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.

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A semi-physical model for the simulation of oil, gas and biomass space heating boilers has been parameterized based on measurements on nine different boiler units and simulation results have been compared to results obtained from measurements in steady state and transient operation. Although the agreement between simulated and measured boiler efficiencies was within the range of measurement uncertainties in most cases, model improvements are expected to be possible concerning the heat capacitance modelling in cycling on/off operation as well as influences of start and stop behaviour on the overall efficiency. It is found that electricity consumption during cycling on/off operation of small pellets or oil space heating boilers may have a significant influence on the overall energy balance of these units. This influence increases strongly with decreasing heat load and increasing number of on/off cycles.

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