No abstract

No abstract

Buildings represent a significant portion of global energy consumption. Ventilation units are complex components, often customized for the specific building, responsible for a large part of energy consumption. Their faults impact buildings’ energy efficiency and occupancy comfort. In order to ensure their correct operation, proper fault detection and diagnostics methods must be applied. Hardware redundancy, an effective approach to detect faults, leads to increased costs and space requirements. We propose exploiting physical relations inside ventilation units to create virtual sensors from other sensors’ readings, introducing redundancy in the system. We use two different measures to detect when a virtual sensor deviates from the physical one: coefficient of determination for linear models, and acceptable range. We tested our method on a real building at the University of Southern Denmark, developing three virtual sensors: temperature, airflow, and fan speed. We employed linear regression models, statistical models, and non-linear regression models. All models detected an anomalous strong oscillation in the temperature sensors. Readings fell outside the acceptable range and the coefficient of determination dropped. Our method showed promising results by introducing redundancy in the system, which can benefit several applications, such as fault detection and diagnostics and fault-tolerant control. Future work will be necessary to discover thresholds and set up automatic fault detection and diagnostics.

No abstract

SUMMARY The main objective of this study is to present an integrated thermoelectric–photovoltaic renewable system to dehumidify air and produce fresh water. The system is combined with a solar distiller humidifying ambient air to enhance distillate output to meet the specified fresh water needs for a residential application. A model is developed to simulate the air dehumidification process using thermoelectrically cooled TEC channels. Experiments were performed to validate the developed model results. It is found that the model predicted well the variation in the air temperature along the channel with a maximum relative error in air temperature less than 2.4%. In addition, the simulation model predicted well the amount of water condensate produced by the integrated system with a maximum relative error of 8.3%. An optimization problem is formulated to design and set the integrated system optimal operation to produce 10 L of fresh water per day meeting the fresh water needs of a typical residential. Using five TEC channels of a length of 1.2 m and an area of 0.07 × .05 m2 integrated with 1.2‐m2 solar distiller that recirculates a constant air mass flow rate of 0.15 kg s−1 is capable of meeting water demand when air mass flow rate through each TEC channel is optimally set at 0.0155 kg s−1. The associated optimal electrical current input to the TEC modules varied depending on the month and is set at 2.2 A in June, 2.1 A in July and 2.0 A in August, September and October. Copyright © 2011 John Wiley & Sons, Ltd.

No abstract

No abstract

The paper presents an open-source Python tool for parameter estimation in FMI-compliant models, called Mod-estPy. The tool enables estimation of model parameters using user-defined sequences of methods, which are particularly helpful in non-convex problems. A user can start estimation with a chosen global search method and subsequently refine the estimates with a local search method. Several methods are available already and the tool's architecture allows for easily adding new ones. The advantages of having a single interface to multiple methods and using them in sequences are highlighted on a case study in which the parameters of a Modelica-based gray-box model of a building zone (nonlinear, multi-output) are estimated using 9 different combinations of methods. The methods are compared in terms of accuracy and computational performance.