A simple neural network for predicting operable window status

Abstract

A simple artificial neural network (ANN) is globally developed and broadly adopted in the building engineering field from many perspectives. It is a powerful tool to help engineer or predict future statements. There are many methods to introduce natural ventilation interior, and the simplest way is to conduct the airflow through windows. The general passive ventilation is practically in Spring and Autumn. This study aims to forecast the trends of indoor temperature and schedule the operation status of operable windows by using time-series differential data set. The building simulation has conducted during the transition periods to create training and testing data. A single-layer artificial neural network has been developed and performs training using the Levenberg-Marquardt algorithm. Additionally, the simulations have investigated in different seasons and places to validate the ANN model and find the best ANN model. From the result, the best trained ANN model is the training data that created covers the spring and autumn seasons with one hidden layer and 25 nodes. The training performances present in terms of MSE and R-values are 0.0507 and 88.25%, respectively. Finally, the best ANN model that has been built from a location is applicable and adapted to another location efficiently.