The following articles from the last issue of Energy and Buildings are probably of interest to building simulationists and automationists.

Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption, by Alberto Hernandez Neto and Flávio Augusto Sanzovo Fiorelli.

There are several ways to attempt to model a building and its heat gains from external sources as well as internal ones in order to evaluate a proper operation, audit retrofit actions, and forecast energy consumption. Different techniques, varying from simple regression to models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be highly under or over estimated.

In this paper, a comparison is made between a simple model based on artificial neural network (ANN) and a model that is based on physical principles (EnergyPlus) as an auditing and predicting tool in order to forecast building energy consumption. The Administration Building of the University of São Paulo is used as a case study. The building energy consumption profiles are collected as well as the campus meteorological data.

Results show that both models are suitable for energy consumption forecast. Additionally, a parametric analysis is carried out for the considered building on EnergyPlus in order to evaluate the influence of several parameters such as the building profile occupation and weather data on such forecasting.

Comparison of thermal comfort algorithms in naturally ventilated office buildings, by Bassam Moujalled, Richard Cantina, and Gérard Guarracino.

With the actual environmental issues of energy savings in buildings, there are more efforts to prevent any increase in energy use associated with installing air-conditioning systems. The actual standard of thermal comfort in buildings ISO 7730 is based on static model that is acceptable in air-conditioned buildings, but unreliable for the case of naturally ventilated buildings. The different field studies have shown that occupants of naturally ventilated buildings accept and prefer a significantly wider range of temperatures compared to occupants of air-conditioned buildings. The results of these field studies have contributed to develop the adaptive approach. Adaptive comfort algorithms have been integrated in EN15251 and ASHRAE standards to take into account the adaptive approach in naturally ventilated buildings. These adaptive algorithms seem to be more efficient for naturally ventilated buildings, but need to be assessed in field studies. This paper evaluates different algorithms from both static and adaptive approach in naturally ventilated buildings across a field survey that has been conducted in France in five naturally ventilated office buildings. The paper presents the methodology guidelines, and the thermal comfort algorithms considered. The results of application of different algorithms are provided with a comparative analysis to assess the applied algorithms.

Dynamical building simulation: A low order model for thermal bridges losses, by Y. Gaoa, J.J. Rouxb, L.H. Zhaoc and Y. Jiang.

Thermal bridges losses represent an increasing part of heat losses owing to significant three-dimensional heat transfer characteristics in modern buildings, but one-dimensional models are used in most simulation software for thermal analyses to simplify the calculations.

State model reduction techniques were used to develop low-order three-dimensional heat transfer model for additional losses of thermal bridges, which is efficient and accuracy. Coupling this technique with traditional one-dimensional model for walls losses, it is possible to reduce a large amount of time simulations.

Low-order model was validated from frequency response and time-domain output. And the effect of this model was shown with its implementation in software “TRNSYS”.