Resumen
The energy performance of residential and commercial buildings is a vital topic because of the rapid urbanization occurring throughout the world. Accommodation buildings are considered energy intensive compared to other commercial facilities. In addition, they are the main component of the tourism industry. Therefore, various actions and policies have been introduced to improve the energy performance of accommodation buildings. This research depends on a national scale database of energy consumption of commercial buildings in Japan. It is the main source of data to conduct this study. The Database for Energy Consumption of Commercial buildings (DECC) is a national survey that is provided by the Japan Sustainable Building Consortium (JSBC). Based on the DECC, the study presents a general benchmark which is developed by applying regression and artificial neural network (ANN) methods to assess the energy performance of accommodation buildings in the Kanto region, Japan. The study presents a broad benchmark to evaluate basic energy consumption of accommodation building with three variables. In addition, the study highlights the necessity of designing the ANN model through the choosing of hidden layers and training method. The outcomes of each learning method and hidden layer was examined using main indicators to verify its accuracy.