Challenge
1

04 - Nest, a Data-driven Building Model

(DE Zusammenfassung)

NEST Fallstudie für "Decarbonising Cities" - Kostengünstiges, daten-getriebenes Gebäudemodell Obwohl Gebäude fast die Hälfte des gesamten Schweizerischen Endenergieverbrauchs verursachen, haben die Digitalisierung und ihre Versprechen hier bisher kaum Einzug gehalten. Im NEST, dem Demonstrator der Empa, werden neue Technologien und Algorithmen entwickelt und getestet. Für diesen Zweck wird das NEST bis ins Detail sensorisch überwacht. Ziel dieser Challenge ist es, die Messdaten des NEST zu nutzen, um Gebäuden die Möglichkeit zu geben, ein wenig in die Zukunft zu blicken und dadurch einen wichtigen Schritt in Richtung Digitalisierung und Energieeffizienz zu machen.

Summary

Although buildings account for almost half of Switzerland's total final energy consumption, digitalisation in the building sector is not yet very advanced. At NEST, Empa's demonstrator, new technologies and algorithms are being developed and tested. For this purpose, NEST is monitored in detail by some 10,000 sensors. The aim of this challenge is to use the rich measurement data from the NEST to give buildings the opportunity to look a little into the future and thereby take an important step towards digitalisation and energy efficiency.

Motivation

Buildings are responsible for 60% of the energy consumption and 40% of the C02 emissions in Switzerland (overall energy statistics SFOE). Through optimal operation, i.e. without structural changes, consumption can be reduced by an estimated 20%.

However, the optimal operation of a building is a methodological challenge. Due to the thermal inertia, regulating measures (e.g. flow rate of the floor heating, charging of a storage tank) must be carried out predictively. Other influencing variables, such as solar radiation, however, have a direct influence on the indoor climate and must therefore be taken from predictions at the time of the regulating intervention.

Building models are used to solve this methodological challenge algorithmically. Building models can either be created deterministically on the basis of physical laws (model predictive control) or they can be learned from measurement data. The NEST research building of Empa and EAWAG grants access to measurement data from several buildings with different types of uses. Measurement data for all relevant systems and building functions have been stored for several years with a temporal resolution of one minute. The data situation, for example for creating data-driven building models, is unique.

Goal

The overall objective is to investigate the dependencies between the current energy input, indoor climate and weather, and the indoor climate at a later point in time. The corresponding question is: Which energy input leads to which future indoor climate for a given initial situation (current indoor climate, weather)?

Participants can choose their own approach or they can use the following, more detailed objectives and questions as a guide.

1) Visualisation of the interrelationships:

  • Which energy input leads to which indoor temperature?
  • How delayed is the reaction of the indoor temperature to a change in the energy input (i.e. how great is the thermal inertia)?
  • What influence do boundary conditions such as the outside temperature or the current indoor climate have on the subsequent indoor climate?
  • Do the above relationships vary between different rooms? Methodological note: H-scatterplots or cross-correlograms, among others, are suitable for the explorative investigation of "lagged correlations".

2) Statistical modelling of the correlations from 1):

  • Can these correlations be quantified with a statistical model?
  • Do the correlations become clearer if not only the current measurements are used as predictors, but also the last N historical measurements?

3) Sensors are expensive. In the real situation, there are usually fewer sensors available:

  • Which sensors (predictors) have which significance in the model from 2)?
  • Can the model be reduced by individual sensors to save costs without the prediction of future room temperature suffering greatly?

Data availability

The participants are provided with the data from the scientific study in an adjusted form, as CSV.

  • An overview of the measurement data and infrastructure at NEST is available at the following link: https://info.nestcollaboration.ch/wikipediapublic/.
  • The measurement data of the NEST research building can be accessed via REST API.
  • A detailed description of the sensors (incl. costs) is provided.
  • a Graphana dashboard provides visual access to all relevant measurement data.
  • Additional measurement data, such as outdoor temperature or solar radiation, can be obtained via publicly accessible interfaces.
  • Literature: Bünning, F., Huber, B., Heer, P., Aboudonia, A. and Lygeros, J., 2020. Experimental demonstration of data predictive control for energy optimisation and thermal comfort in buildings. Energy and Buildings, 211, p.109792.
  • More info: https://www.empa.ch/de/web/nest/

In principle, the above goals can be pursued for several units at NEST. However, it makes sense to start with the Urban Mining & Recycling (UMAR) unit. The measurement data from UMAR were recently used in a research project for a similar purpose (Bünning et al. 2020). Each unit is based on a specific thesis. In UMAR, this is that all resources needed to produce a building must be fully reusable, recyclable or compostable.

Title

Left: UMAR from the outside. On the left and right in the picture are the two bedrooms. Right: Floor plan of the unit. Above left and right the two bedrooms, in the middle the living room. UMAR offers living space for two guest researchers or students. The unit has a living room with kitchen, two identical bedrooms and two bathrooms. Detailed measurements are available for the consumption of hot and cold water, electrical energy and heating and cooling (details here). With this information, a statistical model can be created and trained, which can estimate the expected room temperature based on the actual state (e.g. room temperature), a measure (e.g. increase heating output) and the weather forecast for tomorrow.

Worked on documentation

11.05.2021 06:01 ~ Maud

Team forming

nikki_böhler has joined!

20.01.2021 09:12

Project started

Initialized by nikki_böhler 🎉

20.01.2021 09:12
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