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This group aims to be a vehicle for the discussion on the objectives and contents of the Journal of Validation of Computational Models, as well as any other aspect related to the verification and validation (V&V) of computational models in all areas of engineering and applied science.

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Increasing the energy efficiency of the built environment has become a priority worldwide and especially in Europe. Because of the relatively low turnover rate of the existing built environment, energy efficiency retrofitting appears to be a fundamental step in reducing its energy consumption. Last experiences have shown that there is a vast energy efficiency potential lying in the building stock, and it is mainly untapped. One of the reasons is a lack of robust methodologies able to evaluate the effect of applied energy efficiency measures and inform about the expected impact of potential retrofitting strategies. Nowadays, dynamic measured data coming from automated metering infrastructure provides valuable information to evaluate the effect of energy conservation strategies. For this reason, energy performance modeling and assessment methods based on this data are starting to play a major role. In this paper, several methodologies for the measurement and verification of energy savings, and for the prediction and recommendation of energy retrofitting strategies, are analysed in detail. Practitioners looking at different options for these two processes, will find in this review a thorough and detailed overview of the different methods that can be used. Guidance is also provided to determine which method could work best depending on the specific case under analysis. The reviewed approaches include statistical learning models, machine learning models, Bayesian methods, deterministic approaches, and hybrid techniques that combine deterministic and data-driven modeling. Existing research gaps are identified and prospects for future investigation are presented within the main conclusions of this research work.

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Methods to obtain accurate estimations of the savings generated by building energy efficiency interventions are a topic of great importance, and considered to be one of the keys to increase capital investments in energy conservation strategies worldwide. In this study, a novel data-driven methodology is proposed for the measurement and verification of energy efficiency savings, with special focus on commercial buildings and facilities. The presented approach involves building use characterization by means of a clustering technique that allows to extract typical consumption profile patterns. These are then used, in combination with an innovative technique to evaluate the building’s weather dependency, to design a model able to provide accurate dynamic estimations of the achieved energy savings. The method was tested on synthetic datasets generated using the building energy simulation software EnergyPlus, as well as on monitoring data from real-world buildings. The results obtained with the proposed methodology were compared with the ones provided by applying the time-of-week-and-temperature (TOWT) model, showing up to 10% CV(RMSE) improvement, depending on the case in analysis. Furthermore, a comparison with the deterministic results provided by EnergyPlus showed that the median estimated savings error was always lower than 3% of the total reporting period consumption, with similar accuracy retained even when reducing the total training data available.

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The conceptual simplicity and the ability of cohesive finite element models to describe complex fracture phenomena makes them often the approach of choice to study dynamic fracture. These models have proven to reproduce some experimental features, but to this point, no systematic study has validated their predictive ability; the difficulty in producing a sufficiently complete experimental record, and the intensive computational requirements needed to obtain converged simulations are possible causes. Here, we present a systematic integrated numerical–experimental approach to the verification and validation (V&V) of simulations of dynamic fracture along weak planes. We describe the intertwined computational and the experimental sides of the work, present the V&V results, and extract general conclusions about this kind of integrative approach.

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