New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

Energy Efficient Building (EEB) design decisions that have traditionally been made in the later stages of the design process now often need to be made as early as the feasibility analysis stage. However, at this very early stage, the design frame does not yet provide sufficient details for accurate simulations to be run. In addition, even if the decision-makers consider an exhaustive list of options, the selected design may not be optimal, or carefully considered decisions may later need to be rolled back. At this stage, design exploration is much more important than evaluating the performance of alternatives, thus a more transparent and interpretable design support model is more advantageous for design decision-making. In the present study, we develop an EEB design decision-support model constructed by a transparent meta-model algorithm of simulations that provides reasonable accuracy, whereas most of the literature used opaque algorithms. The conditional inference tree (CIT) algorithm exhibits superior interpretability and reasonable classification accuracy in estimating performance, when compared to other decision trees (classification and regression tree, random forest, and conditional inference forest) and clustering (hierarchical clustering, k-means, self-organizing map, and Gaussian mixture model) algorithms.

School retrofitting should aim to not only improve its energy performance, but also maintain a good IAQ. An optimal combination of retrofitting measures must be selected by considering the transient state changes of the outdoor and built environments. Although a simulation is an effective platform to evaluate a combination of the retrofitting measure candidates, there is a general lack of practical methods for practitioners to collect the field data and prepare a reliable IAQ baseline model within a project timeline. This study suggests a suite of tools to generate a classroom IAQ baseline, which includes standardized diagnostic scenarios based on common retrofitting practices and measurement protocols of classroom IAQs; the diagnostic scenarios intend to quantify the dilution and filtration capabilities of classrooms through deposition, infiltration, and natural/mechanical ventilations when a high concentration is observed; the first principle model is developed to normalize the measurement, which is fitted against the measurement by adjusting its parameter values. In order to save time and effort for practitioners, automated and semi-automated calibrations that run in a short time are also developed. While the automated calibrations performed better in some cases, the semi-automated calibrations performed better than the automated ones in many cases, the CV-RMSE were smaller, by between −7% and −0.5%. Meanwhile, it took a comparably larger effort and longer time (>1 h for the worst cases) for the heuristic calibrations to have a similar accuracy with the machine-driven calibrations. If the model structure suffers a problem with the measurement, the modeler must intervene in the calibrations. In this case, semi-automation can be a diagnostic tool for a practitioner to intuitively determine from which variables to start the calibration.

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This study aims to find a simple rule to see if a freezer door is left open, or if refrigerant is insufficiently charged. We devised a comparative experiment to find an opportunity where the simple rule is able to replace the machine learning approach. In contrast to the previous study performed with the machine learning approach, this paper has derived more explanatory variables and rules for diagnosing the operation faults of a freezer. i) Freezer wall temperature is found to be the most sensitive variable for diagnosing the door opening. When the open door rule based on the freezer wall temperature is applied to the actual state, however, only 62.4% of windows are assessed as "True". In other words, there is 37.6% chance of a false alarm. ii) We also assume that refrigerant mass is proportional to the ratio of accumulated power to power factor. However, only 51.5% of windows turn out "True" when the insufficient refrigerant rule is applied to the actual state. When refrigerant is actually insufficient, there is a 33% chance that critical false alarms still occur, which can harm the credibility of the insufficient refrigerant rule. iii) To diagnose if the door is left open by means of using machine learning, all three variables (Active Power, Laboratory indoor temperature, Refrigerator wall temperature) may not be necessary. Only the refrigerator wall temperature framed within a 3 minute window appears sufficiently credible, rather than the refrigerator wall temperature at each time step. iv) To diagnose if the refrigerant is insufficiently charged, instead of using the three variables, only power related variables including active power and power factor would be sufficient for simpler monitoring and more accurate assessment. 