Impact of data for forecasting on performance of model predictive control in buildings with smart energy storage

Abstract (excerpt)

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 m

Excerpt shown for reference under fair use — read the full paper at the publisher.

Related failures