NEST – una plataforma para acelerar la innovación en edificios

Richner, Peter; Heer, Philipp; Largo, R. H.; Marchesi, E.; Zimmermann, M.

doi:10.3989/id.55380

Cited by 25 publications

(17 citation statements)

References 3 publications

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“…The NEST building at Empa in Switzerland (Richner et al, 2018), shown in Figure 2, is used as a case study. The building contains multiple individual units with different uses that can be added and removed from the building, in addition to office and meeting rooms that are permanently installed.…”

Section: System Descriptionmentioning

confidence: 99%

Improved day ahead heating demand forecasting by online correction methods

Bünning

Heer

Smith

et al. 2020

Energy and Buildings

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To reduce the heating and cooling energy demand of buildings and districts novel control strategies are constantly being developed that require information on the future demand of the controlled entity. Demand forecasting is commonly done with deterministic white box models or fitted grey-box models, however, recently more and more data and machine learning based approaches are being developed. All approaches have weaknesses: white-box models require major modelling effort, grey-box approaches are limited by their model or parameter complexity and machine learning is dependent on hyperparameters, some of which are randomly chosen, and therefore considered unreliable. Here we develop a forecasting approach based on Artificial Neural Networks (ANN) and introduce error correction methods based on online learning and the learned autocorrelation of the forecasting error. We compare the approach to other regression based and grey-box methods in a real case study of a small-scale district energy system with mixed use and unknown lower-level control. We show that the proposed method outperforms the other forecasting methods in terms of average error and coefficient of determination. We further demonstrate that in our case study the error correction methods significantly reduce variance in ANN performance created by randomly initialized parameters in the networks.

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Section: System Descriptionmentioning

confidence: 99%

Improved day ahead heating demand forecasting by online correction methods

Bünning

Heer

Smith

et al. 2020

Energy and Buildings

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“…The controlled apartment is a residential unit in the NEST demonstrator building at Empa in Switzerland (Richner et al, 2018) and is marked in Figure 1. A schematic of the layout is shown in Figure 2.…”

Section: General Descriptionmentioning

confidence: 99%

Experimental demonstration of data predictive control for energy optimization and thermal comfort in buildings

Bünning

Huber

Heer

et al. 2020

Energy and Buildings

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Model Predictive Control for room temperature control in buildings is an effective approach to energy management in buildings. However, the development and maintenance of physical models may be a bottleneck for widespread real life application. Data Predictive Control is an attempt to address this problem by learning the behaviour of the building from historical data and thus reducing the modelling effort. Here, we present an application of a Data Predictive Control approach, based on Random Forests with affine functions and convex optimization, to control the room temperature in a real life apartment. When compared to a conventional hysteresis controller, the applied approach saves 24.9 % of cooling energy while reducing the integral of comfort constraint violations by 72.0 % in a six-day experiment.

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“…The historical data set used is based on an experiment performed at the NEST building [22], in Dübendorf, Switzerland, between 16 th and 23 th November 2020. Considered flexible components include a PV installation of 7.3 kWp, an EV of 7 kW/50 kWh, a home battery of 5 kW/17.5 kWh, space heating demand of 4 kW, and domestic hot water heating demand of 5.7 kW.…”

Section: Case Studymentioning

confidence: 99%

Data-Driven Demand-Side Flexibility Quantification: Prediction and Approximation of Flexibility Envelopes

Hekmat¹,

Zong²,

Zufferey³

et al. 2021

Preprint

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Real-time quantification of residential building energy flexibility is needed to enable a cost-efficient operation of active distribution grids. A promising means is to use the socalled flexibility envelope concept to represent the time-dependent and inter-temporally coupled flexibility potential. However, existing optimization-based quantification entails high computational burdens limiting flexibility utilization in real-time applications, and a more computationally efficient quantification approach is desired. Additionally, the communication of a flexibility envelope to system operators in its original form is data-intensive. In order to address the computational burdens, this paper first trains several machine learning models based on historical quantification results for online use. Subsequently, probability distribution functions are proposed to approximate the flexibility envelopes with significantly fewer parameters, which can be communicated to system operators instead of the original flexibility envelope. The results show that the most promising prediction and approximation approaches allow for a minimum reduction of the computational burden by a factor of 9 and of the communication load by a factor of 6.6, respectively.

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NEST – una plataforma para acelerar la innovación en edificios

Cited by 25 publications

References 3 publications

Improved day ahead heating demand forecasting by online correction methods

Improved day ahead heating demand forecasting by online correction methods

Experimental demonstration of data predictive control for energy optimization and thermal comfort in buildings

Data-Driven Demand-Side Flexibility Quantification: Prediction and Approximation of Flexibility Envelopes

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