The 2030 Challenge is aimed at making all new buildings and major renovations
carbon neutral by 2030. One of the potential solutions to meet this challenge
is through innovative sustainable design strategies. For developing such
strategies it is important to understand how the various building factors
contribute to energy usage of a building, right at design time. The growth of
artificial intelligence (AI) in recent years provides an unprecedented
opportunity to advance sustainable design by learning complex relationships
between building factors from available data. However, rich training datasets
are needed for AI-based solutions to achieve good prediction accuracy.
Unfortunately, obtaining training datasets are time consuming and expensive in
many real-world applications. Motivated by these reasons, we address the
problem of accurately predicting the energy usage of new or unknown building
types, i.e., those building types that do not have any training data. We
propose a novel approach based on zero-shot learning (ZSL) to solve this
problem. Our approach uses side information from building energy modeling
experts to predict the closest building types for a given new/unknown building
type. We then obtain the predicted energy usage for the k-closest building
types using the models learned during training and combine the predicted values
using a weighted averaging function. We evaluated our approach on a dataset
containing five building types generated using BuildSimHub, a popular platform
for building energy modeling. Our approach achieved better average accuracy
than a regression model (based on XGBoost) trained on the entire dataset of
known building types.

利用零样本学习方法，本文提出了一种基于建筑能源模型的预测建筑能耗的方法，该方法使用侧面信息来预测最接近给定未知建筑类型的建筑类型，并在训练中学习建筑模型，并使用加权平均函数组合所预测的建筑能耗值，其平均精度比基于 XGBoost 的回归模型训练的知名建筑类型数据集更优。