Evaluating consumption effects of intelligent control algorithms for district heated buildings

TL;DR

This paper reviews existing methods for monitoring building heating efficiency and introduces a model-based approach to isolate and analyze the effects of intelligent control algorithms on energy consumption in district heated buildings.

Contribution

It proposes a novel model-based method to accurately estimate control-related energy savings and decomposes these effects into sub-components for better insight.

Findings

Existing methods struggle to isolate control effects from other building changes.

The proposed model-based approach effectively tracks control-related energy savings.

Demonstrated on 10 years of real data from the Danfoss Leanheat platform.

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.