Using BP nerual networks for the simulation of energy consumption

Energy efficiency and sustainable development have been the focus of the world´s attention. In order to promote the execution of energy reduction, energy control systems, which could operate the electrical appliances, are under research at present. Before putting the energy control systems into real buildings, comfort assessment and energy consumption analysis need to be conducted but such operations require a large number of test cases to ensure the stability and effectiveness of the systems. Nevertheless, real data collection from each building is tedious and expensive; and manual test data generation may drop some important effective factors or relationships. Therefore, a tool of test data generation, which could generate large volumes of test data, is desperately needed. In this paper, we propose a neural network model to generate a large test data set for comfort assessment and energy consumption analysis. This approach is based on an existing set of real-world data, and generalizes it into a larger data set. Our analysis indicates that the proposed approach is reliable and effective.