The importance of integrally simulating the building, HVAC and control systems, and occupants’ impact for energy predictions of buildings including temperature and humidity control:validated case study museum Hermitage Amsterdam

\u3cp\u3eFor buildings including temperature and humidity control, this study compares the energy prediction accuracy of a ZABES-model (Zone Air Building Energy Simulation) to an IBES-model (Integral Building Energy Simulation), which additionally includes models of the air handling unit (AHU) and controllers. Museum Hermitage Amsterdam served as a case study. For one year, measurements were performed in the main exhibition hall and its AHU. The ZABES-model was developed using heat air and moisture model for building and systems evaluation (implemented in MATLAB). The IBES-model was developed in Simulink and consists of the ZABES-model and models of AHU-components and controllers. Both models have been validated in detail. The IBES-model’s energy prediction errors are well within 10%. However, the ZABES-model underestimated the total annual energy consumption by 84%. Moreover, including occupants’ heat and moisture gains leads to realistic results using the IBES-model, but leads to unrealistic results using the ZABES-model. In conclusion, IBES-models are essential for reliable energy predictions of buildings including humidity control.\u3c/p\u3

Dynamic setpoint calculation including collection and comfort requirements:Energy impact for museums in Southern Europe

\u3cp\u3eFirstly, this study presents an algorithm for setpoint calculation of museums' indoor temperature (T) and relative humidity (RH) integrating collection requirements according to ASHRAE and thermal comfort requirements according to adaptive temperature limits that follow from a one-year long comfort study in case study museum Hermitage Amsterdam. Secondly, this algorithm is implemented into a building energy simulation model to assess the energy impact for various cases: Five levels of museum indoor climate conditioning are applied to four building quality levels (ranging from a historical building to a purpose-built museum building) using weather data from six locations in Southern Europe. A validated building energy simulation model of museum Hermitage Amsterdam was adjusted to represent the four building quality levels, and technical-reference-year (TRY) weather data of six locations were used. The conclusions: The algorithm enables smooth control of hourly and seasonal adjustments in T and RH setpoints; the algorithm boosts energy efficiency due to more effective use of the permissible ranges of T and RH; improving the building quality quickly follows the law of diminishing returns due to internal heat and moisture loads; supposing to result in the same collection risk, subclass A\u3csub\u3es\u3c/sub\u3e (with seasonal adjustments, but smaller hourly fluctuations) is more energy efficient than subclass A\u3csub\u3ed\u3c/sub\u3e (no seasonal adjustments, but larger hourly fluctuations) for most locations.\u3c/p\u3

Dynamic setpoint control for museum indoor climate conditioning integrating collection and comfort requirements: Development and energy impact for Europe.

This study presents a seven-step algorithm for hourly setpoint calculation of museums' indoor temperature (Ti) and relative humidity (RHi) integrating collection requirements (ASHRAE) and thermal comfort requirements. Moreover, building energy simulation results provide insight into the energy impact of five levels of museum indoor climate conditioning applied to four building quality levels (ranging from a historical building to a purpose-built museum building) using weather data from twenty locations throughout Europe. The five levels of indoor climate conditioning were calculated using the presented setpoint algorithm, a validated simulation model of museum Hermitage Amsterdam was adjusted to represent the four building quality levels, and technical-reference-year (TRY) weather data of twenty locations were used. The conclusions: The setpoint algorithm enables smooth control of seasonal adjustments, integrated with permissible short fluctuations of T and RH (according to ASHRAE classes); improving the building quality quickly follows the law of diminishing returns; supposing to result in the same collection risk, subclass Ad (no seasonal adjustments, but larger hourly fluctuations) is more energy efficient than subclass As (with seasonal adjustments, but smaller hourly fluctuations) for most locations; although class AA is more stringent than subclass As, class AA appears to require less energy than subclass As for some locations, due to efficiency differences of the humidification and dehumidification processes

Should solar irradiance be included in inverse modeling?

No abstract

Simplified thermal and hygric building models : a literature review

This paper provides a systematic literature review on simplified building models. Questions are answered like: What kind of modelling approaches are applied? What are their (dis)advantages? What are important modelling aspects? The review showed that simplified building models can be classified into neural network models (black box), linear parametric models (black box or grey box) and lumped capacitance models (white box). Research has mainly dealt with network topology, but more research is needed on the influence of input parameters. The review showed that particularly the modelling of the influence of sun irradiation and thermal capacitance is not performed consistently amongst researchers. Furthermore, a model with physical meaning, dealing with both temperature and relative humidity, is still lacking. Inverse modelling has been widely applied to determine models parameters. Different optimization algorithms have been used, but mainly the conventional Gaus–Newton and the newer genetic algorithms. However, the combination of algorithms to combine their strengths has not been researched. Despite all the attention for state of the art building performance simulation tools, simplified building models should not be forgotten since they have many useful applications. Further research is needed to develop a simplified hygric and thermal building model with physical meaning