An ANN-based fast building energy consumption prediction method for complex architectural form at the early design stage
early design stage, energy consumption prediction, artificial neural network, complex building form
How to control the growth of building energy consumption and achieve the goal of energy saving and emission reduction while ensuring people’s growing demand for indoor comfort is of great practical significance in the new era. The rapid and accurate prediction of the building energy consumption at the early design stage can provide a quantitative basis for the energy-saving design. ANN (artificial neural network) model is the most widely used artificial intelligence model in the field of building performance optimization due to its high speed, high accuracy, and capability of handling nonlinear relationships between variables. In this paper, an ANN-based fast building energy consumption prediction method for complex architectural form for the early design stage was proposed. Under this method, the authors proposed an idea of architectural form decomposition, to eliminate the complexity of building shape at the early design stage, thus transforming the energy consumption prediction problem of one complex architectural form into several energy consumption prediction problems of multiple simple blocks: the method of characterization decomposition (MCD) and the method of spatial homogenization decomposition (MSHD). The ANN model was introduced to realize energy consumption prediction, which fully utilized the two advantages: high speed and good response to complicated relationships. Accuracy verification shows that the relative deviation of cooling and heating energy consumption is within ±10% using the MCD method. The relative deviation of total energy consumption is within 10% using the MSHD method.
Tsinghua University Press
Ziwei Li, Jian Dai, Hongzhong Chen et al. An ANN-based fast building energy consumption prediction method for complex architectural form at the early design stage. Build Simul, 2019, 12(4): 665–681.