Infrastructure Engineering - Research Publications

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Type: Dataset × Start date: 2021 ×

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    Dataset - EMA protocol in action: Unlocking Mexico's clean energy potential
    Castrejon Campos, O ; Aye, L ; Hui, KP ; Vaz-Serra, P ( 2023-10-26)
    This dataset presents the outcomes of implementing the exploratory modelling and analysis (EMA) protocol for identifying robust policy mixes for clean energy transitions. The protocol, detailed in Protocol Exchange (https://protocolexchange.researchsquare.com/), is designed to explore the consequences of diverse policy alternatives and multiple uncertainties within energy transitions through computational experiments. EMA, a computational experimentation technique, plays a key role in systematically exploring the potential impacts of various policy alternatives and uncertainties within complex systems, particularly in the energy domain. This publication outlines the application of the EMA protocol in the specific case of Mexico, offering a detailed approach for researchers, policymakers, and energy analysts to explore the complex interactions between policy alternatives and uncertainties in the clean energy transition. The dataset provides insights into how different policy alternatives perform under various conditions, shedding light on their robustness and potential trade-offs. The dataset encompasses the outcomes of an open exploration and directed search processes, along with analytical sub-processes integrated to provide a comprehensive analysis. The results from implementing the EMA protocol offer a valuable resource for decision-makers and researchers seeking to navigate the complex interactions between policy alternatives and uncertainties in energy transitions.
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    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.
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    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.
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    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.
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    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.