neural network

The purpose of this paper is to find the efficiency of window shades regarding building energy performance and explore the possibility of developing a model that enables users to find proper shades for their specific conditions. The paper investigates different options of shades and their related variables and finds the efficiency of the shades regarding energy load. Each variable was investigated for its effect on the heat loads. Results were used as input variables for neural network prediction model.  A prediction model was developed and trained based on the previous simulation results.

In many parts of Asia as typified by Japan, conditioning of the indoor thermal and air environments using natural ventilation since ancient times. When indoor thermal and air environments are predicted, the use of simulation technologies such as CFD and Heating and Ventilation Network Model has increased. Those have advantages and disadvantages. In addition, AI programs like Neural Network (NN) and Genetic Algorithm (GA) are increasingly utilized in other research areas. In architectural equipment field, there are examples of airconditioning system models with NN.

Due to a variety of influence factors, outside dry-bulb temperature takes on a systematic and randomfluctuation. If a deterministic model is used to forecast the dry-bulb temperature, the predicted resultoften has a rough accuracy. Neural network can learn the internal regularity of the sample data bysample training; therefore it has very much adaptability and advantage in the aspects of forecast.The influence factors of outside dry-bulb temperature exist difference in the daytime and the nighttime,which makes the fluctuant regularity of outside dry-bulb temperature inconsistent.