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This study aimed to investigate the applicability of the solar heating system in different geographical regions with different meteorological conditions, of which parabolic trough solar collectors (PTCs) were operated with the absorption heat pumps (AHP) and oil/water heat exchanger (OWHE) at medium and low operating temperature respectively. The heat transfer model for PTCs was constructed with a lumped parameter method and validated by experimental results. The thermal performance of the system was evaluated by the index of primary energy ratio (PER). The results showed that on overcast days with low direct normal irradiance (DNI), the operation of the PTC + AHP/OWHE system was not cost-effective. On cloudy days with high DNI lasting for a short period (e.g. 2 h), the operation of the PTC + OWHE system was better than that of the PTC + AHP system as the latter needed more preheating energy before the system operation. While on sunny days with high DNI lasting for a longer period (e.g. 8 h), the PTC + AHP system was suggested to be operated as it owned higher PER values. Besides, the solar energy distributions in China could be divided into four categories, i.e. rich (S1), relatively rich (S2), available (S3) and absent (S4). In S1 region the PTC + AHP system was suggested to be operated. In S4 region the PTC + AHP/OWHE system was not suggested to be operated. While in S2 and S3 region, whether the operation of the PTC + AHP/OWHE system was suggested depending on the meteorological conditions. With these findings the developing strategy of the solar heating system in different geographical regions can be devised and the operating strategy on a specific day with different weather conditions can be developed, which is helpful to guide the application of solar energy and improve the energy structure in domestic heating.

Bayesian analysis has attracted more attention in calibrating building energy models since it can naturally account for uncertainty of input parameters. However, standard Bayesian analysis requires to compute likelihood functions to represent the probability of the observed data under a statistical energy model. This makes the implementation of standard Bayesian analysis difficult for calibrating dynamic building energy models. Therefore, this research proposes a new Bayesian method by combining the approximate Bayesian computation with machine learning techniques in calibrating building models without computing likelihood functions. The results indicate that this new Bayesian computation approach combined with the machine learning algorithms can provide fast and reliable calibration for building energy models created using the EnergyPlus program. The calibration metrics in terms of the coefficient variations of root mean square errors from this new Bayesian method are well below the threshold value (15%) recommended from the ASHRAE standard for monthly energy data. Moreover, the better calibration results can be obtained by using linear or nonlinear post-adjustment techniques to account for the differences between the simulated and observed energy data. The method proposed here can significantly extend the application of Bayesian analysis in calibrating building energy models under uncertainty due to its simplicity and fast computation. Furthermore, the new Bayesian approach combined with machine learning technique can also be used to calibrate the simulation-based models for various types of energy systems.