A Dynamic Feedforward Control Strategy for Energy-efficient Building System Operation

TL;DR

This paper proposes a dynamic feedforward control framework that integrates building system characteristics with machine learning predictions to enhance energy efficiency in building operations, demonstrating a 15% energy saving in heating control.

Contribution

It introduces a novel control strategy that embeds dynamic building information into ML-based predictions, improving energy efficiency over traditional methods.

Findings

Achieved 15% additional energy savings in heating system control.

Demonstrated the effectiveness of integrating dynamic system info with ML predictions.

Showed potential for broader application in building energy management.

Abstract

The development of current building energy system operation has benefited from: 1. Informational support from the optimal design through simulation or first-principles models; 2. System load and energy prediction through machine learning (ML). Through the literature review, we note that in current control strategies and optimization algorithms, most of them rely on receiving information from real-time feedback or using only predictive signals based on ML data fitting. They do not fully utilize dynamic building information. In other words, embedding dynamic prior knowledge from building system characteristics simultaneously for system control draws less attention. In this context, we propose an engineer-friendly control strategy framework. The framework is integrated with a feedforward loop that embedded a dynamic building environment with leading and lagging system information involved: The simulation combined with system characteristic information is imported to the ML predictive algorithms. ML generates step-ahead information by rolling-window feed-in of simulation output to minimize the errors of its forecasting predecessor in a loop and achieve an overall optimal. We tested it in a case for heating system control with typical control strategies, which shows our framework owns a further energy-saving potential of 15%.