The objective of this project is to control water delivery and distribution at Nooksack Falls Hydroelectric Station (NFHS) in order to maximize efficiency of the system, thereby increasing energy generation. Two machine learning algorithms will be applied. (1) Q-learning - a reinforcement learning approach to obtain a set of roughly optimal configurations. (2) Recurrent Neural Network (RNN) - rough configurations gathered by the Q-learning agent will be used to train the RNN. The RNN will refine these configurations as well as enabling lifelong learning. The significance of this project is to demonstrate the practical utility of the machine learning techniques described above when applied to real-world processes such as NFHS.