Multiple Sources Of Uncertainty Research Articles

Simple methods for improving the communication of uncertainty in species’ temporal trends

Temporal trends in species occupancy or abundance are a fundamental source of information for ecology and conservation. Model-based uncertainty in these trends is often communicated as frequentist confidence or Bayesian credible intervals, however, these are often misinterpreted in various ways, even by scientists. Research from the science of information visualisation indicates that line ensemble approaches that depict multiple outcomes compatible with a fitted model or data may be superior for the clear communication of model-based uncertainty. The discretisation of continuous probability information into frequency bins has also been shown to be useful for communicating with non-specialists. We present a simple and widely applicable approach that combines these two ideas, and which can be used to clearly communicate model-based uncertainty in species trends (or composite indicators) to stakeholders. We also show how broader ontological uncertainty can be communicated via trend plots using risk-of-bias visualisation approaches developed in other disciplines. The techniques are demonstrated using the example of long-term plant distributional change in Britain, but are applicable to any temporal data consisting of averages and associated uncertainty measures. Our approach supports calls for full transparency in the scientific process by clearly displaying the multiple sources of uncertainty that can be estimated by researchers.

Network-secure bidding strategy for aggregators under uncertainty

The widespread adoption of distributed energy resources (DER) is creating an opportunity for aggregators to transform DER flexibility into electricity market services. In a scenario of high DER integration, aggregators will need to coordinate the optimisation of DER with the distribution system operator (DSO) in order to avoid congestion and voltage incursions in the distribution networks. This coordination task is notably complex since both network and DER operation are impacted by multiple sources of uncertainty. To address these challenges, this paper proposes a new bidding strategy for aggregators of prosumers to make robust network-secure bidding decisions in day-ahead energy and reserve markets. The bidding strategy computes robust network-secure bids without jeopardising the data privacy of aggregators and the DSO. The data privacy is preserved by using the alternating direction method of multipliers (ADMM) to decompose a stochastic network-secure bidding problem into bidding and network subproblems and solve them separately and in parallel. The uncertainty of the prosumers is incorporated in the bidding problem through scenarios of load, renewable generation, and DER preferences. Our experiments show that the proposed bidding strategy computes robust bids against distribution network problems, outperforming deterministic and stochastic state-of-the-art bidding strategies in terms of cost and network observability.

Comparing district heating options under uncertainty using stochastic ordering

District heating is expected to play an important role in the decarbonisation of the energy sector in the coming years since low carbon sources such as waste heat and biomass are increasingly being used to generate heat. The design of district heating often has competing objectives: the need for inexpensive energy and meeting low carbon targets. In addition, the planning of district heating schemes is subject to multiple sources of uncertainty, such as variability in heat demand and energy prices. This paper proposes a decision support tool to analyse and compare system designs for district heating under uncertainty using stochastic ordering (dominance) so that decision-makers can make robust decisions. The uncertainty in input parameters of the energy system model together with general scenarios are introduced to generate distributions of net present costs and emissions for each design. To perform inference about the induced distributions of outputs, we apply the orderings in the mean and dispersion. The proposed approach is demonstrated in an application to the waste heat recovery problem in district heating in Brunswick, Germany. The results obtained show that heat pump, a low carbon design option, is more robust in comparison to combined heat and power (CHP) and a mix of CHP and heat pump under all three scenarios, highlighting that robustness is an attractive feature of low-temperature waste heat recovery. • Decisions regarding energy planning involve uncertainty. • New low-carbon technologies are being introduced as part of district heating. • Stochastic ordering is proposed to compare the system designs’ performance under uncertainty. • The orderings in mean and dispersion are considered for waste heat recovery.

Coupling length: a generalized gleno-acetabular distance measurement for interpreting the size and gait of quadrupedal trackmakers

The gleno-acetabular distance DGA, a conventional proxy for the size of a quadrupedal trackmaker, is often estimated as the distance GA between the midpoint between a left and right pair of pes tracks and the midpoint between a selected pair of left and right manus tracks. While frequently used to estimate trackmaker size from fossil trackways, the relationship between GA and DGA depends upon the gait (which is unknown for extinct trackmakers), and is subject to multiple additional sources of uncertainty including which specific pair of manus tracks to associate with a given pair of pes tracks. Here a generalization is introduced, termed coupling length, which does not require any presumption about trackmaker gait of the degree of overstepping. On the contrary, a systematic analysis of a trackway in terms of coupling length can permit estimation of both the size and the gait with which the trackmaker progressed. Coupling length can be computed at successive points along a trackway, allowing exploration of a range of hypothetical gaits and body sizes for the trackmaker responsible. A fitness function quantifying persistent variation in coupling length along a trackway is used to indicate whether a given trackway could have been created by a fairly consistent gait, and if so, a range of high-fitness solution gaits and their associated DGA. The method was applied to selected quasi-regular sauropod trackways and a solution found for a narrow range of gaits with limb phase of about 0.3 and DGA = 1.6 ± 0.2 m. This is the first estimation of sauropod trackmaker gait, and introduces a novel method by which irregularity along a trackway is used as a source of information to constrain inferences of trackmaker behavior. The computed DGA for this sauropod suggests significantly smaller trackmakers than conventional estimations based on track dimensions and hip height estimates. Size estimation by this approach offers greatly reduced uncertainty compared to conventional estimates.

Quantifying the effects of forecast uncertainty on the role of different battery technologies in grid-connected solar photovoltaic/wind/micro-hydro micro-grids: An optimal planning study

Designing a reliable and robust micro-grid (MG) aided by energy storage devices requires quantifying the parametric uncertainty associated with input data time-series, among other types of uncertainties – and in particular, the uncertainty in forecasted meteorological, load demand, and wholesale electricity price time-series. Given the computational complexities involved, the recent battery-storage-supported MG capacity optimisation literature fails to simultaneously characterise multiple sources of data uncertainty. This paper, therefore, seeks to address this knowledge gap by hedging against a wide array of parametric uncertainties at the same time, whilst exploring the potentially salient implications of high-dimensional uncertainty characterisation for the costing and configuration of battery-supported, grid-tied MGs. To this end, this paper introduces a novel computationally efficient, stochastic MG design optimisation model that enables the simultaneous handling of multiple uncertain inputs. Notably, the model yields in-depth, accurate, and robust infrastructure decision-making support, particularly with regard to the optimal size of battery storage, by specifically analysing the worst-case, most likely case, and best-case uncertainty characterisation scenarios. To demonstrate the effectiveness of the model within a community renewable energy project scheme, a case study of a grid-connected, battery-storage-supported MG in the town of Ohakune, New Zealand is presented. Importantly, the numerical simulation results indicate that the life-cycle cost of the MG would have been underestimated by as much as ~8% (equating to NZ$0.3 m) in the most likely case – with an associated ~15% (equating to 143 kWh) battery size underestimation – if the variability inherent in forecast inputs was not factored into the analysis. Also, the proportion of the energy storage capacity to the total renewable power generation capacity is ~71%, ~76%, and ~68% in the best-case, most likely case, and worst-case scenarios, respectively, suggesting that the risk-seeking and risk-averse MG planning strategies have a slightly contradictory effect on the storage-to-generation ratio. Comprehensive sensitivity analyses of different battery technologies with different specific power, specific energy, and depth-of-discharge specifications have also demonstrated the robustness and validity of the model, whilst additionally identifying the sodium‑sulfur (NaS) battery technology as the most profitable choice in grid-connected MG development applications – which can be primarily attributed to the fact that it provides the best trade-off between the specific power and specific energy.

Bi-objective stochastic model for the design of biofuel supply chains incorporating risk

The design of a biofuel supply chain network involves multiple uncertainty sources such as the biomass supply and quality. Most of the previous works took a risk-neutral approach by modeling this problem as two-stage stochastic formulation. However, most decision makers are not risk-neutral and a better understanding of the risk involved is germane. This study proposes a bi-objective two-stage stochastic optimization model that considers the expected total cost and the CVaR as objective functions. The first stage determines the location of biorefineries, and the second stage decides the flow between suppliers (counties) and biorefineries. We assume as non deterministic two features of switchgrass (biomass), the ash and moisture contents. To solve this problem, a hybrid method based on a Simulated Annealing algorithm and the augmented ε-constraint method is developed and tested using a realistic large-scale case study in the state of Texas. Based on the experimental results, we observe that the CVaR is not a suitable risk measure when the problem involves a third party because the cost of third-party does not imply risk for the decision makers (biorefinery owner). Therefore, the PVaR model is more suitable for the nature of this problem since PVaR includes the risk inherent to the third party in case this entity is not able to fulfill the demand of biofuel not covered by the supply chain network. As a result, we reformulate the original model by replacing the CVaR by PVaR to create a tailor-made formulation for the bi-objective two-stage stochastic programming problem including third party biofuel suppliers. Compared to existing solution procedures, the proposed hybrid method has merits in terms of computational burden and solution quality. Computational experiments show that the lower the confidence level in the third party (that is, a less reliable supplier needed to meet the bioethanol demand), the higher the expected costs and the PVaR (risk) for the supply chain design.

Development of a disaggregated multi-level factorial hydrologic data assimilation model

A disaggregated multi-level factorial hydrologic data assimilation model (FHDA) is proposed for exploring not only the direct effects from individual uncertainties but also, more importantly, the composite ones from multi-layer and multi-parameter interactions among multiple uncertainties in hydrologic data assimilation systems. Based on a disaggregated multi-level factorial analysis method, the proposed FHDA examined the contributions of multiple uncertainty sources, including data assimilation scheme, sample size, forcing data error, and observed data error. Three parameter assimilation schemes of data assimilation i.e., Ensemble Kalman Filter (EnKF), Standard Kernel Smoother (EnKFSKS), and Kernel Smoother with location shrinkage (EnKFKSLS) are tested. The results indicate that: i) the streamflow observations can be well tracked by 95% prediction intervals. ii) reducing streamflow observation error is the most efficient way to improve both deterministic and probabilistic predictions. iii) data assimilation scheme plays a vital role in hydrological predictions, especially for probabilistic ones (i.e., contributes 22% for the ensemble prediction uncertainty). Therefore, to improve the prediction accuracy, it is necessary to optimize the parameter assimilation schemes in hydrological data assimilation model.

Автоматическая маршрутизация судов во льдах: постановка задачи и инструменты решения

Development of the applied solutions to optimize ship path in dynamic ice environment is one of the urgent problems due to the ongoing growth of Arctic shipping. Ice routing makes it possible to increase the efficiency of sea transportation, reduce the risks of ship operation in ice, and minimize the negative anthropogenic impact on the atmosphere. This article describes in a concentrated form the authors’ experience in this area and presents Boreas, the developed research software application for automatic ice routing. Mathematical formulation of ice routing task is based on a universal economic criterion that allows optimizing not only a trajectory, but also the amount of icebreaker assistance, ship operation modes (astern or bow forward), and some other aspects of ice navigation. Functionality and architecture of the Boreas software allow carrying out various studies in the field of ice routing. The application supports alternative ice data sources and various speed regimes of a ship. It allows using different search algorithms (grid, wave-based, and combined) and considers numerous navigation features (predefined fairways, navigation depths, and restricted areas). As an example of using the Boreas software, we compared the route of Norilskiy Nickel containership from Dudinka to Murmansk on March 19—22, 2018 with several automatically generated routes for various speed regimes. Based on our experience and the results of this study, we can state that the development of applied solutions for Arctic routing is significantly complicated by multiple sources of uncertainty and requires further research. The article formulates a list of scientific and technical problems that need to be solved for the comprehensive understanding and further implementation of ice routing technologies in the practice of ship navigation in the Arctic.

Transportation Infrastructure Decision Flexibility in Response to Climate Change and Demand Uncertainties: The Mackenzie Valley Highway in Canada’s Northwest Territories

AbstractBarge transport operations on the Mackenzie River, a major transportation corridor in the Northwest Territories, are impacted by multiple sources of uncertainties. In particular, the impact...

Importance sampling for probabilistic prognosis of sector-wide flight separation safety

This study develops a probabilistic simulation methodology for the en-route safety assessment of multiple aircraft flying within an airspace sector. This assessment is affected by multiple sources of uncertainty, such as external inputs such as wind gust, internal factors such as aircraft component failure, and model errors (e.g., in the aircraft dynamics model) and variability in observed trajectories. The concern is to evaluate whether any pair of aircraft fail to maintain a minimum required lateral separation, and the safety assessment problem involves the prognosis of separation violations between all possible aircraft pairs within the sector, over a duration of interest. Some of the uncertainty sources are modeled as random variables and others as random processes. We combine two deterministic trajectory prediction models – the kinematics-based Generalized National Airspace Trajectory-Prediction System (GNATS), and the dynamics-based Base of Aircraft Data (BADA) – to formulate a probabilistic aircraft dynamic model which also includes model errors as additional external inputs. A computationally efficient importance sampling-based scheme is developed for this safety assessment problem involving a large number of aircraft. This approach is demonstrated using historical flight data, for aircraft operating in the Houston air traffic sector.

Environmental DNA dispersal from Atlantic salmon farms

The spatial spread of genetic material is fundamental to analyses of invasive species, species dispersal, and disease surveillance. Using a quantitative environmental DNA methodology, we assessed spatial variation in Atlantic salmon ( Salmo salar) eDNA concentration, originating from four active salmon farms, along ∼55 km of narrow channels in British Columbia, Canada. We evaluated eDNA from 36 and 47 seawater samples collected at 2 and 8 m depths, respectively, at 0.3–3 km intervals along the channels. We fitted a Laplace dispersal kernel to eDNA data separately for 2 and 8 m depths. The model estimates that 95% of eDNA spread at 2 m depth was within 1.6 km upstream and 3.2 km downstream from farms relative to a prevailing current, and this was expanded at 8 m (1.8 km upstream; 3.7 km downstream). Our modeling results were robust to multiple sources of simulated uncertainty associated with sampling regime and variable eDNA shedding rates. Our results provide a benchmark for the spatial spread of biological material such as pathogens or eDNA from invasive or imperilled species in a coastal marine context. This work has implications for the interpretation of eDNA data for species surveillance and predicting disease spread.

A clustering-based framework for searching vulnerabilities in the operation dynamics of Cyber-Physical Energy Systems

Being highly digitalized and interconnected through smart sensors in an advanced communication network, Cyber-Physical Energy Systems (CPESs) enhance the dynamic control under different operational conditions, but bring up concerns on cybersecurity failures due to deliberate cyber-attacks. In this study, we propose a clustering-based framework for searching the minimum combination of the most vulnerable communication channels (between sensors and the control center) that are susceptible to the operation dynamics of CPESs. We first adopt a mixed-integer linear programming (MILP) approach to select the extreme operational condition period clusters, taking into account the multiple sources of uncertainty influencing the system dynamic behavior. For each cluster, we inject undetectable false data vectors into different sets of communication channels, and test their effects on system functionalities by resorting to the Conditional Value-at-Risk (CVaR) measure. The minimum combination of vulnerabilities is identified, when its False Data Injection Attacks (FDIAs) start to deviate the system functionality from the normal range.We illustrate the proposed framework by application to a renewable energy system susceptible to primary renewable resources availability. Specifically, the IEEE 30-bus test system is taken as case study and considered under FDIA scenarios in weather-related extreme operation transients, when accidents can aggravate damages.

Quantifying multiple uncertainties in modelling shallow water-sediment flows: A stochastic Galerkin framework with Haar wavelet expansion and an operator-splitting approach

The interactive processes of shallow water flow, sediment transport, and morphological evolution constitute a hierarchy of multi-physical problems of significant interests in a spectrum of engineering and science areas. To date, modelling shallow water hydro-sediment-morphodynamic (SHSM) processes is subject to multiple sources of uncertainty arising from input data and incomplete understanding of the underlying physics. A stochastic SHSM model with multiple uncertainties has yet to be developed as most SHSM models still concern deterministic problems and only one has been recently extended to a stochastic setting, but is restricted to a single source of uncertainty. Here we first present a new probabilistic SHSM model incorporating multiple uncertainties within the stochastic Galerkin framework using a multidimensional tensor product of Haar wavelet expansion to capture local, nonlinear variations in joint probability distributions and an operator-splitting-based method to ensure that the modelling system remains hyperbolic. Then, we verify the proposed model via benchmark probabilistic numerical tests with joint uncertainties introduced in initial and boundary conditions, matching established experiments of flow-sediment-bed evolutions driven by a sudden dam break and by a landslide dam failure and large-scale rapid flow-sediment-bed evolution in response to flash flood. The present work facilitates a promising modelling framework for quantifying multiple uncertainties in practical shallow water hydro-sediment-morphodynamic modelling applications.

Quantifying uncertainty in dose–response screenings of nanoparticles: a Bayesian data analysis

Fitting theoretical models to experimental data for dose–response screenings of nanoparticles yields values of several hazard metrics that can support risk management. In this paper, we describe a Bayesian approach to the analysis of dose–response data for nanoparticles that takes into account multiple sources of uncertainty. Specifically, we develop a Bayesian model for the analysis of data for the cytotoxicity of ZnO nanoparticles that follow the log-logistic equation. This model reproduces the unequal variance across doses observed in the experimental data, incorporates information about the sensitivity of the cytotoxicity assay used (i.e. resazurin), and complements experimental data with historical information about the system. The model determines probability distributions for multiple values of toxicity potency (EC50), and exponential decay (the slope s); these distributions provide a direct measure of uncertainty in terms of probabilistic credibility intervals. By substituting these distributions in the log-logistic equation, we determine upper and lower limits of the benchmark dose (BMD), corresponding to upper and lower limits of credibility intervals with 95% probability given the experimental data, multiple sources of uncertainty, and historical information. In view of a reduction of costs and time of dose–response screenings, we use the Bayesian model for the cytotoxicity of ZnO nanoparticles to identify the experimental design that uses the minimum number of data while reducing uncertainty in the estimation of both fitting parameters and BMD.

Qualitative exploration of nurses' experiences of the COVID-19 pandemic using the Reconceptualized Uncertainty in Illness Theory: An interpretive descriptive study.

To explore nurses' personal, social and professional experiences in COVID-19 emergency departments and intensive care units based on the Reconceptualized Uncertainty in Illness Theory. This study used an interpretive qualitative descriptive design. Open-ended interviews were conducted between August and November 2020 with 21 nurses using interview questions based on the Reconceptualized Uncertainty in Illness Theory. Data were analysed using steps informed by hermeneutic phenomenology and reported using Consolidated Criteria for Reporting Qualitative Research. Data were grouped under five categories with 10 themes based on the Reconceptualized Uncertainty in Illness Theory: 'antecedents of uncertainty', 'appraisal of uncertainty', 'coping with uncertainty', 'probabilistic thinking' and 'formation of a new life perspective'. Nurses reported that they experienced uncertainty about being infected and infecting others with COVID-19 as well as caring for COVID-19 patients. Despite the uncertainties related to COVID-19, nurses were determined to focus on caring for COVID-19 patients by prioritizing their professional identity and responsibilities. They described feeling motivated by patients' positive feedback, which also increased their professional satisfaction. They realized that caring was therapeutic not only for their patients, but also for them. They experienced empowerment in the care team through active reuse of their prior knowledge and experience and expanded decision-making roles. They also emphasized that they perceived the COVID-19 pandemic as a unique experience for their individual and professional growth. Nurses experienced multiple sources of uncertainty during the COVID-19 pandemic. To manage uncertainty, they used strategies including focusing on patient care and satisfaction. Additionally, they made sense of their experience by acknowledging individual and professional growth. This study has a potential impact to support healthcare policymakers to meet the needs of nurses in possible future pandemics.

Risk-based flood adaptation assessment for large-scale buildings in coastal cities using cloud computing

• A risk-based flood adaptation model was developed and investigated in the cloud computing system • Both global and local sensitivity analysis were conducted to show sensitivity of model parameters • Adaptation scenarios were designed to incorporate life-cycle cost and benefits of adaptation, social vulnerability, and public adaptation policies • Public adaptation with seawall and enforced building elevation policy within flood zones could substantially reduce building damages Flood risk management (FRM) in coastal cities is a challenging task due to uncertain climate hazards under sea-level rise (SLR) and large-scale vulnerable buildings within in the floodplain. This study presents a building-level adaptation framework to evaluate alternative community adaptation strategies relying on cloud computing. We incorporated multiple sources of model uncertainties and randomly generated storm surges in each year of simulation using the extreme value distribution (GEV) theory. Based on a case study in Miami-Dade County, Florida, four adaptation scenarios were designed to evaluate their effectiveness in adaptation. Our sensitivity analysis suggested a positive linear relationship between community flood risk reduction and total community adaptation costs in the life-cycle cost-benefit (LCCB) model. Our results showed that uncertainties of the total community damage based on the LCCB model ranges from $ 221 million to $ 2.75 billion. However, when considering social vulnerability, the total community damage increased substantially, ranging from $ 244 million to $ 3.44 billion. Nevertheless, a 6ft public seawall based on the upper bound of the GEV distribution with the enforced building elevation policy in flood zones could substantially reduce community flood damage under uncertain sea-level rises.

FARMER’S INCOME RISK AND RISK MANAGEMENT BY CROSS-HEDGING: A NOTE

The purpose of this study is to provide theoretical insights into the optimal hedging strategies in farmers contracts usage. We study the hedging decisions of a risk-averse farmer. The farmer faces multiple sources of price uncertainty. Cross-hedging is plausible in that one of these two commodities has a futures market. We show that the farmer’s optimal futures market position is a fullhedge, an over-hedge, or an under-hedge, depending on whether the two random prices are strongly positively correlated, uncorrelated, or negatively correlated, respectively.

Communicating Uncertainty in Digital Humanities Visualization Research.

Due to their historical nature, humanistic data encompass multiple sources of uncertainty. While humanists are accustomed to handling such uncertainty with their established methods, they are cautious of visualizations that appear overly objective and fail to communicate this uncertainty. To design more trustworthy visualizations for humanistic research, therefore, a deeper understanding of its relation to uncertainty is needed. We systematically reviewed 126 publications from digital humanities literature that use visualization as part of their research process, and examined how uncertainty was handled and represented in their visualizations. Crossing these dimensions with the visualization type and use, we identified that uncertainty originated from multiple steps in the research process from the source artifacts to their datafication. We also noted how besides known uncertainty coping strategies, such as excluding data and evaluating its effects, humanists also embraced uncertainty as a separate dimension important to retain. By mapping how the visualizations encoded uncertainty, we identified four approaches that varied in terms of explicitness and customization. This work contributes with two empirical taxonomies of uncertainty and it's corresponding coping strategies, as well as with the foundation of a research agenda for uncertainty visualization in the digital humanities. Our findings further the synergy among humanists and visualization researchers, and ultimately contribute to the development of more trustworthy, uncertainty-aware visualizations.

Uncertainty-based weight determination for surrogate optimization

Fuel surrogates are simplified models that mimic the combustion characteristics of very complex real transportation fuels and enable a detailed description of the computational system for the targeted real fuels. Current efforts in surrogate development focus on matching multiple target properties of the real fuel using numerical optimization of species compositions. A way to solve the multi-objective optimization problem is to employ the weighted-sum approach with arbitrarily assigned weights. In this paper, we propose a novel approach to reduce such arbitrariness by incorporating physical information into the surrogate optimization process, leveraging uncertainties from experimental measurements and mixture property predictions to determine the weight of each target property. The underlying principle of this approach is assigning low weight for properties with high uncertainty since a tight emulation of that property is not necessary. We propose formulations that convert relative uncertainties of each target property into weights for multi-objective surrogate optimization, which penalize target properties with high uncertainties. The method is flexible enough to accommodate not only multiple uncertainty sources, but also users’ preferences and the sensitivity of weights to uncertainty variations can be readily adjusted. The proposed method is applied to formulate surrogate mixtures for three reference target jet fuels (Jet-A POSF-10325, JP-8 POSF-10264, JP-5 POSF-10289), which have considerably different hydrocarbon compositions and properties. The results show that the surrogates developed by the uncertainty-based weight method emulate the target properties of fuels very closely, and they are able to capture the compositional characteristics of the target fuels as well.

Risk identification of residential energy demand: the case studies of Australia, Chile, the United Kingdom and the United States

ABSTRACT A wide range of residential sector energy models have been developed in recent years to determine energy demand and CO2 emissions and to evaluate energy saving policies. However, modelling outputs are subject to significant variations due to multiple sources of uncertainty, primarily stemming from input parameters and assumptions. This study aims to assess the transferability of the Transferable Energy Model (TREM) and quantify the prediction uncertainty of residential sector energy demand until 2030 in four case study countries (Australia, Chile, United Kingdom and the United States). TREM is able to determine the future annual energy demand in the residential sector according to the area of energy use (space heating, hot water provision, cooking, electrical appliances, lighting), whilst quantifying uncertainties in the results. Significant variations (between −12% and +63%) in residential energy demand in the year 2030 with respect to 2010 were found among the case study countries, suggesting that single total energy demand estimates are associated with considerable uncertainties. This paper also presents a comprehensive database of the range of possible variations in residential energy demand related to a wide range of energy saving measures in each case study country.