As the electric grid undergoes the transition to a carbon free future, many new techniques for optimizing the grid’s energy usage and carbon footprint are being designed. A common technique used by many approaches is to reduce the energy usage of the grid’s peak demand periods since doing so is beneficial for reducing the carbon usage of the grid. Consequently, the design of peak forecasting methods that predict when and how much peak demand will be seen is at the heart of many energy optimization approaches. In this paper, we present PeakTK, an open-source toolkit and reference datasets for peak forecasting in energy systems. PeakTK implements a range of peak forecasting methods that have been proposed recently and exposes them through well-defined interfaces and library modules. Our goal is to improve reproducibility of energy systems research by providing a common framework for evaluating and comparing new peak forecasting algorithms. Further, PeakTK provides libraries to enable researchers and practitioners to easily incorporate peak forecasting methods into their research when implementing higher level grid optimizations. We discuss the design and implementation of PeakTK and present case studies to demonstrate how PeakTK can be used for forecasting or quantitative comparisons of energy optimization methods.