Krzysztof Arendt

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.

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.

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.

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.

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).