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

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.

Modelling household spatial energy intensity consumption patterns for building envelopes, heating systems and temperature controls in cities

“…In CHM, the internal spaces of every dwelling in a linear block of terrace houses and tall buildings are divided into zones, and there is no difference in their usage and activity, heat gains and losses characteristics, heating systems and operational regimes. Internal layout or thermal zoning has not been considered as an uncertainty variable [28] in other domestic energy models which focus on energy performance assessment using steady-state models [29]. CHM [30] does not consider the number of thermal zones as one of the 31 most sensitive parameters in the local sensitivity study.…”

Prediction of Wave Energy Transformation Capability in Isolated Islands by Using the Monte Carlo Method

“…The essential concept is to use randomness in the experiments, for which the particular result is not acknowledged in advance [37]. The mathematical simulation model can be used in many fields of engineering such as, energy model simulation [38,39], renewable energy generation, prediction and economic analysis (Wind, Photovoltaic, Wave Energy, etc.) [40][41][42][43][44][45][46][47][48][49].…”

Engineering Modelling of Building Energy Consumption in Cities: Identifying Key Variables and Their Interactions with the Built Environment

“…pay normal prices during the day, but cheaper rates for seven hours during the night) and planned permission granted for converting houses to Houses in Multiple Occupations (HMO) and houses and flats having sharing amenities, as a number of researchers have suggested, for instance, [3] and [4]. The reason for having an increased number of data set variables is that this may lead to a better insight into the effect of uncertainties in the aggregated process [5] by generating a more complete representation of the dwelling. In the case of dwellings using the Economy 7 tariff, an increase number of variables is needed to provide a better description of the dwelling (the hot water heating system and the tank insulation are the new variables).…”