Search

Cooling demands of commercial buildings present a relevant challenge for a sustainable future. They account for over half of the overall energy needs for the operation of an average office building in warm climates, and this situation is expected to become more pressing due to increasing temperatures in cities worldwide. To tackle this issue, it is widely agreed that the application of passive strategies should be the first step in the design of energy efficient buildings, only using active equipment if it is truly necessary. Nonetheless, there is still further need for information regarding the potential limits derived from their application. This paper explores the effectiveness of selected passive cooling strategies in commercial buildings from warm climates, defining performance ranges based on the assessment of multiple scenarios and climate contexts. This task was conducted through the statistical analysis of results from documented research experiences, to define overall ranges and boundary conditions; and through software simulation of selected parameters to isolate their impact under a controlled experimental setup. General findings showed that the mere application of passive strategies is not enough to guarantee relevant savings. Their effectiveness was conditioned to both the harshness of a given climate and different building parameters. Specific recommendations were also discussed for the selected passive strategies considered in the evaluation. [ABSTRACT FROM AUTHOR]

Buildings consume a significant amount of energy worldwide in maintaining comfort for occupants. Building energy management systems (BEMS) are employed to ensure that the energy consumed is used efficiently. However these systems often do not adequately perform in minimising energy use. This is due to a number of reasons, including poor configuration or a lack of information such as being able to anticipate changes in weather conditions. We are now at the stage that building behaviour can be simulated, whereby computer programs can be used to predict building conditions, and therefore enable buildings to use energy more efficiently, when integrated with BEMS (i.e. simulation assisted control [1]). In this paper we demonstrate a low cost BEMS that uses building simulation to predict optimised electrical heating startup control points. Those that use electricity for heating in Scotland, where this study was based, tend to be fuel-poor, hence there is a strong case for optimisation, particularly when electricity costs nearly three times as much as the equivalent unit of gas for heating applications. The proposed system demonstrated a 50% energy saving in the reduced heating time compared to scheduling when retrospectively evaluated. [ABSTRACT FROM AUTHOR]

Natural ventilation is a green building strategy that improves building energy efficiency, indoor thermal environment, and air quality. However, in practice, it is not always clear when and how to utilize the natural ventilation and coordinate its operation with the HVAC system. This paper introduces a reinforcement learning control strategy, specifically through model-free Q-learning, that makes optimal control decisions for HVAC and window systems to minimize both energy consumption and thermal discomfort. This control system evaluates the outdoor and indoor environments (temperature, humidity, solar radiation, and wind speed) at each time step, and responds with the best control decision that targets both immediate and long-term goals. The reinforcement learning control is evaluated through numerical simulation on a building thermal model and compared with a rule-based heuristic control strategy. Case studies in hot-and-humid Miami and warm-and-mild Los Angeles demonstrated the superior performance of reinforcement learning control, which led to 13% and 23% lower HVAC system energy consumption, 62% and 80% lower discomfort degree hours, and 63% and 77% fewer high humidity hours compared to heuristic control. Unlike heuristic control that requires specific knowledge of individual buildings and the creation of exhaustive decision-making scenarios to improve performance, reinforcement learning control guarantees optimality through self-advancement on given goals and cost functions and is able to adapt to stochastic occupancy and occupant behaviors, which is difficult to accommodate by heuristic control. [ABSTRACT FROM AUTHOR]

The requirements concerning energy performance of buildings and their internal installations, particularly HVAC systems, have been growing continuously in Poland and all over the world. The existing, traditional calculation methods following from the static heat exchange model are frequently not sufficient for a reasonable heating design of a building. Both in Poland and elsewhere in the world, methods and software are employed which allow a detailed simulation of the heating and moisture conditions in a building, and also an analysis of the performance of HVAC systems within a building. However, these systems are usually difficult in use and complex. In addition, the development of a simulation model that is sufficiently adequate to the real building requires considerable time involvement of a designer, is time-consuming and laborious (Menberg et al., 2019) [1]. A simplification of the simulation model of a building renders it possible to reduce the costs of computer simulations. The paper analyses in detail the effect of introducing a number of different variants of the simulation model developed in DesignBuilder on the quality of final results obtained. The objective of this analysis is to find simplifications which allow obtaining simulation results which have an acceptable level of deviations from the detailed model, thus facilitating a quick energy performance analysis of a given building. [ABSTRACT FROM AUTHOR]