Previous studies have identified a significant potential in using economic model predictive control for space heating. This type of control requires a thermodynamic model of the controlled building that maps certain controllable inputs (heat power) and measured disturbances (ambient temperature and solar irradiation) to the controlled output variable (room temperature). Occupancy related disturbances, such as people heat gains and venting through windows, are often completely ignored or assumed to be fully known (measured) in these studies. However, this assumption is usually not fulfilled in practice and the current simulation study investigated the consequences thereof. The results indicate that the predictive performance (root mean square errors) of a black-box state-space model is not significantly affected by ignoring people heat gains. On the other hand, the predictive performance was significantly improved by including window opening status as a model input. The performance of black-box models for MPC of space heating could therefore benefit from having inputs from sensors that tracks window opening.
Several studies have indicated that Model Predictive Control (MPC) of space heating systems can utilize the thermal mass of residential buildings as short-term thermal storage for various demand response purposes. Realization of this potential relies heavily on the accuracy of the model used to represent the thermodynamics of the building. Such models, whether they are grey box or black box, are calibrated using relevant data obtained from initial measurements, and the performance of the calibrated model is validated using data from a subsequent period. However, many studies use validation periods with weather conditions similar to those of the calibration period. Only a few studies investigate whether the calibrated model performs satisfactory when subjected to significantly different conditions. This paper presents data from a simulation-based study on the effect of seasonal weather changes on the performance of a black-box model. The study was conducted using 11 years of Danish weather data (2008-2018). The results indicate that the performance of the black-box model deteriorate as the weather data conditions become increasingly different from those used in the initial model calibration. Further, the results show that calibration in heating season leads to satisfactory model performance through the heating season, but lower performance in transitional seasons (especially spring). Results also show that calibration in February led to highest model performance through heating season, while calibration in March led to satisfactory model performance in the whole heating and fall season.
Background: The ambient and indoor environment are pivotal to our health. We spend most of our time indoors within our home, why our home is where we are most exposed to indoor pollutants and indoor air quality (IAQ). Populations within social housing areas are more vulnerable due to advanced age, co-morbidity and social economic status. Commonly, studies within social housing are cross-sectional, few Nordic longitudinal studies exist, and fewer studies combine quantitative and qualitative measurements in a mixed method approach. Method: This research proposal provides an extensive detailed description of the design and methodology of the HOME-Health study. The study is a longitudinal study and is a natural experiment employing structured surveys, objective measurements of indoor air parameters, lung function test and qualitative semi-structured interviews. Data collection are conducted seasonally (winter and summer), before and after building energy renovation (BER). Generalisability: The study population before BER (n = 432) was explored and found similar to the Danish social housing population in terms of age, gender, persons per apartment and migration status. Future analyses should be stratified by multi-family apartments and terraced houses. Research aim: The aim of the HOME-Health study is to provide knowledge about residents’ seasonal state of health, perception of indoor enviromental quality (IEQ), IEQ-related behaviours and practices, and objective measurements of IAQ before and after BER. By applying a design with repeated measurement before and after BER, and combining both objective and subjective quantitative as well as qualitative data the study is expected to create in-depth knowledge. Future results will provide evidence of both energy-savings and non-energy savings from different BER projects. Knowledge which are expected to benefit future renovation projects within social housing areas.
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