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ID:
132680
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| Publication |
2014.
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| Summary/Abstract |
Surrogate models are an important part of building energy labelling programs, but these models still present low accuracy, particularly in cooling-dominated climates. The objective of this study was to evaluate the feasibility of using an artificial neural network (ANN) to improve the accuracy of surrogate models for labelling purposes. An ANN was applied to model the building stock of a city in Brazil, based on the results of extensive simulations using the high-resolution building energy simulation program EnergyPlus. Sensitivity and uncertainty analyses were carried out to evaluate the behaviour of the ANN model, and the variations in the best and worst performance for several typologies were analysed in relation to variations in the input parameters and building characteristics. The results obtained indicate that an ANN can represent the interaction between input and output data for a vast and diverse building stock. Sensitivity analysis showed that no single input parameter can be identified as the main factor responsible for the building energy performance. The uncertainty associated with several parameters plays a major role in assessing building energy performance, together with the facade area and the shell-to-floor ratio. The results of this study may have a profound impact as ANNs could be applied in the future to define regulations in many countries, with positive effects on optimizing the energy consumption.
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| 2 |
ID:
104952
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2011.
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| Summary/Abstract |
The objective of this study is to project transport energy consumption in Thailand for the next 20 years. The study develops log-linear regression models and feed-forward neural network models, using the as independent variables national gross domestic product, population and the numbers of registered vehicles. The models are based on 20-year historical data between years 1989 and 2008, and are used to project the trends in future transport energy consumption for years 2010-2030. The final log-linear models include only gross domestic product, since all independent variables are highly correlated. It was found that the projection results of this study were in the range of 54.84-59.05 million tonnes of oil equivalent, 2.5 times the 2008 consumption. The projected demand is only 61-65% of that predicted in a previous study, which used the LEAP model. This major discrepancy in transport energy demand projections suggests that projects related to this key indicator should take into account alternative projections, because these numbers greatly affect plans, policies and budget allocation for national energy management.
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