  Data is required to develop forecasting models for use in Model Predictive
Control (MPC) schemes in building energy systems. However, data is costly to
both collect and exploit. Determining cost optimal data usage strategies
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
multi-building energy systems using a simulated case study with historic
building energy data. The impact on forecast accuracy of measures to improve
model data efficiency are quantified, specifically for: reuse of prediction
models, reduction of training data duration, 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.
