An energy benchmarking model based on artificial neural network method with a case example for tropical climates

Energy benchmarking is an important step in evaluating a building's energy use and comparing it with similar buildings in similar climates. Depending on the benchmarking results, extra measures can be taken to reduce energy consumption when the subject building has been assessed to consume more than other similar buildings. This study presents the current state of energy benchmarking-related research and available tools. An artificial neural networks (ANN)-based benchmarking technique is presented as a highly effective method. The model specifically focuses on predicting a weighted energy use index (EUI) by taking into consideration various building variables, such as plug load density, lighting type and hours of operation, air conditioning equipment type and efficiency, etc. Data collected from laboratory, office and classroom-type buildings and mixed use buildings in Hawaii are used to present the ANN-based benchmarking technique. The developed model successfully predicted the benchmarking EUI for the buildings considered in the study. The model coefficient of correlation was 0.86 for the whole building benchmarking analysis, indicating a good correlation between the measured EUI and the ANN predictions. Additionally, the use of ANN benchmark model for predicting potential energy savings from retrofit projects was evaluated. Some of the benchmarking input variables were modified to reflect a potential energy savings from a retrofit project and the new input set was simulated with the ANN model. The preliminary results show that the developed ANN model can be used to predict energy savings from retrofit projects. Copyright (c) 2006 John Wiley & Sons, Ltd.