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Abstract Sensitivity analysis (SA) can be applied to building energy models (BEM) to identify which input parameters that drive the majority of the model output variation. The screening-based Morris method is often applied for this purpose; however, consideration regarding the effect of the user-defined number of levels (p) and trajectories (r) on the obtained results are rare. This paper investigates how the choice of p and r affects the outcome of a SA using the Morris method on a high fidelity BEM. The results indicates that the Morris method was not able to replicate the ranking from the variance-based Sobol' method no matter the choice of r and p. It was, however, able to identify groups of input parameters (parameter clusters) most sensitive to the model output variability, but it required significantly more r than usually applied in studies featuring the Morris method. The reason is that marginal differences in absolute values of elementary effects (the sensitivity indices of the Morris method) for some input parameters may lead to a change in ranking position several times as the number of r increases. Users of the Morris method must therefore not be predetermined on the size of the parameter cluster; instead, they must make a visual assessment of the convergence of the parameter ranking to qualitatively determine the appropriate size of parameter cluster. The final recommendation for future studies deploying the Morris method for SA applied to a high fidelity BEM is to choose p ≥ 4 as it seems to lead the analysis towards a more truthful ranking, and then run simulations in steps of r = 100 when making the visual assessment to determine convergence and the size of parameter cluster. The identified need for more r questions the general notion that the Morris method is a computationally efficient screening method in terms of absolute time use. However, the Morris method is still much more computational efficient than a Sobol'-based analysis if the purpose of the SA is to identify a cluster of input parameters most sensitive to the model output variability. [ABSTRACT FROM AUTHOR]

Building life cycle assessment (LCA) aims at guiding decision towards more sustainable alternatives of a building project. But, in order to successfully reduce the high environmental impacts of the building sector, the results of building LCA have to be robust. Sensitivity analysis (SA) helps at investigating the sources of uncertainty and variability in a model and assessing the results' robustness. Choosing an adapted SA method remains difficult: using the more precise SA methods would require a too large calculation time, particularly for an application in early design phase. In this paper, the most influential factors of building LCA tools, linked with energy simulation tools, were investigated using screening and global SA methods that are compatible with the building LCA fields. The relative influence of the uncertain factors served as a metric to compare more precisely the results given by this large panel of methods. The results show that despite their different precision vs computational cost compromises, all the methods identified the same set of influential factors. In the studied case of a passive single family house, they consisted in the building lifetime, the electricity production mix and factors affecting the energy consumption. However, the relative influence of the factors differed. Two new method adaptations were investigated in order to obtain precise results in a reasonable simulation time. The adaptation of the Plackett and Burman (PB) screening performed well compared to the reference method (Sobol indices), as the same set of influential factors was always identified in a calculation time divided by almost 30. [ABSTRACT FROM AUTHOR]

Energy consumption models play an integral part in energy management and conservation, as it pertains to buildings. It can assist in evaluating building energy efficiency, in carrying out building commissioning, and in identifying and diagnosing building system faults. This review takes an in-depth look at energy-demand prediction models for buildings in that it delves into recent developments in building energy approaches used to predict energy usage. By enlisting current approaches to the modelling of buildings, methods for building energy simulations can be categorized into four level classes as follows: (i) data-driven approaches; (ii) physics-based approaches; (iii) large scale building energy forecasting approaches; and (iv) hybrid approaches. The focus of this review is to target the data-driven approach and large-scale building energy predicting-based approaches. Here the data driven approaches can be categorized by (1) artificial neural network based approaches; (2) clustering based approaches; (3) statistical and machine learning-based approaches; and (4) support vector machine based approaches. From there, the type of data-driven based approach is further grouped by (a) benchmarking models; (b) energy-mapping models; (c) energy forecasting models; and (d) energy profiling models. Large-scale building-energy prediction techniques is then categorized as follows: (1) white-box based approaches; (2) black-box based approaches, and (3) grey-box based approaches. The current study explores first-rate data-driven based approaches about building energy analysis for industrial, commercial, domestic, etc., within a rural and urban setting. This review paper is based on the necessity of identifying points of departure and research opportunities for urban and rural-level analyses of building level energy performance. A variety of issues are explored which include: energy performance metrics; end-use of different building types; multiple levels of granularity; and urban and rural scales. Each technique encompasses a variety of input information as well as varying calculations or simulation models along with furnishing contrasting outcomes that suggest a variety of usages. A thorough review of each technique is presented in this study. This review highlights strengths, shortcomings, and purpose of the methods of numerous data-mining based approaches. A comprehensive review of energy forecasting models that are specified in the literature part is also provided. [ABSTRACT FROM AUTHOR]

Developing BEPS models which predict energy usage to a high degree of accuracy can be extremely time consuming. As a result, assumptions are often made regarding the input data required. Making these assumptions without introducing a significant amount of uncertainty to the model can be difficult, and requires experience. Even so, rules of thumb from one geographic region are not automatically transferrable to other regions. This paper develops a methodology which can be used to determine useful guidelines for defining the most influential input data for an accurate BEPS model. Differential sensitivity analysis is carried out on parametric data gathered from five archetype dwelling models. The sensitivity analysis results are used in order to form a guideline minimum set of accurately defined input data. Although the guidelines formed apply specifically to Irish residential dwellings, the methodology and processes used in defining the guidelines is highly repeatable. The guideline minimum data set was applied to practical examples in order to be validated. Existing buildings were modelled, and only the parameters within the minimum data set are accurately defined. All building models predict annual energy usage to within 10% of actual measured data, with seasonal energy profiles well-matching. [ABSTRACT FROM AUTHOR]

Climatic zoning for building energy efficiency applications is an important element in building energy policy and regulations. There are several methodologies available to conduct climatic zoning, providing significantly different results. Currently, there are no procedures to assess the validity of a proposed climatic zoning, hindering the decision to use one particular climatic zoning methodology instead of another. This paper introduces a quality index and a procedure to support the validation of climatic zoning. The procedure is based on building performance simulation results concerning the building stock that is targeted in the climatic zoning policy or program. Simulation results are used to calculate a new index, the Mean Percentage of Misclassified Areas (MPMA), which assesses the quality of the zoning under analysis. The capabilities of this procedure were demonstrated by the evaluation of four alternatives for the climatic zoning of Nicaragua, obtained using different methodologies and previously reported in the literature. The building stock used in this case study is composed of a few archetypes based on typical naturally ventilated dwellings in this country. Simulations were conducted using the program EnergyPlus for a total of 328 locations in Nicaragua. Degree-hours of discomfort based on the adaptive model of ASHRAE Standard 55 were used as a performance indicator. Results indicate that zoning obtained using cluster analysis and cooling degree-days may misclassify 1 out of 5 areas in Nicaragua (MPMA around 18–20%). This study concludes that the validation procedure and proposed index are useful for highlighting qualities and deficiencies of existing climatic zoning methods, particularly when these methods are used in less conventional applications, such as for policy making targeting naturally ventilated dwellings in tropical climates. The application of this procedure in more than 50 countries which adopt climatic zoning is foreseen as the next step in his area, substantially affecting the prescription of building materials and components worldwide. [ABSTRACT FROM AUTHOR]

Innovative and efficient ways to carry out numerical simulations are worth of investigation to reduce the computational complexity of building models and make it possible to solve complex problems. This paper presents a reduced order model, based on Proper Generalised Decomposition (PGD), to assess 2-dimensional heat and moisture transfer in walls. This model is associated with the multizone model Domus using an indirect coupling method. Both models are co-simulated to perform whole-building hygrothermal simulation, considering 2D transfer in walls. The whole-building model is first validated with data from the IEA Annex 41. Then, a case study is considered taking into account a 2-zones building with an intermediary shared wall modelled in 2 dimensions to illustrate the importance of the technique to analyse the hygrothermal behaviour of the wall. It has been highlighted that the whole model enables to perform more precisely analyses such as mould growth on the internal surface. In addition, important theoretical numerical savings (90%) are observed when compared to the large original model. However, the effective numerical savings are not so important (40%) due to the limitations of the co-simulation method. [ABSTRACT FROM AUTHOR]