Technology modeling is a vital part of developing and understanding energy system scenarios and policy, but it is challenging due to data limitations, deep uncertainty, and the complex social and technological dynamics involved in the evolution of energy systems. These difficulties are often compounded by unsound technology forecasting practice, including overfitting, data selection bias, and ad hoc assumptions, leading to unreliable conclusions. We flag several cases where this has been problematic and analyze in detail a recent model for predicting the pace of solar photovoltaic and wind energy deployment. We discuss general takeaways and provide suggestions for how statistical testing should be conducted to avoid such problems in the future and to quantify the reliability of forecasts.

中文翻译：

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数据驱动的能源技术建模需要更好的统计测试


技术建模是开发和理解能源系统情景和政策的重要组成部分，但由于数据限制、深度不确定性以及能源系统演化中涉及的复杂社会和技术动态，它具有挑战性。这些困难往往因不健全的技术预测实践而变得更加复杂，包括过度拟合、数据选择偏差和临时假设，导致得出不可靠的结论。我们标记了几个存在问题的案例，并详细分析了最近用于预测太阳能光伏和风能部署速度的模型。我们讨论一般要点，并就如何进行统计测试以避免未来出现此类问题并量化预测的可靠性提供建议。