Evaluating consumption effects of intelligent control algorithms for district heated buildings

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.

Figures (17)

• Figure 1: Energy flows in and out from a building: $q_{\mathrm{hvac}}$ denotes the heat flowing in from the heating system, $q_{\mathrm{rad}}$ is the contribution of the solar radiation, $q_{\mathrm{int}}$ are the internal heat gains from, e.g., people and appliances, and $q_{\mathrm{out}}$
• Figure 2: Daily average power consumption vs. outside temperature, color indicates the solar radiation intensity.
• Figure 3: Daily average power consumption vs. outside temperature, color indicates the solar radiation intensity. Lines indicate model predictions with different solar radiation intensities. In the model predictions, indoor temperature was fixed to its mean value.
• Figure 4: Illustration of two "control regimes" in fixed outdoor conditions. If supply temperature is lower than a (outdoor condition specific) critical value, we are in the "supply temperature control regime" where the supply temperature directly dictates the heating power and indoor comfort. In contrast, the right side of the critical value is the "thermostat regime" where thermostats regulate the flow and dependency on supply temperature is weak.
• Figure 5: Total heating power vs. supply temperature (daily averages), color indicates time.