Dataset on effects of learning curve models on onshore wind and solar PV cost developments in the USA

This dataset includes input data to estimate learning-by-deploying (LbD) and learning-by-researching (LbR) rates for onshore wind and solar PV in the United States of America (USA). Using different learning curve approaches the simulated technological-based cost developments are also presented. Coefficient of determination (R squared) and Root Mean Square Error (RMSE) were applied for quantification of the agreement between simulated and observed technological-based costs.

本数据集包含用于估算美利坚合众国（USA）陆上风电与太阳能光伏（solar PV）的边部署边学习（learning-by-deploying, LbD）及边研发边学习（learning-by-researching, LbR）速率的输入数据。此外，本研究借助多种学习曲线方法，模拟并展示了基于技术演进的成本演化轨迹。本研究采用决定系数（Coefficient of determination，即R²）与均方根误差（Root Mean Square Error，RMSE），对模拟所得技术成本与实际观测技术成本之间的契合程度进行量化分析。