Realistic Energy Research Articles

Optimal weighting strategies for maximizing contrast-to-noise ratio in photon counting CT images.

Photon counting detectors (PCDs) with energy discrimination capabilities have the potential to generate grayscale CT images with improved contrast-to-noise ratio (CNR) through optimal weighting of their spectral measurements. This study evaluates the CNR performance of grayscale CT projections and images generated from spectral measurements of PCDs using three energy-weighting strategies: pre-log weighting, post-log weighting, and material decomposition (MD) weighting. This study provides the expressions of optimal weights and maximum achievable CNR of these energy-weighting strategies, which only require the knowledge of detected bin counts and do not require information of PCD energy responses or imaging techniques. We defined and solved a generalized eigenvalue problem to obtain the maximum achievable CNR in the projection domain for low-contrast tasks using three energy-weighting strategies: pre-log weighting (weighted sum of energy bin counts), post-log weighting (weighted sum of line integrals), and MD weighting (weighted sum of basis material thicknesses, which is equivalent to virtual monoenergetic images [VMIs]). These expressions only contain energy bin counts from PCD measurements. We used a realistic PCD energy response model to simulate the detected bin counts and conducted Monte Carlo simulations of different contrast tasks and phantoms to evaluate the projection- and image-domain CNR performance of these energy-weighting strategies. Additionally, the total counts method (a special case of pre-log weighting with unity weights) was included for comparison. We also conducted Gammex head and body phantom scans on an edge-on-irradiated silicon PCCT prototype to evaluate the image-domain CNR performance of these energy-weighting strategies. The results show that pre-log, post-log, and MD weighting strategies generate approximately equal projection-domain maximum achievable CNR, with a difference of less than 2%, and outperform the total counts method. These three energy-weighting strategies also generate approximately equal image-domain maximum CNR when the contrast task is located at the center of a homogeneous phantom. Pre-log weighting generates the highest image-domain CNR for an off-center contrast task location or inhomogeneous phantoms while also outperforming the total counts method. We derived the expression of projection-domain maximum achievable CNR using three energy-weighting strategies. Our results suggest that using pre-log weighting strategies enables fast grayscale CT image generation with high CNR from spectral PCD measurements for inhomogeneous phantoms and off-center region of interests (ROIs).

EOS: Impact Evaluation of Electric Vehicle Adoption on Peak Load Shaving Using Agent-Based Modeling

The increasing adoption of electric vehicles (EVs) by the general population creates an opportunity to deploy the energy storage capability of EVs for performing peak energy shaving in their households and ultimately in their neighborhood grid during surging demand. However, the impact of the adoption rate in a neighborhood might be counterbalanced by the energy demand of EVs during off-peak hours. Therefore, achieving optimal peak energy shaving is a product of a sensitive balancing process that depends on the EV adoption rate. In this paper, we propose EOS, an agent-based simulation model, to represent independent household energy usage and estimate the real-time neighborhood energy consumption and peak shaving energy amount of a neighborhood. This study uses Residential Energy Consumption Survey (RECS) and the American Time Use Survey (ATUS) data to model realistic real-time household energy use. We evaluate the impact of the EV adoption rates of a neighborhood on performing energy peak shaving during sudden energy surges. Our findings reveal these trade-offs and, specifically, a reduction of up to 30% of the peak neighborhood energy usage for the optimal neighborhood EV adoption rate in a 1089 household neighborhood.

Short fatigue crack growth sensitivity to thermo-mechanical fatigue loading

Most high-temperature components are subject to out-of-phase thermomechanical fatigue (OP-TMF), which induces crack growth at low temperatures. However, OP-TMF has been little studied in the context of short cracks. This study focuses on the experimental sensitivity of OP-TMF loading conditions playing on temperature range, gradient, and dwell time for thin sheet superalloy specimens. The material of interest is a Co-based superalloy, HA188. It is widely used in combustion chambers.The experimental analysis is based on full-field measurements for temperature, strain and damage by infrared thermography, digital image correlation and high resolution images from 300 to 900°C. The main conclusion is that the temperature gradient, together with the temperature amplitude, largely determines the strain amplitude and subsequent fatigue crack growth rate (FCGR) of short cracks. In situ measurements of damage and crack closure were obtained using supervised machine learning based on images. This clarifies that crack closure is only partial and that the crack network growth rate is consistent with the individual short crack growth rate. Finally, 3D finite element analysis considering realistic temperature field and strain energy based FCGR model was able to evaluate the fatigue life in this context. It is shown that the OP-TMF FCGR is very close to the FCGR of the maximum temperature of the TMF cycle due to partial crack closure.

Optimizing Drone Delivery Paths from Shared Bases: A Location-Routing Problem with Realistic Energy Constraints

Recently, Amazon patented fulfillment centers for drones on a large scale in densely populated areas. A network of such shared centers can be used for landing and launching drones as an alternative to the traditional private bases in near future. This paper studies how a user of such a shared delivery infrastructure can optimally determine the required bases among the existing bases to perform her delivery tasks. To this end, a location-routing problem is studied where the problem determines the drone launching centers and their routes to deliver parcels. A realistic energy function is used that incorporates the effect of load weight for calculating energy consumption in all flight phases including take-off, level flight, hovering, and landing. Although the energy consumption is nonlinear, the problem is formulated as a mixed-integer linear programming model and strengthened by valid inequalities and a pre-processing algorithm that enables us to solve instances with up to 100 customers using off-the-shelf optimization solvers. Moreover, a heuristic method is presented to solve instances with up to 200 customers. Results indicate the importance of incorporating the load-dependent energy formula in contrast to using fixed flight duration constraints for drones. The value of allowing multiple visits on each trip versus only simple back-and-forth trips is also assessed. The advantage of using an integrated location-routing approach is shown over the sequential approach in which the locations of bases are decided first and the routes are constructed next. Moreover, to manage future demand uncertainty, the model is extended for the case that a number of demand scenarios are given.

Multi-wavelength spectroscopic analysis of the ULX Holmberg II X-1 and its nebula suggests the presence of a heavy black hole accreting from a B-type donor

Ultra-luminous X-ray sources (ULXs) are high-mass X-ray binaries with an X-ray luminosity above $10^ $. These ULXs can be powered by black holes that are more massive than $20M_ accreting in a standard regime, or lighter compact objects accreting supercritically. There are only a few ULXs with known optical or ultraviolet (UV) counterparts, and their nature is debated. Determining whether optical/UV radiation is produced by the donor star or by the accretion disc is crucial for understanding ULX physics and testing massive binary evolution. We conduct, for the first time, a fully consistent multi-wavelength spectral analysis of a ULX and its circumstellar nebula. We aim to establish the donor star type and test the presence of strong disc winds in the prototypical ULX Holmberg\,II X-1 ( Furthermore, we aim to obtain a realistic spectral energy distribution of the ionising source, which is needed for robust nebula analysis. We acquired new UV spectra of with the Hubble Space Telescope ( ) and complemented them with archival optical and X-ray data. We explored the spectral energy distribution of the source and analysed the spectra using the stellar atmosphere code and the photoionisation code Our analysis of the X-ray, UV, and optical spectra of and its nebula consistently explains the observations. We do not find traces of disc wind signatures in the UV and the optical, rejecting previous claims of the ULX being a supercritical accretor. The optical/UV counterpart of is explained by a B-type supergiant donor star. Thus, the observations are fully compatible with being a close binary consisting of an $ 66\,M_ black hole accreting matter from an $ 22\,M_ B-supergiant companion. Furthermore, we propose a possible evolution scenario for the system, suggesting that is a potential gravitational wave source progenitor.

Drive Cycle‐Based Estimation of Energy Consumption for Electric Two‐Wheeler

ABSTRACTThe transportation sector is the backbone of the economic growth of any country. However, the heavy dependence of this sector on petroleum fuel is a matter of concern for sustainable development. To address this issue countries are working toward green energy‐based transportation, and among all viable solutions electric vehicles (EVs) are emerging as front runners. Range anxiety is one of the most prominent concerns in EV adoption. The range of a vehicle depends on the energy consumption so it becomes crucial to estimate it very precisely. There are many standard drive cycles such as the New European Driving Cycle (NEDC) and Worldwide harmonized Light‐duty Vehicle Test Cycles (WLTC) which are used for the estimation of energy consumption. However, these standard cycles fail to capture the driving behavior of real traffic. Due to this reason, these standard cycles underestimate the energy consumption compared with actual consumption. For more realistic energy requirement estimations, researchers are focusing on the development of real‐world drive cycles specific to a particular geography. In this paper, a real‐world drive cycle of electric two‐wheeler has been developed for the city of Lucknow, India, and compared with the driving characteristics and energy consumption estimates of WLTC. The energy requirement per km for the Lucknow drive cycle and WLTC are found as 14.89 Wh/km and 11.95 Wh/km, respectively, which indicates per km energy required estimation for LDC is 24.60% higher than WLTC.

Tier-specific energy benchmarking for disparate schools in developing South Africa

The increasing impacts of human-induced climate change in developing countries have spurred government policies, activism, and sustainability research aimed at reducing energy consumption. Understanding the electricity usage of buildings is crucial to cutting carbon emissions and achieving cost savings. This study addresses the challenge of establishing realistic and relevant energy benchmarks for educational institutions in developing countries, specifically focusing on the Western Cape, South Africa. Schools in this region exhibit significant differences in energy intensities but are currently assessed using the same reference standard. A top-down analysis was performed using descriptive statistics to develop energy performance benchmarks tailored to unique patterns of energy consumption in schools. Data from 31 less affluent schools were collected using smart meters to ensure accuracy. The proposed reference benchmarks, ranging from 12 to 37kWh/m2 per year, are significantly lower than the existing 60kWh/m2 per year benchmark, demonstrating substantial potential for energy savings. This nuanced benchmarking approach accounts for seasonal and term variations in energy usage, providing a more accurate comparison across schools. The research introduces a novel, context-sensitive benchmarking method that extends beyond existing standards by incorporating these variations. It underscores the importance of localized benchmarks for achieving school energy efficiency, contributing to environmental preservation and financial savings. The proposed benchmarks offer a robust framework for policymakers, standard bureaus, and education departments to craft energy efficiency policies that drive progress in the education sector. By addressing the unique energy usage patterns of schools, this approach facilitates targeted interventions, leading to improved energy management and sustainability.

The Movement of Efimov States in 2n Halo Nucleus: probing the roles of mass asymmetry and short range of interaction

This brief communication presents the results of our work to study the movement of Efimov states in 2-neutron halo nuclei. We discuss the emergence and evolution of Efimov states in to resonances beyond the two-body (n-Core) breakup threshold and also the movement of the Efimov state in a 2-neutron halo nucleus to the region of the so called, Thomas Collapse. We especially, probe the roles of mass asymmetry of the three-body system (n-n-core) and the short range of the n-core interaction in the movement of the Efimov states. The analysis has been carried out within the framework of a three-body model, assuming two-body, non-local, separable potentials for the binary sub-systems, namely, the n-n and n-core systems. The primary motivation has been to scan the parameter space of the range parameter and determine the value(s) for which the system can support the Efimov states. The analysis shows that the value of the range parameter $\beta$ = 5.2$\alpha$ ( $\alpha$ being the energy parameter given in terms of the deuteron binding energy ) produces the realistic three-body binding energy for $^{20}$C and also generates the excited Efimov states. However, as the value of $\beta$ is increased corresponding to decrease in the range of the two-body (n-core) interaction the ground and excited state binding energies increase drastically. In other words, as the range of the two-body interaction tends to zero the system enters the Thomas Collapse region away from the Efimov region.

Social tipping dynamics in the energy system

Abstract. This paper reviews evidence on how the fast growth in renewable energy technologies can trigger social tipping dynamics that potentially accelerate a system-wide energy transition. It does so by reviewing a variety of literature across several disciplines addressing socio-technical dimensions of energy transitions. The tipping dynamics in wind and solar power create potential for cascading effects to energy demand sectors, including household energy demand. These most likely start with shift actions and adoption of household-scale batteries and heat pumps. Key enablers are strong regulations incentivising reductions in demand and setting minimum efficiency levels for buildings and appliances. While there is evidence of spillovers to more environmentally friendly behaviour, the extent of these and the key leverage points to bring them about present a knowledge gap. Moreover, these behavioural feedback loops require strong additional policy support to “make them stick”. Understanding the economic and social tipping dynamics in a system can empower decision-makers, fostering realistic energy transition policies. This paper highlights energy communities as a promising niche for leveraging tipping dynamics. Ultimately, bridging the gap between these tipping dynamics and institutional reforms is crucial for unlocking the full potential of sustainable energy systems.

Optimization and Evaluation of the Weather Research and Forecasting (WRF) Model for Wind Energy Resource Assessment and Mapping in Iran

This study aims to optimize the Weather Research and Forecasting (WRF) model regarding the choice of the best planetary boundary layer (PBL) physical scheme and to evaluate the model’s performance for wind energy assessment and mapping over the Iranian territory. In this initiative, five PBL and surface layer parameterization schemes were tested, and their performance was evaluated via comparison with observational wind data. The study used two-way nesting domains with spatial resolutions of 15 km and 5 km to represent atmospheric circulation patterns affecting the study area. Additionally, a seventeen-year simulation (2004–2020) was conducted, producing wind datasets for the entire Iranian territory. The accuracy of the WRF model was assessed by comparing its results with observations from multiple sites and with the high-resolution Global Wind Atlas. Statistical parameters and wind power density were calculated from the simulated data and compared with observations to evaluate wind energy potential at specific sites. The model’s performance was sensitive to the horizontal resolution of the terrain data, with weaker simulations for wind speeds below 3 m/s and above 10 m/s. The results confirm that the WRF model provides reliable wind speed data for realistic wind energy assessment studies in Iran. The model-generated wind resource map identifies areas with high wind (wind speed > 5.6 m/s) potential that are currently without wind farms or Aeolic parks for exploitation of the wind energy potential. The Sistan Basin in eastern Iran was identified as the area with the highest wind power density, while areas west of the Zagros Mountains and in southwest Iran showed high aeolian potential during summer. A novelty of this research is the application of the WRF model in an area characterized by high topographical complexities and specific geographical features. The results provide practical solutions and valuable insights for industry stakeholders, facilitating informed decision making, reducing uncertainties, and promoting the effective utilization of wind energy resources in the region.

Combined Supercritical CO2 Brayton Cycle and Organic Rankine Cycle for Exhaust Heat Recovery

Abstract In order to reduce energy consumption and related CO2 emissions, waste heat recovery is considered a viable opportunity in several economic sectors, with a focus on industry and transportation. Among different proposed technologies, thermodynamic cycles using suitable organic working fluids seem to be promising options, and the possibility of combining two different cycles improves the final recovered energy. In this paper, a combination of Brayton and Rankine cycles is proposed: the upper cycle has supercritical carbon dioxide (sCO2) as its working fluid, while the bottomed Rankine section is realized by an organic fluid (organic Rankine cycle (ORC)). This combined unit is applied to recover the exhaust energy from the flue gases of an internal combustion engine (ICE) for the transportation sector. The sCO2 Brayton cycle is directly facing the exhaust gases, and it should dispose of a certain amount of energy at lower pressure, which can be further recovered by the ORC unit. A specific mathematical model has been developed, which uses experimental engine data to estimate a realistic final recoverable energy. The model is able to evaluate the performance of each recovery subsection, highlighting interactions and possible trade-offs between them. Hence, the combined system can be optimized from a global point of view, identifying the most influential operating parameters and also considering a regeneration stage in the ORC unit.

Numerical energy evaluation of an optimized hybrid energy harvesting system (CPV-TEG) considering the effects of solar tracking

Currently, the energy demand at a global level is accelerated, and the use of non-renewable energies increases the problems related to global warming. Solar technologies, mainly photovoltaics, have been used as an alternative, but conversion efficiency is still low. Hybrid generation systems, which integrate photovoltaic and thermoelectric technologies, seek to provide a solution to efficiency and high costs per area unit. These systems need high tracking precision to concentrate solar energy, which requires robotic tracking systems, which generate energy waste due to the tracking action. The studies and developments do not contemplate in their analysis the losses due to tracking or the precision required by the optical concentration element. The present research develops a novel methodology for the design of optimized hybrid energy harvesting systems, integrating the concentrated photovoltaic and thermoelectric generator technologies and including the effects of solar tracking in the energy sense. In addition, a process for the numerical evaluation of hybrid systems is presented, considering thermal and tracking simulations to estimate the global energy balance. Consequently, the design methodology and numerical evaluation are implemented, comparing three concentration configurations. Additionally, a focus control device is designed for a high concentration index and evaluated. The mean total electrical efficiency of the hybrid system is 40.6763%, 38.84%, 13.3439%, and 35.0484%, respectively. With global gain estimates of 189.48 Wh, 201.83 Wh, 47.43 Wh, and 141.91 Wh. Finally, the results are compared with other developments, concluding that the hybrid system has high energy production. In conclusion, the proposal increases the overall performance of hybrid energy harvesting systems, allowing the development of future research regarding novel concentration devices, cooling methods, harvesting systems, and absorbing designs, among other innovations in more realistic energy evaluation terms.

Identification of energy management configuration concepts from a set of pareto-optimal solutions

Implementing resource efficient energy management systems in facilities and buildings becomes increasingly important in the transformation to a sustainable society. However, selecting a suitable configuration based on multiple, typically conflicting objectives, such as cost, robustness with respect to uncertainty of grid operation, or renewable energy utilization, is a difficult multi-criteria decision making problem. The recently developed concept identification technique can facilitate a decision maker by sorting configuration options into semantically meaningful groups (concepts).In this process, the partitioning of the objectives and design parameters into different sets (called description spaces) is a very important step. In this study we focus on utilizing the concept identification technique for finding relevant and viable energy management configurations from a very large data set of Pareto-optimal solutions. The data set consists of 20,000 realistic Pareto-optimal building energy management configurations generated by a many-objective evolutionary optimization of a high quality Digital Twin energy management simulator. We analyze how the choice of description spaces, i.e., the partitioning of the objectives and parameters, impacts the type of information that can be extracted. We show that the decision maker can introduce constraints and biases into that process to meet expectations and preferences. The iterative approach presented in this work allows for the generation of valuable insights into trade-offs between specific objectives, and constitutes a powerful and flexible tool to support the decision making process when designing large and complex energy management systems.

Comparison of externalities of biogas and photovoltaic solar energy for energy planning

The Brazilian electricity matrix is around 80% renewable, and it has registered continuous growth in the expansion of biogas and photovoltaic (PV) systems. The main current Brazilian incentive program for the distributed generation of renewable energy, through a single net metering tariff, that is, a generation compensation system in credits to the consumer who owns the generating unit, does not seem to be effective to promote the expected growth of the biogas. Thus, the objective of this paper was to evaluate quantitatively and qualitatively a set of significant externalities for both sources to contribute to a more realistic energy planning. The externalities quantitatively evaluated for these two renewable sources were: avoided emissions, employment and income, biofertilizers for biogas and use as a constructive element for PV systems. A significant result was that the economic value of the externalities of biogas can exceed those of PV systems for the rural producer by more than 11 times, for society by 1.7 times. It is concluded that, due to the value of biogas externalities being much higher economically than those of PV systems, Brazilian public policies for energy planning should prioritize biogas over solar photovoltaic energy systems.

Realistic operation and strategic energy, economic and environmental analyses of a hybrid renewable energy based - micro combined heat and power

The expansion of emerging energy sources that are sustainable and cleaner has opened the chance for CHP systems to be integrated for higher efficiency and performance. Prior to the operation in a real system, the integrated micro-CHP system needs a comprehensive performance analysis. Combining with a realistic energy management, the strategic operation must be dealing with the complexity of the system components at the optimal energy serving cost and keep it environmentally friendly. This study develops and analyses the m-CHP system that integrates renewable energy sources coupled with a realistic load-based management. The components control and system management are developed through a validation of the actual data for each component and integrating them in a simulation environment using LABVIEW to ensure the system works as intended. The comprehensive assessments are conducted in terms of energy, economic and environmental aspects to evaluate the feasibility of implementing the system in the long-term practices. In addition, a critical comparison of the proposed method with the existing projects locally in Malaysia and globally is presented to illustrate the potential of the m-CHP for public and private buildings.During 25 years of the operation, the proposed m-CHP system achieved a primary energy savings ratio of 44.7 %, with a net present value of USD 17,790.42. For electrical and thermal loads, the proposed m-CHP could reduce carbon dioxide emissions by 53.1 % and 73.5 %, respectively.

Interfacial charge transfer complexes between ZnO and benzene derivatives: Characterization and photocatalytic hydrogen production

The interfacial charge transfer (ICT) complex formation is a simple procedure to bring optical absorption of wide-bandgap oxide materials in the visible spectral range, crucial for enhancing their use in photo-driven reactions. The optical absorption of the prepared ICT complexes between ZnO and five different colorless benzene derivatives is red-shifted compared to pristine ZnO nanopowder. The density functional theory (DFT) calculations provided realistic energy level alignment in hybrid systems. Also, the DFT-calculated infrared spectra support the binding structures derived based on experimental measurements of free and adsorbed ligands onto ZnO surfaces. The photocatalytic performance of prepared hybrids was evaluated using photocatalytic hydrogen generation in the water-splitting reaction. The ZnO nanopowders modified with catechol and caffeic acid have over 50% higher hydrogen production rate than pristine ZnO, displaying steady hydrogen production under long-run working conditions.

SparseRNAfolD: optimized sparse RNA pseudoknot-free folding with dangle consideration

MotivationComputational RNA secondary structure prediction by free energy minimization is indispensable for analyzing structural RNAs and their interactions. These methods find the structure with the minimum free energy (MFE) among exponentially many possible structures and have a restrictive time and space complexity (O(n3)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^3)$$\\end{document} time and O(n2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^2)$$\\end{document} space for pseudoknot-free structures) for longer RNA sequences. Furthermore, accurate free energy calculations, including dangle contributions can be difficult and costly to implement, particularly when optimizing for time and space requirements.ResultsHere we introduce a fast and efficient sparsified MFE pseudoknot-free structure prediction algorithm, SparseRNAFolD, that utilizes an accurate energy model that accounts for dangle contributions. While the sparsification technique was previously employed to improve the time and space complexity of a pseudoknot-free structure prediction method with a realistic energy model, SparseMFEFold, it was not extended to include dangle contributions due to the complexity of computation. This may come at the cost of prediction accuracy. In this work, we compare three different sparsified implementations for dangle contributions and provide pros and cons of each method. As well, we compare our algorithm to LinearFold, a linear time and space algorithm, where we find that in practice, SparseRNAFolD has lower memory consumption across all lengths of sequence and a faster time for lengths up to 1000 bases.ConclusionOur SparseRNAFolD algorithm is an MFE-based algorithm that guarantees optimality of result and employs the most general energy model, including dangle contributions. We provide a basis for applying dangles to sparsified recursion in a pseudoknot-free model that has the potential to be extended to pseudoknots.

Reconstructing axion-like particles from beam dumps with simulation-based inference

Axion-like particles (ALPs) that decay into photon pairs pose a challenge for experiments that rely on the construction of a decay vertex in order to search for long-lived particles. This is particularly true for beam-dump experiments, where the distance between the unknown decay position and the calorimeter can be very large. In this work we use machine learning to explore the possibility to reconstruct the ALP properties, in particular its mass and lifetime, from such inaccurate observations. We use a simulation-based inference approach based on conditional invertible neural networks to reconstruct the posterior probability of the ALP parameters for a given set of events. We find that for realistic angular and energy resolution, such a neural network significantly outperforms parameter reconstruction from conventional high-level variables while at the same time providing reliable uncertainty estimates. Moreover, the neural network can quickly be re-trained for different detector properties, making it an ideal framework for optimizing experimental design.

Home electricity data generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles

In this paper, we present the Home Electricity Data Generator (HEDGE), an open-access tool for the random generation of realistic residential energy data. HEDGE generates realistic daily profiles of residential PV generation, household electric loads, and electric vehicle consumption and at-home availability, based on real-life UK datasets. The lack of usable data is a major hurdle for research on residential distributed energy resources characterisation and coordination, especially when using data-driven methods such as machine learning-based forecasting and reinforcement learning-based control. We fill this gap with the open-access HEDGE tool which generates data sequences of energy data for several days in a way that is consistent for single homes, both in terms of profile magnitude and behavioural clusters.•From raw datasets, pre-processing steps are conducted, including filling in incomplete data sequences, and clustering profiles into behaviour clusters. Transitions between successive behaviour clusters and profiles magnitudes are characterised.•Generative adversarial networks (GANs) are then trained to generate realistic synthetic data representative of each behaviour groups consistent with real-life behavioural and physical patterns.•Using the characterisation of behaviour cluster and profile magnitude transitions, and the GAN-based profiles generator, a Markov chain mechanism can generate realistic energy data for successive days.

A Novel Approach to Determine Multi-Tiered Nearly Zero-Energy Performance Benchmarks Using Probabilistic Reference Buildings and Risk Analysis Approaches

The Energy Performance of Buildings Directive (EPBD) mandates European Union Member States (MS) to conduct cost-optimal studies using the national calculation methodology (NCM), typically through non-calibrated asset-rating software. Nearly zero-energy building (NZEB) levels must be derived for each chosen Reference Building (RB), which are generally defined using deterministic parameters. Previous research proposed an innovative cost-optimal method that replaces ‘non-calibrated deterministic RBs’ with ‘probabilistically Bayesian calibrated reference building (RB)’ to better handle building stock uncertainties and diversities when deriving benchmarks. This paper aims to develop a framework to address two research gaps necessary for the successful application of the innovative cost optimal method: (1) providing objective criteria for defining NZEB benchmarks and (2) propagating uncertainties and financial risk for each defined benchmark. A robust approach for defining NZEB benchmarks according to four different ambition levels (low, medium, high, and highest) was developed by objectively considering distinct points from multiple cost-optimal plots employing different financial perspectives. Risk analysis is then performed for each defined benchmark by propagating risk from the posterior calibration parameter distributions to visualize and statistically quantify the financial risk, including robust risk, that the private investor could face for reaching each derived benchmark ambition level. The innovative cost-optimal methodology that incorporates the developed framework was applied to a hotel RB case study. The results showed that the developed framework is capable of deriving distinct benchmarks and quantitatively uncovering the full financial risk levels for the four different renovation ambition levels. The current cost-optimal method was also performed for the hotel case study with the RB defined determinitically and using the non-calibrated NCM software, SBEM-mt v4.2c. It was found that the financial feasibility and energy-saving results per benchmark are significantly more realistic and transparent for the proposed innovative cost-optimal method including a better match between the simulated and metered energy consumption with a difference of less than 1% in annual performance. Thus, the performance gap between calculated and actual energy performance that is synonymous with the EPBD methodology, as reported in the literature, is bridged. The case study also showed the importance of the risk analysis. Performing the cost-optimal analysis for a Bayesian calibrated RB using the mean value of the posterior calibrated parameter distributions without propagating uncertainty produced highly optimistic results that obscured the real financial risk for achieving the higher ambition levels of the NZEB benchmarks. Consequently, the developed framework demonstrated a time-bound tightening approach to achieve higher energy performance ambitions, improve risk transparency to private investors, and facilitate more targeted policies towards a net zero-carbon status. Thus, the proposed method considering parameter uncertainty and calibrated RBs is instrumental for devising robust policy measures for the EPBD, to achieve a realistic and long-lasting sustainable energy goal for European buildings.