Bagging Neural Networks for Predicting Water Consumption

Neural network ensembles can improve the generalization and stabilization ability of learning systems effectively through aggregating numbers of neural networks. It is considered as a promising kind of engineering neural computing technology. In this paper, we construct a learning model based on bagged neural network ensembles by bagging BP（Back Propagation） networks. Through analyzing the historical data of civic water consumption, the model can predict water consumptions efficiently for various kinds of users and several of periods of time. Our experiments and practical applications show that the model has high predicting precision, good stabilization as well as feasibility.

Bagging Neural Networks for Predicting Water Consumption