The building sector contributes significantly to global energy consumption and emission of greenhouse gases. Thermal insulation along with installation of energy-efficient building systems can reduce energy needs while preserving or improving occupant comfort levels. Still sensible control decisions, to harmoniously and effectively operate all building thermal systems, can be used to further improve building energy performance and/or thermal comfort. In this article, a simulation-assisted methodology is presented to automatically generate such decisions. There are two ingredients to our approach: a thermal simulation model-a surrogate of the real building-used to evaluate the effects of potential decisions; and, a cognitive adaptive optimization algorithm used to intelligently search for the "best" control decision. A user-defined cost function is used to compare various decision strategies. Corroborating simulation results are presented to quantify the expected benefits of the proposed approach.
Abstract-Energy efficient passive designs and constructions have been extensively studied in the last decades as a way to 2 improve the ability of a building to store thermal energy, increase its thermal mass, increase passive insulation and 3 reduce heat losses. However, many studies show that passive thermal designs alone are not enough to fully exploit the 4 potential for energy efficiency in buildings: in fact, harmonizing the active elements for indoor thermal comfort with the 5 passive design of the building can lead to further improvements in both energy efficiency and comfort. These 6 improvements can be achieved via the design of appropriate Building Optimization and Control (BOC) systems, a task 7 which is more complex in high-inertia buildings than in conventional ones. This is because high thermal mass implies a 8 high memory, so that wrong control decisions will have negative repercussions over long time horizons. The design of 9 proactive control strategies with the capability of acting in advance of a future situation, rather than just reacting to 10 current conditions, is of crucial importance for a full exploitation of the capabilities of a high-inertia building. This paper 11 applies a simulation-assisted control methodology to a high-inertia building in Kassel, Germany. A simulation model of 12 the building is used to proactively optimize, using both current and future information about the external weather 13
I. INTRODUCTION 20Motivated by the fact that around half of the energy produced on the planet is used for the daily needs of building systems, 21 and floors are made to store, cage and distribute solar energy in the form of heat in the winter and reject solar heat in the 26 summer; passive cooling with different forms of ventilation and earth coupling; passive day-lighting to most effectively capture 27
The computational cost for the repeated evaluation of zonal-type building simulation models can be prohibitive especially in contexts, such as Building Optimization and Control Design, where repeated evaluation of the models -for different initial and boundary conditions -is required. In the present paper, two techniques to reduce simulation time are investigated: (i) geometry simplification for periodic geometries; and, (ii) the use of co-simulation to split a building into simpler sub-buildings, that can be evaluated in parallel, exploiting the resources of multi-core computational architectures. These simulation speed-up approaches are evaluated, with respect to accuracy and computational effort, against the validated full-scale models of two real buildings.
Solar thermal systems in residential buildings are generally controlled by two-level controllers, which activate solar thermal or at times with low solar radiation auxiliary energy supply into a thermal storage. Simple controllers do not have any information on actual or expected solar radiation. This leads to interference of auxiliary-and solar heat supply, which reduces the share of solar thermal energy fed into the thermal storage. Increasing accuracy of weather forecast data suggests incorporation of this information in the control algorithm. This work analyzes the maximum potential performance enhancement when applying such an intelligent predictive control. Two solar thermal systems with one auxiliary source respectively are designed in TRNSYS-these systems represent the base case. Further, a number of simulations are conducted with minor variations for the plant parameters-this gives generic results for different system configurations. In addition, each system configuration is altered to mimic the behavior of a plant with intelligent predictive control. Comparison of results indicates an improvement potential up to 10% for annual solar fractions and up to 30% for monthly solar fractions. The performance bound with respect to the annual auxiliary energy savings is approximately 8%.
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