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Recently, there has been an increasing interest in the development of an approach to characterize the as-built heat loss coefficient (HLC) of buildings based on a combination of on-board monitoring (OBM) and data-driven modeling. OBM is hereby defined as the monitoring of the energy consumption and interior climate of in-use buildings via non-intrusive sensors. The main challenge faced by researchers is the identification of the required input data and the appropriate data analysis techniques to assess the HLC of specific building types, with a certain degree of accuracy and/or within a budget constraint. A wide range of characterization techniques can be imagined, going from simplified steady-state models applied to smart energy meter data, to advanced dynamic analysis models identified on full OBM data sets that are further enriched with geometric info, survey results, or on-site inspections. This paper evaluates the extent to which these techniques result in different HLC estimates. To this end, it performs a sensitivity analysis of the characterization outcome for a case study dwelling. Thirty-five unique input data packages are defined using a tree structure. Subsequently, four different data analysis methods are applied on these sets: the steady-state average, Linear Regression and Energy Signature method, and the dynamic AutoRegressive with eXogenous input model (ARX). In addition to the sensitivity analysis, the paper compares the HLC values determined via OBM characterization to the theoretically calculated value, and explores the factors contributing to the observed discrepancies. The results demonstrate that deviations up to 26.9% can occur on the characterized as-built HLC, depending on the amount of monitoring data and prior information used to establish the interior temperature of the dwelling. The approach used to represent the internal and solar heat gains also proves to have a significant influence on the HLC estimate. The impact of the selected input data is higher than that of the applied data analysis method.

The bias and uncertainty of calculated decay heat from spent nuclear fuel (SNF) are essential for code validation. Also, predicting these quantities is crucial for deriving decay heat safety margins, influencing the design and safety of facilities at the back end of the nuclear fuel cycle. This paper aims to analyze the calculated spent nuclear fuel decay heat biases, uncertainties, and correlations. The calculations are based on the Polaris and ORIGEN codes of the SCALE code system. Stochastically propagated uncertainties of inputs and nuclear data into calculated decay heats are compared. Uncertainty propagation using the former code is straightforward. In contrast, the counterpart of ORIGEN necessitated the pre-generation of perturbed nuclear cross-section libraries using TRITON, followed by coincident perturbations in the ORIGEN calculations. The decay heat uncertainties and correlations have shown that the observed validation biases are insignificant for both Polaris and ORIGEN. Also, similarities are noted between the calculated decay heat uncertainties and correlations of both codes. The fuel assembly burnup and cooling time significantly influence uncertainties and correlations, equivalently expressed in both Polaris and ORIGEN models. The analyzed decay heat data are highly correlated, particularly the fuel assemblies having either similar burnup or similar cooling time. The correlations were used in predicting the validation bias using machine learning models (ML). The predictive performance was analyzed for machine learning models weighting highly correlated benchmarks. The application of random forest models has resulted in promising variance reductions and predicted biases significantly similar to the validation ones. The machine learning results were verified using the MOCABA algorithm (a general Monte Carlo-Bayes procedure). The bias predictive performance of the Bayesian approach is examined on the same validation data. The study highlights the potential of neighborhood-based models, using correlations, in predicting the bias of spent nuclear fuel decay heat calculations and identifying influential and highly similar benchmarks.

The presented study examines the economic viability and optimization of a previously designed integrated process for producing liquid methanol. The annualized cost of the system method is applied for economic analysis. The optimization method includes a robust hybrid approach that combines the NSGA-II multi-objective optimization algorithm with artificial intelligence. Decision variables for the optimization are taken from a sensitivity analysis to optimize the exergy and energy efficiencies and the investment return period. Decision-making methodologies, including LINMAP, fuzzy, and TOPSIS, are utilized to identify the optimal outcomes, effectively identifying points along the Pareto-optimal front. Compared with the original design, the research outcomes demonstrate an over 38% reduction in the process’s investment return period post optimization, as evaluated through the TOPSIS and LINMAP methodologies. Additionally, the highest level of thermal efficiency achieved through optimization stands at 79.9%, assessed using the LINMAP and TOPSIS methods, and 79.2% using the fuzzy Bellman–Zadeh method. The process optimization in the presented research, coupled with the improved economic feasibility, mitigates energy consumption through maximizing efficiency, thereby fostering sustainable and environmentally friendly development.

Abstract The empirical environmental Kuznets curve (EKC) literature is vast but far from conclusive. Many authors have analysed the existence of an EKC for various pollutants. Others have used the EKC framework to identify country characteristics that help to explain the income–environment relationship. In this framework environmental degradation is analysed using a second or third order polynomial in income and a limited number of control variables. Some authors question whether this standard framework is appropriate. This paper proposes an alternative to study the specific characteristics of countries that have experienced economic growth and an improving environment at the same time. We estimate a binary response model and find an EKC-like relation between the probability that a country's environment improves with economic growth and per capita GDP. Our evidence further suggests that the level of environmental damage is an important explanatory variable. We also confirm the importance of an open political system. These results indicate that the binary response model could be a valuable alternative to test which country specific characteristics are associated with a negative IER.

Given the current integration processes of the world economies, the issue of economic security of the state is extremely acute. The openness of markets facilitates the free flow of resources but also threatens the rapid transfer of crisis phenomena from one economy to another. There is a need to develop an innovative methodological tool for assessing the level of economic security in the country based on the calculation of a generalized indicator that will serve as a benchmark in the formation of state economic policy in the context of ensuring its sustainable growth and countering internal and external threats. The methodological tools of the study are methods of normalization, Fishburn’s rule, multiplicative model. Assessment of economic security level in the article is carried out in the following logical sequence: formation of an extended list of indicators characterizing the economic security of the state; characteristics of indicators in terms of opportunities and risk; determining the priority of indicators in the summarizing indicator; bringing indicators to a single measurement system; calculation of the integral indicator of economic security by constructing a multiplicative model; qualitative interpretation of the integral indicator of economic security. The results of the selection of relevant factors influencing the economic security of the state revealed that the selected determinants confirm the hypothesis of financialization of all economic processes in Ukraine and the need for state regulation of the financial system in order to ensure economic security. Empirical calculations have shown that during 2008–2018, Ukraine's level of economic security can be assessed as medium or low, and its dynamics correlate with crisis processes in the financial and economic sector and exacerbation of political instability in the country. Taking into account the low level of economic security in Ukraine, the authors elaborated a set of measures to improve the state regulation of economic security within the institutional, procedural and functional elements of it, as well as substantiate the impact of the proposed innovations on the economic security of the state. The results of the research can be useful for governments in order to adequately respond to destabilizing factors in the national economy. Keywords: economic security, integral indicator, innovation, money laundering, state regulation

Abstract Grid-connected renewable energy systems are considered a viable solution for satisfying the swiftly growing demand. Nevertheless, the intermittent nature of renewable energy sources (RESs) hinders their performance and can not be efficiently utilized, rather imposes power quality and instability problem on the grid system. To alleviate this challenge, it is common practice to integrate RESs with efficient battery energy storage technologies. Lead-acid batteries were playing the leading role utilized as stationary energy storage systems. However, currently, there are other battery technologies like lithium-ion (Li-ion), which are used in stationary storage applications though there is uncertainty in its cost-effectiveness. In this paper, a state-of-the-art simulation model and techno-economic analysis of Li-ion and lead-acid batteries integrated with Photovoltaic Grid-Connected System (PVGCS) were performed with consideration of real commercial load profiles and resource data. The Hybrid Optimization Model for Electric Renewables (HOMER) was used for the study of the techno-economic analysis. Besides, the performance of these batteries is greatly affected by the rate of charge and discharge cycling effects which gradually degrades the capacity of the battery. This effect was also investigated with Matlab using a simplified equivalent circuit model by considering a typical stationary application datasheet. The techno-economic simulation output provided that the system with Li-ion battery resulted in a Levelized Cost of Energy (LCOE) of 0.32 €/kWh compared to the system with lead-acid battery with LCOE of 0.34 €/kWh. Besides, the Net Present Cost (NPC) of the system with Li-ion batteries is found to be €14399 compared to the system with the lead-acid battery resulted in an NPC of €15106. According to the result found, Li-ion batteries are techno-economically more viable than lead-acid batteries under the considered specifications and application profile.

Photovoltaic thermal (PVT) solar collectors can be an effective solution for system output improvement. Photovoltaic thermal collectors naturally have a more expensive receiver than simpler photovo ...

Housing plays an essential role in sustainable governance due to its socio-economic and environmental connection. However, the relationship between governance policies, market behavior, and socio-economic outcomes varies geographically and demographically. Therefore, segregated policies developed and implemented may fail to achieve their desired objectives because of the sensitivity of housing policies for their connection to human wellbeing. The effectiveness of housing policies in geographically connected regions is one of the areas that has received little attention in the Canadian context. The study follows a multi-step empirical method using a multiple linear regression model and a difference-in-difference approach to assessing the geographical variation of speculation and property taxes on housing markets. The study confirms that speculation taxes are not an effective tool in curbing house prices. Similarly, considering the role of property taxes in providing public services, delinking property taxes from a potential contributor to house prices would provide a better lens to develop local housing policies. Furthermore, the study also confirms that the housing market can be better assessed at a local scale, considering geographical influence in conjunction with investment trends.