD study focuses on combining learning curves with engineering assessment methods for improved methods of assessing and managing technical change for emerging energy technologies. A third approach, parametric modelling, provides a potential means to integrate the two methods.