ANFIS for building cooling load estimation

Using the new technology and technique to improve and optimize the building performance from the conceptual design phase has a significant meaning. The introduction of Artificial Intelligent algorithms together with the advancement in computing capability recently open the doors to new horizon. In this paper, the application of Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the cooling load of building from early design is investigated. The building cooling load estimation for most building is complicated and time consuming due different design options, location, weather data...And the outputs are also many like cooling load, heating load, etc… Many commercial, complex software packages for heating load and cooling load of specific building. ANFIS can help to predict the energy consumption based its learning capability of large data from building in general and heating and cooling loads in particular. The building energy dataset now can be generated using computational BIM model using Dynamo for Autodesk Revit by Autodesk Inc., Grasshopper for Rhinoceros, McNeel corporations. In this paper, the dataset is generated by Autodesk Ecotect and is provided freely by UCI dataset repository to download. By ANFIS the energy consumption of residential building is predicted precisely.