Abstract
It is important to consider water constraints when making decisions for future energy allocations, as some promising renewable energy sources have a high demand for water and are restricted by water availability. The Energy-Water-Emissions Dashboard (EWED) project is an information exchange system of the United State Energy-Water Nexus. Our previous publication describes our approach of using machine learning models to predict future electricity generation, water consumption, and water withdrawal of different types of power plants across the United States. The performance of water consumption prediction is less desirable than that of electricity generation and water withdrawal. This paper describes a novel two-phase approach to improve the prediction of water consumption. The first phase uses Recurrent Neural Network (RNN) to predict future water consumption based on time series. The predicted result is then fed into the second phase as a new feature to produce the final water consumption prediction using Artificial Neural Network (ANN). Compared to our previous ANN prediction, Root Mean Square Error (RMSE) decreased 6.9% and Mean Absolute Error (MAE) decreased 21%. Compared with the conventional coefficient method used by EWED, RMSE decreased 53%. The performance evaluation is comprehensive with five statistical measures and is accurate with k-fold cross-validation.