Next Article in Journal
Investigation of Heat Pump Operation Strategies with Thermal Storage in Heating Conditions
Previous Article in Journal
Optimizing the Structure of the Straight Cone Nozzle and the Parameters of Borehole Hydraulic Mining for Huadian Oil Shale Based on Experimental Research
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Energies 2017, 10(12), 1986; https://doi.org/10.3390/en10121986

Using a Local Framework Combining Principal Component Regression and Monte Carlo Simulation for Uncertainty and Sensitivity Analysis of a Domestic Energy Model in Sub-City Areas

1
School of Architecture Planning & Landscape, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
2
Escuela Superior Politécnica del Litoral, Facultad de Ingeniería en Electricidad y Computación, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador
3
School of Civil Engineering & Geosciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
*
Author to whom correspondence should be addressed.
Received: 26 September 2017 / Revised: 19 October 2017 / Accepted: 28 October 2017 / Published: 1 December 2017
Full-Text   |   PDF [7792 KB, uploaded 1 December 2017]   |  

Abstract

Domestic energy modelling is complex, in terms of user input and the approach used to define the model; therefore, there is an increase in the sources of uncertainties. Previous efforts to perform sensitivity and uncertainty analyses have focused on national energy models, while in this research, the objective is to extend traditional sensitivity analysis and use a local framework combining principal component regression and Monte Carlo Simulation. Therefore, in our method the total amount of the energy output’s variance is decomposed, in relative terms, according to the contribution of the different predictor parameters. Our framework provides compelling evidence that local area characteristics are important in energy modelling and those national and regional indexes and values may not properly reflect the local conditions, resulting in programmes and interventions that will be sub-optimal. Furthermore, our uncertainty methodology uses a three dimensional integrative taxonomy and a concept map. The concept map identified concrete terminal causes of uncertainty within the taxonomic framework of sources, issues, sub-issues and a further abstraction of those quantities in terms of accuracy and precision. Understanding uncertainties in this way provides a possible framework for modellers, policy makers and data collectors to improve practice in key areas and to reduce uncertainty. View Full-Text
Keywords: concept map; cities; Monte Carlo Simulation; neighbourhood urban energy modelling; principal component regression; sensitivity analysis; uncertainty taxonomy concept map; cities; Monte Carlo Simulation; neighbourhood urban energy modelling; principal component regression; sensitivity analysis; uncertainty taxonomy
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Urquizo, J.; Calderón, C.; James, P. Using a Local Framework Combining Principal Component Regression and Monte Carlo Simulation for Uncertainty and Sensitivity Analysis of a Domestic Energy Model in Sub-City Areas. Energies 2017, 10, 1986.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top