Infrastructure Engineering - Research Publications

Permanent URI for this collection

http://hdl.handle.net/11343/347

Filters

2020 - 2022 8
Reset filters

Settings
Statistics
Citations
End date: 2022 × Type: Dataset ×

Search Results

Now showing 1 - 8 of 8
  • Item
    No Preview Available
    Dataset on effects of learning curve models on onshore wind and solar PV cost developments in the USA (Version 2)
    Castrejon Campos, O ; Aye, L ; Hui, KP ( 2022-02-21)
    This dataset includes input data to estimate learning-by-deploying (LbD) and learning-by-researching (LbR) rates for onshore wind and solar PV in the United States of America (USA). Using different learning curve approaches the simulated technological-based cost developments are also presented. Coefficient of determination (R squared) and Root Mean Square Error (RMSE) were applied for quantification of the agreement between simulated and observed technological-based costs.
  • Item
    No Preview Available
    Dataset on effects of learning curve models on onshore wind and solar PV cost developments in the USA
    Castrejon Campos, O ; Aye, L ; Hui, K ( 2021-11-03)
    This dataset includes input data to estimate learning-by-deploying (LbD) and learning-by-researching (LbR) rates for onshore wind and solar PV in the United States of America (USA). Using different learning curve approaches the simulated technological-based cost developments are also presented. Coefficient of determination (R squared) and Root Mean Square Error (RMSE) were applied for quantification of the agreement between simulated and observed technological-based costs.
  • Item
    No Preview Available
    Dataset on validation of double U-tube borehole and seasonal solar thermal energy storage system TRNSYS models
    Shah, SK ; Aye, L ; Rismanchi, B ( 2021-08-09)
    This dataset includes data from the validation of double U-tube borehole and seasonal solar thermal energy storage system TRNSYS models. The simulated transient temperatures at various points of the systems were compared with the measured ones. To quantify the agreement between each simulated and measured temperature of interest, mean bias error (MBE), root mean square error (RMSE) and correlation coefficient (CC) were applied.
  • Item
    No Preview Available
    Dataset on effects of learning curve models on clean energy technology cost developments
    Castrejón Campos, O ; Aye, L ; Hui, KF ( 2021-01-21)
    This dataset includes input data to estimate learning-by-doing (LbD) and learning-by-researching (LbR) rates for onshore wind and solar PV in the United States. Using different learning curve approaches the simulated technology cost developments are also presented. Coefficient of determination (R square) and Root Mean Square Error (RMSE) were applied for quantification of the agreement between simulated and observed technology costs.
  • Item
    No Preview Available
    Dataset on baseline performance and sensitivity analysis of a prefabricated house in six climate zones in Australia
    Naji, S ; Aye, L ; Noguchi, M ( 2020-11-26)
    This dataset includes the results of baseline performance evaluation and sensitivity analysis of a prefabricated house in six climate zones in Australia. The performance parameters investigated are monthly heating loads, monthly cooling loads, monthly thermal discomfort hours (TDHs) and monthly daylight unsatisfied hours (DUHs) of the living room, study room, and rumpus room in the prefabricated house.
  • Item
    No Preview Available
    Dataset on validation of TRNSYS building model for a prefabricated house built in Australia
    Naji, S ; Aye, L ; Noguchi, M ( 2020-11-26)
    This dataset includes data from the validation of TRNSYS building model for a prefabricated house built in Australia. The simulated indoor temperatures were compared with the measured ones in Melbourne. The comparison was carried out for the period between 19:00:00 on 31 March 2018 and 00:00:00 on 2 April 2018. Coefficient of determination (R²), Root Mean Square Error (RMSE), Mean Bias Error (MBE), Mean Absolute Error (MAE), and Correlation Coefficient (CC) were applied for quantification of the agreement between simulated and measured temperatures.
  • Item
    Thumbnail Image
    Dataset on thermal properties, sound reductions, TVOC emissions, and costs of envelope components for prefabricated buildings in Australia (Version 2)
    Naji, S ; Aye, L ; Noguchi, M ( 2020-07-18)
    The data included in the dataset are related to prefabricated building components and their specifications. The specification provided are component type, material, thickness, density, thermal conductivity, specific heat, sound reduction index, total volatile organic compounds (TVOC) emissions and costs in various locations of Australia. The components that are included in this dataset are wall cladding, wall core, interior wall lining, insulation, roof cladding, floor covers and glazing. The authors attempted to cover most of the available component types and their available thicknesses. However, the authors acknowledge that due customisability of these products, other variations of the materials and their dimensions may have not been mentioned in the dataset. For some materials the specifications related to certain properties could net be accessed. Therefore, this dataset is designed to be open for updates and further development in the future. The dataset has been used in sets of building envelope design optimisation practices as input parameters.
  • Item
    Thumbnail Image
    Dataset on thermal properties, sound reductions, TVOC emissions, and costs of envelope components for prefabricated buildings in Australia (Version 1)
    Naji, S ; Aye, L ; Noguchi, M ( 2020-04-04)
    This data article includes the common envelope components for prefabricated buildings in Australia. The thermal properties, sound reductions, total volatile organic compound emission rates and the cost data are included in this dataset. The material types and their available thicknesses were collected from commercially available standardised construction components. This data set can be used for building energy and indoor environmental quality performance evaluations. The cost data can be used for estimating the initial cost of building envelope. By further modification of data, they can also be used in Building Information Modelling (BIM) tools.