Model Predictive Control can cope with conflicting control objectives in building energy managements. In terms of user satisfaction, visual comfort has been proven in several studies to be a crucial factor, however thermal comfort is typically considered the only important aspect. Besides human well-being, visual comfort strongly impacts the productivity of the occupants in offices. Therefore, from an economic point of view, it is essential to include visual comfort in Model Predictive Control for buildings. In this paper semi-linear support vector machine is applied to learn suitable models for visual comfort measured by Daylight Glare Probability. The resulting model is incorporated into a Model Predictive Control framework, together with an autoregressive exogenous model accounting for the thermal dynamics of the building. The approach is validated through an extensive numerical case study, and the benefits of including visual comfort and blind control in the Model Predictive Control problem are evaluated. We observe that the proposed Model Predictive Control scheme ensures both the thermal and visual comfort constraints at the expense of 2.2% to 7.2% higher energy consumption compared to the benchmark Model Predictive Control configuration, which considers only the thermal comfort constraints.

In this one-day workshop we are going to make access. We aim to counteract the phenomenon that access to making (e.g., in makerspaces, fablabs, etc.) is not equally distributed, with certain groups of people being underrepresented (e.g., women∗1). After brief introductions from participants and a set of three impulse keynotes, we will envision and "make"interventions together, such as speculative or provocative objects and actions. The workshop takes a constructive stance with the goal to not rest on empirical and theoretical findings or individual experiences, but to translate those into viable interventions. These serve as exemplars of findings with the clear goal of being deployed soon after.