This paper proposes an improvement of the conventional Lightweight Steel Frame (LSF) wall structure suitable for the design of high-performance modular buildings. A mobile module, named MUZA, is used as a case study building to analyse the performance of such LSF structures in terms of their thermal bridging effect on the U-value of the opaque envelope elements, linear heat losses at junctions, and moisture condensation risk, as well as thermal bridging effect on the overall energy performance of the building. The study included an additional climate- and orientation-dependent analysis that examined the performance of MUZA under various conditions. The main conclusion is that the steel studs increase the U-value from 28.4% to 41.6% compared to cases without the studs, which consequently increases transmission losses through opaque elements. Thanks to the continuous covering of the metal studs with thermal insulation, the thermal bridges at the element junctions are minimized, and in almost all cases, the Ψ-values are well below 0.1 W/(m·K) and are free from moisture condensation. The overall impact of thermal bridges on heating energy demand is significant, while the impact on cooling energy is less pronounced. The designed module with the proposed LSF wall structure can meet the Croatian requirements for Nearly Zero-Energy Buildings (NZEB), but the shading devices and photovoltaics orientation must be optimized depending on the climatic conditions and the orientation of the large transparent openings. MUZA can be a promising solution for post-disaster housing, providing better indoor environmental quality, healthy living conditions, and low energy bills for the affected people. In addition, it can also be used for permanent housing when a fast and robust modular construction is required which is also energy efficient and sustainable.

Reducing heat losses through the building envelope is one of the most important aspects to be met if the targets set by the European Union are to be achieved. In order to obtain a more realistic energy demand, dynamic heat transfer simulations are used to calculate the energy consumption of buildings, since steady-state calculations do not take into account the thermal mass in buildings. These dynamic simulations employ methods based on analytical models since numerical models are unsuitable for longer time periods. The analytical models used herein fall into the category of conduction transfer functions (CTFs). Two methods for computing CTFs that are addressed in this research are the Laplace method and the State-Space method. The objective of this paper is to verify the efficiency of the Laplace and State-Space methods for calculating the energy demand of a building in the case of heavyweight building elements and shorter sampling times, and to provide a means for improving the algorithms used by these methods. The Laplace and State-Space method algorithms were implemented in Mathematica, and the results were compared to EnergyPlus and TRNSYS, which use similar algorithms to calculate energy demand. It was shown in this paper that for the heavyweight wall element and a time step of 0.25 h, the difference between the total energy transferred through the inner surface was about 31% for EnergyPlus and 78% for TRNSYS compared to the reference solution. For the lightweight wall element, the results were stable for the time step of 0.25 h, but for the time step of 0.1 h, the differences were 45.64% and 303% between EnergyPlus, TRNSYS and the reference solution, respectively, compared to the State-Space and Laplace methods for which the maximum difference was 12.03% with a time step of 0.1 h. While dynamic heat transfer simulations are better than calculations based on steady-state boundary conditions, they also have their limitations and could lead to unsatisfactory results for short sampling times and if not applied properly.

To reduce the impact on climate change, many countries have developed strategies for the building sector with a goal to reduce the energy demands and carbon emission of buildings. As most buildings that exist today will very likely exist in foreseeable future, many buildings will need to undergo major renovations. One of the most important parameters in determining the transmission heat losses through the building envelope is the U-value, i.e., thermal transmittance, and it is simply the rate of heat transfer per unit temperature. Since the U-value is one of the most important parameters regarding building energy performance, envelope elements that do not perform well in terms of transmission heat losses must undergo a renovation processes. The in-situ U-value of building elements is usually determined by the Heat Flux Method (HFM). One of the issues of current application of the HFM is the measurement duration. This paper explores the possibilities of reducing the measurement time by predicting the heat flux rate using a multilayer perceptron (MLP), a class of artificial neural network. The MLP uses two input layers that are the interior and exterior air temperatures, and the output layer that is the predicted heat flux rate. The predicted value is trained by comparing the predicted heat flux rates with the measured values, and by rearranging the neural network parameters (weights and biases) in corresponding neurons by minimizing the mean squared error defined for trained values (measured versus predicted heat flux rates). The use of MLP shows promising results for predicting the heat flux rates for the analyzed cases (4 days HFM results) when the training is performed on 2/3 or 1/2 of the overall measurement time. The application of the MLP could be in reducing the in-situ measurement time when determining heat losses through building elements in shorter time periods.