Data is required to develop forecasting models for use in Model Predictive
Control (MPC) schemes in building energy systems. However, data usage incurs
costs from both its collection and exploitation. Determining cost optimal data
usage requires understanding of the forecast accuracy and resulting MPC
operational performance it enables. This study investigates the performance of
both simple and state-of-the-art machine learning prediction models for MPC in
a multi-building energy system simulation using historic building energy data.
The impact of data usage on forecast accuracy is quantified for the following
data efficiency measures: reuse of prediction models, reduction of training
data volumes, reduction of model data features, and online model training. A
simple linear multi-layer perceptron model is shown to provide equivalent
forecast accuracy to state-of-the-art models, with greater data efficiency and
generalisability. The use of more than 2 years of training data for load
prediction models provided no significant improvement in forecast accuracy.
Forecast accuracy and data efficiency were improved simultaneously by using
change-point analysis to screen training data. Reused models and those trained
with 3 months of data had on average 10% higher error than baseline, indicating
that deploying MPC systems without prior data collection may be economic.

利用历史建筑能源数据，研究了简单的线性多层感知器模型在多建筑能源系统模拟中提供与先进模型相当的预测精度，并具有更高的数据效率和泛化能力。通过使用变点分析对训练数据进行筛选，同时提高了预测精度和数据效率。重用模型和使用三个月的数据训练的模型平均误差比基准模型高出 10％，表明在没有事先数据收集的情况下部署 MPC 系统可能是经济可行的。