With the continued increase in the amount of renewable generation in the formof distributed energy resources, reliability planning has progressively become a more challenging task for the modern power system. This is because with higher penetration of renewable generation, the system has to bear a higher degree of variability and uncertainty. One way to address this problem is by generating realistic scenarios that complement and supplement actual system conditions. This thesis presents a methodology to create such correlated synthetic scenarios for load and renewable generation using machine learning. Machine learning algorithms need to have ample amounts of data available to them for training purposes. However, real-world datasets are often skewed in the distribution of the different events in the sample space. Data augmentation and scenario generation techniques are often utilized to complement the datasets with additional samples or by filling in missing data points. Datasets pertaining to the electric power system are especially prone to having very few samples for certain events, such as abnormal operating conditions, as they are not very common in an actual power system. A recurrent generative adversarial network (GAN) model is presented in this thesis to generate solar and load scenarios in a correlated manner using an actual dataset obtained from a power utility located in the U.S. Southwest. The generated solar and load profiles are verified both statistically and by implementation on a simulated test system, and the performance of correlated scenario generation vs. uncorrelated scenario generation is investigated. Given the interconnected relationships between the variables of the dataset, it is observed that correlated scenario generation results in more realistic synthetic scenarios, particularly for abnormal system conditions. When combined with actual but scarce abnormal conditions, the augmented dataset of system conditions provides a better platform for performing contingency studies for a more thorough reliability planning. The proposed scenario generation method is scalable and can be modified to work with different time-series datasets. Moreover, when the model is trained in a conditional manner, it can be used to synthesise any number of scenarios for the different events present in a given dataset. In summary, this thesis explores scenario generation using a recurrent conditional GAN and investigates the benefits of correlated generation compared to uncorrelated synthesis of profiles for the reliability planning problem of power systems.