Author
Krzysztof Arendt
Other affiliations: Maersk, Gdańsk University of Technology
Bio: Krzysztof Arendt is an academic researcher from University of Southern Denmark. The author has contributed to research in topics: Model predictive control & Energy consumption. The author has an hindex of 10, co-authored 28 publications receiving 352 citations. Previous affiliations of Krzysztof Arendt include Maersk & Gdańsk University of Technology.
Papers
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TL;DR: This paper provides a unified framework for model predictive building control technology with focus on the real-world applications and presents the essential components of a practical implementation of MPC such as different control architectures and nuances of communication infrastructures within supervisory control and data acquisition (SCADA) systems.
276 citations
TL;DR: An online building energy performance monitoring and evaluation tool ObepME is proposed, serving as a basis for fault detection and diagnostics and forming a backbone for continuous commissioning, to better characterize, evaluate and bridge energy performance gaps.
62 citations
TL;DR: It is found that a difference of up to 20% in cooling cost for the cases studied can occur between the best performing model and the worst performing model, and the primary factors attributing to this were model structure and initial parameter guesses during parameter estimation of the model.
61 citations
TL;DR: In this article, the authors compared the accuracy and computational demand of two occupancy estimation and prediction approaches suitable for building model predictive control: (1) count prediction based on indoor climate modeling and parameter estimation using common sensors, (2) counting based on data from 3D stereovision camera.
Abstract: Model predictive control is a promising approach to optimize the operation of building systems and provide demand-response functionalities without compromising indoor comfort. The performance of model predictive control relies, among other things, on the quality of weather forecasts and building occupancy predictions. The present study compares the accuracy and computational demand of two occupancy estimation and prediction approaches suitable for building model predictive control: (1) count prediction based on indoor climate modeling and parameter estimation “using common sensors”, (2) count prediction based on data from 3D stereovision camera. The performance of the two approaches was tested in two rooms of a case study building. The results show that the method with dedicated sensors outperforms common sensors. However, if a building is not equipped with dedicated sensors, the present study shows that the common sensor method can be a satisfactory alternative to be used in model predictive control.
48 citations
TL;DR: In this paper, the authors investigated the influence of the cavity concentration in hollow bricks on static and dynamic thermal parameters: a time lag, a decrement factor, an equivalent thermal diffusivity (ETD), and equivalent thermal conductivities (ETC).
39 citations
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TL;DR: This paper provides a unified framework for model predictive building control technology with focus on the real-world applications and presents the essential components of a practical implementation of MPC such as different control architectures and nuances of communication infrastructures within supervisory control and data acquisition (SCADA) systems.
276 citations
TL;DR: Reinforcement Learning (RL), as an emerging control technique, has attracted growing research interest and demonstrated its potential to enhance building performance while addressing some limitations of other advanced control techniques, such as model predictive control.
233 citations
TL;DR: In this article, the authors reviewed the published researches on post-occupancy performance of green buildings in terms of energy use, indoor environment quality (IEQ) and occupant satisfaction.
178 citations
TL;DR: This study systematically surveyed how machine learning has been applied at different stages of building life cycle and can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings.
160 citations
TL;DR: It was found XGBoost and LSTM provided the most accurate load prediction in the shallow and deep learning category, and both outperformed the best baseline model, which uses the previous day’s data for prediction.
157 citations