Evaluating consumption effects of intelligent control algorithms for district heated buildings

2026-03-08

AI Summary

This study addresses the challenge of disentangling energy savings attributable to intelligent heating control from confounding factors such as weather variations and occupant behavior in existing building energy performance assessments. To this end, the authors propose a data-driven modeling approach that leverages long-term empirical measurements to construct an energy consumption model capable of isolating the effect of control interventions. The method enables, for the first time, an independent quantification and interpretable decomposition of the energy-saving contributions of specific heating control strategies. Validation using nearly a decade of real-world operational data from the Danfoss Leanheat platform demonstrates that the proposed approach effectively identifies and accurately quantifies energy impacts induced by control algorithms, thereby overcoming the limitations of conventional evaluation methods that fail to decouple intertwined influencing factors.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Search and Optimization: Heuristic Search
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

As buildings become increasingly connected and sensor-rich, intelligent remote heating control is rapidly superseding conventional local heating control. Such control algorithms often aim at reducing energy consumption by minimizing over-heating and utilizing free solar energy, for instance. Numerous companies offering heating optimization solutions have recently emerged. After installing such a system, end-users naturally want to quantify and verify the effect of such an investment, i.e., monetary return. Methods for tracking buildings'heating efficiency are diverse, ranging from simple weather normalization to more complex modeling approaches, but lack transparency and commonly agreed best practices. The problem is further complicated by the fact that buildings constantly undergo non-control-related changes that affect their energy efficiency, making it difficult to isolate and track only control-related effects using the existing methods. In this paper, we first review and derive methods for monitoring the overall efficiency of buildings, and show their inability to isolate the control effects from other changes happening in the buildings. We then propose a model-based approach for estimating and tracking only the control-related effects. Moreover, we show how the models can decompose the total control effect into sub-components to reveal where the energy effects come from. We demonstrate the methods using real data collected over approximately 10 years from the Danfoss Leanheat Building platform. Our scope focuses on district heated buildings with substation-level (supply temperature) control, but the methodology extends to other cases as well.

Problem

• intelligent control
• energy consumption
• district heating
• control effect isolation
• building energy efficiency

Innovation

• model-based evaluation
• control effect isolation
• district heating
• energy efficiency decomposition
• intelligent building control