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Air conditioning systems can play a positive or negative role in the spread of COVID-19 infection. The importance of sufficient outdoor air changes in buildings was highlighted by the World Health Organization, therefore these should be guaranteed by mechanical ventilation systems or adequate air conditioning systems. The proposed case study concerns the optimal number of outdoor air changes to limit COVID-19 contagion for a school building in Central Italy. The Wells–Riley model is used to assess the risk of airborne infection, while energy consumption is calculated by a dynamic energy simulation software. The scope of the paper offers an innovative method to define the optimal ventilation strategy for the building's HVAC system design to reduce the risk of infection with limited increases in energy consumption and greenhouse gas emissions. Results show that the desirable approach is the one in which the same low value of contagion risk is set in all rooms. This new approach results in significant energy savings, compared to the most common ones (setting the same high outdoor air rates for all rooms) to counteract the risk of infection. Finally, the zero-emission building target is verified by introducing a suitable photovoltaic system to offset pollutant emissions. [ABSTRACT FROM AUTHOR]

With the renewed focus on indoor air quality (IAQ) due to "Sick building syndrome" and the recent COVID-19 pandemic, the availability of innovative components and innovative guidance for maintenance and systemic safety design will play an important role, with HVAC systems as protagonists. UV-C irradiation has been investigated for a long time, and some system solutions are known. The aim of this work is to provide an overview of the latest outcomes related to the innovative components of HVAC systems using UV-C irradiation and investigate the current state of the art. A procedure based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was adopted, and the Scopus database was used to query the relevant literature. A total of 66 publications qualified for inclusion in the survey: 29 articles report experimental investigations, 24 articles are related to numerical or theoretical analysis, and both approaches were used in 13 articles. Many papers deal with upper-room UVGI, AHUs, and ducts. A few papers analyse mobile devices. The evaluation of the dose, as in the case of the definition of irradiance, is reported in a small number of articles. This lack of information makes the scenario imprecise and non-quantitative. [ABSTRACT FROM AUTHOR]

The impact of the global COVID-19 pandemic has been devastating in many countries, increasing household energy poverty. Lockdown measures have brought the EU economies into recession phases and forced people to stay confined to their homes, aggravating these issues. From the second half of 2021, when the worst seemed behind us, a new threat has appeared threatening economic recovery: the inflationary process in energy prices. This paper aims to verify the effects on energy poverty in European countries following the economic crisis generated by COVID-19 and the current inflationary scenario due to the increase in energy commodity prices through dynamic factor models, estimating the time it will take for energy poverty to return to levels before the shocks that occurred over the past two years. The outcomes show that the overall rise in energy prices (in particular gas) that unexpectedly affected European countries modifies the forecast scenarios, delaying, at best, the first improvements, initially expected as early as 2021, until after 2022. [ABSTRACT FROM AUTHOR]

Although the severity of the COVID-19 pandemic has appears to have subsided in most parts of the world, nevertheless, in addition to six million deaths, it has yielded unprecedented challenges in the economy, energy, education, urban services, and healthcare sectors. Meanwhile, based on some reports, smart solutions and technologies have had significant success in achieving pandemic-resilient cities. This paper reviews smart city initiatives and contributions to the prevention and treatment of coronavirus disease, as well as reducing its destructive impact, leading towards pandemic-resilient economic and health systems. Furthermore, the situational awareness contributions are reviewed in pandemic-resilient governance. The main contribution of this study is to describe the construction of social capital in smart cities as a facilitator in creating a pandemic-resilient society in crisis through two analyses. Moreover, this research describes smart cities' energy as interconnection of energy hubs (EHs) that leads to a high level of resiliency in dealing with the main challenges of the electricity industry during the pandemic. Energy-hub-based smart cities can contribute to designing pandemic-resilient energy infrastructure, which can significantly affect resilience in economic and health infrastructure. In brief, this paper describes a smart city as a pandemic-resilient city in the economic, energy, and health infrastructural, social, and governmental areas. [ABSTRACT FROM AUTHOR]

Education for sustainable development has among its pillars, capacity building, which equips future generations with the set of skills needed to face the challenge of the transformation of society for sustainable development. This paper presents a training course for a novel model of long-term energy planning (the ModUlar energy system Simulation Environment, MUSE), as an example of capacity building activities for sustainable development. The activities were part of the Joint Summer School on Modelling Tools for Sustainable development, held in Trieste (Italy) in 2022. This summer school was one of the first successful implementations of education and training courses in a super-hybrid mode in the post-COVID era. Describing the training activities for MUSE open-source, this paper addresses one of the challenges that education for sustainable development is expected to increasingly face in the future: the training of future professionals in the use of novel toolkits and the implementation of truly trans-disciplinary approaches.This paper discusses the pre-school online training course for MUSE, the summer school contents, and some student modeling outcomes. While doing so, it shows the importance of leveraging the abstract contents of a course with practical exercises when learning a new tool. Reflecting upon the students' experience, this paper draws conclusions that can be used to improve future editions of the same course and be extended to the design of training courses for other tools. [ABSTRACT FROM AUTHOR]

In this paper, using data from Romania, we analysed the changes in electricity consumption generated during the COVID-19 crisis, and the measures taken against the spread of the coronavirus to limit the effects of the pandemic. Using a seasonal autoregressive econometric model, we found that, beyond seasonal (weekly, monthly, quarterly, yearly) effects, the average daily electricity real consumption in Romania, during the state of the emergency period (16 March 16 to 14 May 2020) decreased by −194.8 MW (about −2.9%), compared to the historical data (2006–March 2022), and this decrease is not due to the action of some random factors, and it is not a manifestation of domain-specific seasonality. The literature discusses the hypothesis that during the pandemic time, the profile of daily electricity consumption on weekdays was close to the typical Sunday profile. We tested a similar hypothesis for Romania. As a methodology, we tried to go beyond the simple interpretation of statistics and graphics (as found in most papers) and we calculated some measures of distances (the Mahalanobis distance, Manhattan distance) and similarity (coefficient of correlation, cosines coefficient) between the vectors of daily electricity real consumptions, by hourly intervals. As the time interval, we have analysed, for Romania, the electricity real consumption over the period January 2006–March 2022, by day of the week and within the day, by hourly intervals (5911 observations). We found (not very strong) evidence supporting a hypothesis that, in the pandemic crisis, the profile of electricity consumption approaches the weekend pattern only for the state of the emergency period, and we could not find the same evidence for the state of the alert period (June 2020–March 2022). The strongest closeness is to the hourly consumption pattern of Saturday. That is, for Romania, in terms of electricity consumption, "under lockdown, every day is a Sunday" (Staffell) it is rather "under lockdown, every day is (almost) a Saturday"! During the state of the alert period, consumption returned to the pre-crisis profile. Since certain behaviours generated by the pandemic have been maintained in the medium and long term (distance learning, working from home, online sales, etc.), such studies can have policy implications, especially for setting energy policy measures (e.g., in balancing load peaks). [ABSTRACT FROM AUTHOR]

Accurate electricity demand forecasting is essential for global energy security, reducing costs, ensuring grid stability, and informing decision making in the energy sector. Disruptions often lead to unpredictable demand shifts, posing greater challenges for short-term load forecasting. Understanding electricity demand patterns during a pandemic offers insights into handling future disruptions. This study aims to develop an effective forecasting model for daily electricity peak demand, which is crucial for managing potential disruptions. This paper proposed a hybrid approach to address scenarios involving both government intervention and non-intervention, utilizing integration methods such as stepwise regression, similar day selection-based day type criterion, variational mode decomposition, empirical mode decomposition, fast Fourier transform, and neural networks with grid search optimization for the problem. The electricity peak load data in Thailand during the year of the COVID-19 situation is used as a case study to demonstrate the effectiveness of the approach. To enhance the flexibility and adaptability of the approach, the new criterion of separating datasets and the new criterion of similar day selection are proposed to perform one-day-ahead forecasting with rolling datasets. Computational analysis confirms the method's effectiveness, adaptability, reduced input, and computational efficiency, rendering it a practical choice for daily electricity peak demand forecasting, especially in disrupted situations. [ABSTRACT FROM AUTHOR]

Global energy consumption is growing rapidly, with the frequency and intensity of extreme events constantly increasing, posing a long-term threat to power supply and consumption. Therefore, analyzing the spatiotemporal characteristics of electricity consumption and quantitatively assessing the impact of extreme events on electricity consumption are of great significance. Based on fine-grained electricity consumption data from Europe for the years 2019–2022, this paper employs a data mining perspective and four methods including Z-score, Isolation Forest, Local Outlier Factor, and Autoencoder to detect abnormal electricity consumption during extreme events. Additionally, it combines indicators such as elastic loss, vulnerability, and duration to measure the impact of extreme events on electricity consumption. It is found that low temperatures could lead to abrupt changes in electricity consumption, with Northern Europe being more significantly affected by low temperatures. The COVID-19 pandemic had the most significant impact on electricity consumption in Europe, with the middle part of Europe being the hardest hit during the first wave of the pandemic. Electricity anomalies during the pandemic period were related to national pandemic control policies and exhibited some lag. High temperatures persisted for a longer duration in the middle part of Europe. [ABSTRACT FROM AUTHOR]

The objective of this paper is to study the spillover effects between energy markets and stock markets with emphasis on the significant crisis periods of the last 15 years, the period of the financial crisis that officially started in 2008, the pandemic period, generically called COVID-19, and the recent confrontation in Eastern Europe. Understanding the volatility transmission mechanisms between the energy and capital markets and also from the energy markets back and the spillover effects that result is very important. We use multivariate GARCH models to highlight a spillover effect between energy commodities and equities in Central and Eastern Europe. The highest correlations are recorded for CEE stock markets with electricity and Brent, and the lowest for CEE stock markets with gas. The biggest symmetric shocks between energy and CEE stock markets occurred during the COVID-19 pandemic. In contrast, the biggest asymmetric shocks occurred during the financial crisis (for gas) and the Ukrainian invasion (for Brent). We also find that volatility is more sensitive to its lagged values in the marketplace than it is to new information. The impact and contagion of shocks caused by the oil market are greater than those of other energy markets. [ABSTRACT FROM AUTHOR]

Based on the spillover index and an improved spillover asymmetric measure method, this paper studies the volatility spillover and its asymmetric effect between crude oil and agricultural commodity futures in pre- and post-outbreak of COVID-19. We find that the total volatility spillover is higher with pre-outbreak of COVID-19. In addition, the volatility spillover caused by China's crude oil is more prominent than international crude oil around the COVID-19, which highlights the necessity of risk control through the establishment of an energy financial market in China. Finally, although the asymmetric effect of volatility spillover has always existed, crude oil was less impacted by good news post-outbreak of COVID-19, indicating that the outbreak of COVID-19 makes assets dominated by commodity attributes more sensitive to bad news. These findings are beneficial for investors to establish a cross-sector risk hedging portfolio, and provide empirical evidence for policymakers to ensure energy and food security. [ABSTRACT FROM AUTHOR]

The impact of the COVID-19 pandemic on the economy and society has gained the interest of academics and policymakers in recent years. Our paper aims to investigate and systemize the evidence from 1901 publications belonging to the top 1% of worldwide topics by prominence. This paper helps estimate a pandemic's short-run and longer-run effects on energy economics and environmental pollution. By systematizing the literature, we analyze key parameters influencing the deviation of previous worldwide economic and environmental development trajectories due to the COVID-19 pandemic. This paper examines research on the consequences of COVID-19 in five dimensions, particularly the impact of COVID-19 on (1) the environment and climate change, (2) sustainable development, (3) renewable energy and energy policy, and (4) methodology for forecasting and evaluating the energy sector and economic sectors. Our results indicate that the pandemic crisis's impact on achieving sustainable development goals in the context of energy change and pollution is controversial and complex. On the one hand, scientists are unequivocal about the positive impact of the COVID-19 pandemic on improving air quality and reducing CO2 emissions. Nevertheless, the long-term effects are threatened by gaps between countries in economic prosperity and different vaccination rates. Most studies have found that only a joint social effort and international collaboration can move to a clean energy system. In addition, using the Scopus database and modern tools of machine analysis, we determine leading authors in the subject area of "renewable energy, sustainability, and the environment", as well as the top networks and scientific communities that appear within energy. The analysis of this manuscript can be helpful to policymakers and stakeholders in developing comprehensive energy efficiency programs and energy-saving strategies to achieve SDG targets. [ABSTRACT FROM AUTHOR]

Sustainability has become a broad societal goal, aiming to ensure that human beings coexist safely and harmoniously with nature over a longer time. The influence of the COVID-19 pandemic on the global economy is coming to an end. The development and merits of sustainable energy supply, advanced technology, and economic features have received significant attention over the last few decades. However, significant gaps still exist with respect to how to design, construct, and implement hybrid and optimal energy systems with the lowest investment and cost. Since 2002, the Sustainable Development of Energy, Water, and Environment Systems (SDEWES) conferences have become a significant meeting venue for researchers to introduce, discuss, share, and disseminate novel concepts and ideas. This paper presents an overview of published articles in the Special Issues (SIs) dedicated by the series SDEWES conferences, especially those published in Energies recommended by the 16th SDEWES Conference, which was held on 10–15 October 2021 in Dubrovnik, Croatia. This SI in Energies focused on four main topics, including the application of renewable bioenergy, component enhancement in renewable systems, sustainable development for buildings and economic analysis and evaluation for sustainability. The collected papers provide insight into the topics related to recent advances in improving sustainable efficiency, including studies on waste-to-wealth techniques, utilization of hybrid bioenergy systems, heat exchangers and other components for performance enhancement, energy supply and demand analysis, low-temperature DHC systems, techno-economic assessment, and environmental evaluation. [ABSTRACT FROM AUTHOR]

The European transition to green economy is based on long-term strategies. The Europe 2020 Strategy was launched in 2010 in order to promote smart, sustainable, and inclusive growth in the region. Later, the ambitions regarding this strategy were followed by the Green Deal and Sustainable Development Goals. Now, the effort of countries towards the green economy is even more relevant than ever because of the context of today's global energy crisis and economic uncertainty due to many challenges such as the COVID-19 pandemic or Russia invasion of Ukraine. This paper seeks to assess the countries' achievements in seeking climate change and energy targets of the Europe 2020 Strategy by applying the innovative multi-criteria decision-making (MCDM) technique. The kernel-based comprehensive assessment (KerCA) method was applied for the calculations of the progress achieved and countries' rankings. It allows to evaluate countries' achievements and compare them using the mathematical models. The analysis of the national target implementation in the countries allows to determine the compliance of countries with their commitments and liability towards other countries and the European Union (EU). An analysis of the implementation of the strategy can serve as a guide to further develop climate change and energy policy in the region. Furthermore, the added value of the article is systematic overview of scientific studies proposing different methodologies for the analysis of target implementation at the whole region level. The novelty of this paper also relies on the approach, which considers not only the level of achievement of the target, but also assesses the excess. [ABSTRACT FROM AUTHOR]

Predicting energy demand in adverse scenarios, such as the COVID-19 pandemic, is critical to ensure the supply of electricity and the operation of essential services in metropolitan regions. In this paper, we propose a deep learning model to predict the demand for the next day using the "IEEE DataPort Competition Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm" database. The best model uses hybrid deep neural network architecture (convolutional network–recurrent network) to extract spatial-temporal features from the input data. A preliminary analysis of the input data was performed, excluding anomalous variables. A sliding window was applied for importing the data into the network input. The input data was normalized, using a higher weight for the demand variable. The proposed model's performance was better than the models that stood out in the competition, with a mean absolute error of 2361.84 kW. The high similarity between the actual demand curve and the predicted demand curve evidences the efficiency of the application of deep networks compared with the classical methods applied by other authors. In the pandemic scenario, the applied technique proved to be the best strategy to predict demand for the next day. [ABSTRACT FROM AUTHOR]

The high volatility of commodity prices and various problems that the energy sector has to deal with in the era of COVID-19 have significantly increased the risk of oil price changes. These changes are of the main concern of companies for which oil is the main input in the production process, and therefore oil price determines the production costs. The main goal of this paper is to discover decision rules for a buyer of American WTI (West Texas Intermediate) crude oil call options. The presented research uses factors characterizing the option price, such as implied volatility and option sensitivity factors (delta, gamma, vega, and theta, known as "Greeks"). The performed analysis covers the years 2008–2022 and options with an exercise period up to three months. The decision rules are discovered using association analysis and are evaluated in terms of the three investment efficiency indicators: total payoff, average payoff, and return on investment. The results show the existence of certain ranges of the analyzed parameters for which the mentioned efficiency indicators reached particularly high values. The relationships discovered and recorded in the form of decision rules can be effectively used or adapted by practitioners to support their decisions in oil price risk management. [ABSTRACT FROM AUTHOR]

The negative socio-economic consequences of the COVID-19 pandemic are widely discussed. However, relatively less attention is paid to its impact on the world commodity price formation including energy and food prices. The aim of this paper is to examine the impact of the COVID-19 pandemic on world energy commodity prices and their interactions with world food commodity prices. Using the World Bank data on commodity prices we look for evidence of changes in energy and food prices caused by occurrence of the COVID-19 pandemic, which was assumed to be a negative shock to the global economy in terms of both supply and demand. Based on data series analysis of indices of world energy and food commodity prices, it is evident that after the outbreak of the COVID-19 pandemic the energy prices, especially oil prices, plummeted. Food prices followed the same direction; however, their plunge was much less extreme. In general, it can be concluded that the pandemic caused a severe energy price shock which clearly had a negative impact on global economic growth, but the scale of this impact differs depending on the type of economic sector and countries' net export positions in energy and food trade. [ABSTRACT FROM AUTHOR]

After the economic shock caused by COVID-19, with relevant effects on both the supply and demand for energy assets, there was greater interest in understanding the relationships between key energy prices. In order to contribute to a deeper understanding of energy price relationships, this paper analyzes the dynamics between the weekly spot prices of oil, natural gas and benchmark ethanol in the US markets. The analysis period started on 23 June 2006 and ended on 10 June 2022. This study used the DMCA cross-correlation coefficient in a dynamic way, using sliding windows. Among the main results, it was found that: (i) in the post-pandemic period, oil and natural gas were not correlated, in both short- and long-term timescales; and (ii) ethanol was negatively associated with natural gas in the most recent post-pandemic period, especially in short-term scales. The results of the present study are potentially relevant for both market and public agents regarding investment diversification strategies and can aid public policies due to the understanding of the interrelationship between energy prices. [ABSTRACT FROM AUTHOR]

This paper investigates the month-wise impact of COVID-19 conditions on residential load due to people's presence at home during office hours in Memphis city, Tennessee, USA. The energy consumption patterns of four consumers are analyzed based on the data available from pre-COVID to continuing COVID-19 situations. It is observed that the energy consumptions from April 2020 to June 2021 for all families have increased during office hours than that of pre-COVID months. Therefore, the impacts of the increased loads on distribution transformer are analyzed in terms of hottest spot, top-oil temperature, and loss of transformer life. Moreover, an experimental setup is made to produce the harmonics in currents of non-linear residential load which has detrimental effects on temperature rise of distribution transformer. In addition, this work proposes implementation of harmonic filter at the load side considering the impact of harmonics produced by loads to reduce the temperature rise due to the increased load consumption and presence of harmonics in currents produced by the load. The rise in temperatures and the loss of life of distribution transformer with and without the proposed solutions are simulated in MATLAB to show the efficacy of the proposed solution. Moreover, cost value analysis among different methods, which can be implemented to reduce the adverse impact on distribution transformer, are provided to rank the available methods. [ABSTRACT FROM AUTHOR]

Decarbonization of urban mobility requires complex, interconnected actions. The instruments for decarbonizing urban mobility include urban collective transport, bicycles and means of personal transport, cybermobility and electromobility. The article presents the results of research on the transport behavior of the creative class, the effects of the COVID-19 pandemic on the functioning of urban mobility, and the results of research on the efficiency of electromobility in the field of the implementation of battery-powered buses. The research was aimed at a critical assessment of the current mobility model and creation of the basis for change in this area. In particular, these studies focused on changes in transport needs and greater use of cyberspace for various activities, which should reduce the volume of physical flows of people in urban transport networks. The aim of the article is to present the conclusions of these studies in terms of their use to change mobility models in cities. The practical significance of the presented research consists in indicating the factors of building individualized urban mobility strategies, with particular emphasis on the issues of sustainable development and decarbonization. The authors' contribution to the development of science is demonstration of the possibilities of changing the mobility model in the city and reducing greenhouse gas emissions. In particular, this applies to urban metropolises that are moving away from heavy mining and processing industries, i.e., the number of jobs in business and educational services is increasing, and the industry of new technologies is developing. The conclusions formulated in the paper indicate, in particular, the significant decarbonization potential of cybermobility and the use of zero-emission means of transport, such as bicycles and scooters, which can be released by changing the approach to the organization of work and learning, in particular their virtualization and deconcentration of travel sources. [ABSTRACT FROM AUTHOR]

Effective operation of a microgrid depends critically on accurate forecasting of its components. Recently, internet forecasting competitions have been used to determine the best methods for energy forecasting, with some competitions having a special focus on microgrids and COVID-19 energy-use forecasting. This paper describes forecasting for the IEEE Computational Intelligence Society 3rd Technical Challenge, which required predicting solar and building loads of a microgrid system at Monash University for the month of November 2020. The forecast achieved the lowest error rate in the competition. We review the literature on recent energy forecasting competitions and metrics and explain how the solution drew from top-ranked solutions in previous energy forecasting competitions such as the Global Energy Forecasting Competition series. The techniques can be reapplied in other forecasting endeavours, while approaches to some of the time-series forecasting are more ad hoc and specific to the competition. Novel thresholding approaches were used to improve the quality of the input data. As the training and evaluation phase of the challenge occurred during COVID-19 lockdown and reopening, the building demand was subject to pandemic-related effects. Finally, we assess other data sources which would have improved the model forecast skill such as data from different numerical weather prediction (NWP) models, solar observations, and high-resolution price and demand data in the vicinity of the campus. [ABSTRACT FROM AUTHOR]

The application of newly available technologies in the green maritime sector is difficult due to conflicting requirements and the inter-relation of different ecological, technological and economical parameters. The governments incentivize radical reductions in harmful emissions as an overall priority. If the politics do not change, the continuous implementation of stricter government regulations for reducing emissions will eventually result in the mandatory use of, what we currently consider, alternative fuels. Immediate application of radically different strategies would significantly increase the economic costs of maritime transport, thus jeopardizing its greatest benefit: the transport of massive quantities of freight at the lowest cost. Increased maritime transport costs would immediately disrupt the global economy, as seen recently during the COVID-19 pandemic. For this reason, the industry has shifted towards a gradual decrease in emissions through the implementation of "better" transitional solutions until alternative fuels eventually become low-cost fuels. Since this topic is very broad and interdisciplinary, our systematic overview gives insight into the state-of-the-art available technologies in green maritime transport with a focus on the following subjects: (i) alternative fuels; (ii) hybrid propulsion systems and hydrogen technologies; (iii) the benefits of digitalization in the maritime sector aimed at increasing vessel efficiency; (iv) hull drag reduction technologies; and (v) carbon capture technologies. This paper outlines the challenges, advantages and disadvantages of their implementation. The results of this analysis elucidate the current technologies' readiness levels and their expected development over the coming years. [ABSTRACT FROM AUTHOR]

Today, one of the main priorities in Europe is to introduce measures to increase environmental protection. Therefore, SMEs operating in the branch related to renewable energy are essential for several EU priorities. SMEs can also be vulnerable, especially during times of crisis, which could negatively affect renewable energy development. Therefore, this paper aims to evaluate and analyze the financial security management of renewable energy SMEs during the COVID-19 pandemic. The research is conducted on SMEs operating in the renewable energy sector in Poland. The research periods are 2019 (before the COVID-19 crisis) and 2020 (during the crisis). By analyzing different financial performance ratios, we found that SMEs operating in the energy sector have been able to stabilize and maintain their current economic position compared to the past. The results of this study indicate that the receivables collection period, cash conversion cycle, and operating cycle could have been insignificantly shorter during the COVID-19 crisis. Furthermore, to have stronger financial security, SMEs have adopted a conservative policy in which the share of accounts receivable in current assets has been significantly reduced during COVID-19. In contrast, an insignificant increase in liquidity, quick ratios, and inventory turnover have been observed. In general, Polish SMEs have chosen to adopt conservative strategies during COVID-19 to have better liquidity security. [ABSTRACT FROM AUTHOR]

The smart city has been a growing utopia, a brilliant image of a city of the future, in the past twenty years. Since its birth, at the end of the previous century, several changes have been seen in urban areas, both aligned and detached from this concept. On the one side, digital implementation seems to be growing in all the major cities, especially in the service sector, which are experiencing a proliferation of new solutions, tools and modalities of interactions. On the other side, new concepts are rising such as the "digital twin", the "15-minute city", and the "metaverse city", evidencing both the necessity to continuously innovate and reach higher levels of digitalization but also the need to focus on people's life. This paper aims to provide a contribution to the understanding of the concept's evolution at the forefront of climate change with the aim to detect the elements of innovation, focusing on implementation roadmaps and trends but also searching for evolutions in research due to the COVID-19 pandemic. The hypothesis is that some changes of direction could have been triggered by the pandemic due to the urgency of finding concrete solutions. The conclusions will show that it is possible to detect some of them, especially in the technological domain. [ABSTRACT FROM AUTHOR]

Forecasting return and profit is a primary challenge for financial practitioners and an even more critical issue when it comes to forecasting energy market returns. This research attempts to propose an effective method to predict the Brent Crude Oil return, which results in remarkable performance compared with the well-known models in the return prediction. The proposed hybrid model is based on long short-term memory (LSTM) and convolutional neural network (CNN) networks where the autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroscedasticity (GARCH) outputs are used as features, along with return lags, price, and macroeconomic variables to train the models, resulting in significant improvement in the model's performance. According to the obtained results, our proposed model performs better than other models, including artificial neural network (ANN), principal component analysis (PCA)-ANN, LSTM, and CNN. We show the efficiency of our proposed model by testing it with a simple trading strategy, indicating that the cumulative profit obtained from trading with the prediction results of the proposed 2D CNN-LSTM model is higher than those of the other models presented in this research. In the second part of this study, we consider the spread of COVID-19 and its impact on the financial markets to present a precise LSTM model that can reflect the impact of this disease on the Brent Crude Oil return. This paper uses the significance test and correlation measures to show the similarity between the series of Brent Crude Oil during the SARS and the COVID-19 pandemics, after which the data during the SARS period are used along with the data during COVID-19 to train the LSTM. The results demonstrate that the proposed LSTM model, tuned by the SARS data, can better predict the Brent Crude Oil return during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic created large disruptions in the energy industry as governments all around the world responded by enforcing lockdowns on 54% of the global population to slow down the spread of the virus. This impacts the energy consumption of the residential sector as policies prevented residents from leaving their homes and accelerated the transition towards remote work environments. The impact of lockdown on South Africa's energy sector is unique to other countries due to its extreme inequalities across the affluence divide and ongoing blackouts that affect the entire country. A case study is performed in George, a region in South Africa with a distribution of wealth and energy inequality typical for South Africa. In this paper, the following questions are answered: (i) How has the lockdown period influenced the total energy consumption of a household? and (ii) To what extent has the lockdown affected energy consumption of households based on wealth and demographics? The results of this study showed that implementing the highest stage of lockdown resulted in wealthy communities increasing their overall energy consumption by 5 percentage points and poor communities by 2.5 percentage points. The findings of this study support the use of local data and insight for the unique difference in behavioural change across different communities for optimal decision making in future pandemics or natural disasters. [ABSTRACT FROM AUTHOR]

Since the emergence of the COVID-19 pandemic, people all around the globe have seen its effects, including city closures, travel restrictions, and stringent security measures. However, the effects of the COVID-19 pandemic extend beyond people's everyday lives. It impacts the air, water, soil, and carbon emissions as well. This article examines the effect of energy and the COVID-19 pandemic on China's carbon dioxide emissions in light of the aforementioned context, using the daily data from 20 January 2020 and ending on 20 April 2022. Using the nonlinear autoregressive distributed lag model for empirical analysis, the findings indicate that COVID-19 pandemic confirmed cases and renewable energy advance environmental sustainability due to their negative effects on carbon dioxide emissions, whereas fossil fuel energy hinders environmental sustainability due to its positive effect on carbon dioxide emissions. Moreover, these results are also supported by the results of the frequency domain causality test and the Markow switching regression. In light of these results, there are several policy implications, such as vaccination, renewable energy utilization, and non-renewable energy alternative policies, which have been proposed in this paper. [ABSTRACT FROM AUTHOR]

The aim of this paper was to examine whether the COVID-19 epidemic has slowed the fulfilment of one of the core tasks of the energy sector "Ensure Access to Affordable, Reliable, Sustainable and Modern Energy for All" (SDG7) taking into account corporate social responsibility. Four research questions and hypotheses were posed, relating to the perspectives of local authorities, the activities of large energy companies, the impact of the epidemic on the implementation of the SDG7 and, in addition, to the understanding of CSR principles from the point of view of ordinary entrepreneurs. A qualitative descriptive analysis based on two reliable databases and a survey procedure (Question 4) was used to answer the research questions posed. The goal was achieved by positively confirming three hypotheses and testing one negatively, relating to COVID-19's slowing role in SDG7 implementation. The analysis showed that the 2020–2021 epidemic in Poland has led to more initiatives in this area, contrary to expectations. However, they were linked to the simultaneous implementation of other SDGs, which distorted their importance for achieving Goal 7. In summary, although energy companies were more active than expected during the epidemic, they had a low contribution to SDG 7. This also applies to local authorities. An analysis of the knowledge about CSR in a group of entrepreneurs from the Lublin district (case study) confirmed the opinion appearing in the literature about the lack of understanding of the concept and the need for its application. [ABSTRACT FROM AUTHOR]

This paper uses two competing machine learning models, namely the Support Vector Regression (SVR) and the eXtreme Gradient Boosting (XGBoost) against the Autoregressive Integrated Moving Average ARIMAX (p,d,q) model to identify their predictive performance of the crude oil volatility index before and during COVID-19. In terms of accuracy, forecasting results reveal that the SVR model dominates the XGBoost and ARIMAX models in predicting the crude oil volatility index before COVID-19. However, the XGBoost model provides more accurate predictions of the crude oil volatility index than the SVR and ARIMAX models during the pandemic. The inverse cumulative distribution of residuals suggests that both ML models produce good results in terms of convergence. Findings also indicate that there is a fast convergence to the optimal solution when using the XGBoost model. When analyzing the feature importance, the Shapley Additive Explanation Method reveals that the SVR performs significantly better than the XGBoost in terms of feature importance. During the pandemic, the predictive power of the CBOE Volatility Index and Economic Policy Uncertainty index for forecasting the crude oil volatility index is improved compared to the pre-COVID-19 period. These findings imply that investor fear-induced uncertainty in the financial market and economic policy uncertainty are the most significant features and hence represent substantial sources of uncertainty in the oil market. [ABSTRACT FROM AUTHOR]

A large number of studies have confirmed that oil speculation has played a vital role in oil price fluctuation in recent years. However, the heterogeneous impact of oil financialization on oil price fluctuation has not received enough attention. Based on time series data from January 1990 to October 2021, this paper adopts the Time-Varying Parameter Vector Auto-Regression (TVP-VAR) model and the Ensemble Empirical Mode Decomposition (EEMD) method to study the heterogeneous impact of oil financialization on oil price fluctuation from three perspectives: different periods, different frequencies, and different time points of major events. The research results are as follows. First, the impact of oil financialization on oil price fluctuation in different periods is heterogeneous in terms of fluctuation amplitude and intensity. During major events such as the financial crisis or the COVID pandemic, the impact of oil financialization on oil price fluctuation is volatile and intense. Second, the impact of oil financialization on the oil price fluctuation of different frequencies is mainly reflected in the direction and duration. Oil financialization mainly promotes high-frequency oil price fluctuation in the short term, and it mainly suppresses low-frequency oil price fluctuation in the long term. Third, the impact of oil financialization on oil price fluctuation is heterogeneous in terms of duration, intensity, and transmission speed at different time points of major events. [ABSTRACT FROM AUTHOR]

The paper presents the results of sociological qualitative research on safety management in work organization. The focus was on how to adapt the workforce to the sanitary regime during the time of the COVID-19 pandemic in the lignite mining and energy sector in the Bełchatów poviat (Łódź voivodeship, Poland). The study aimed to identify the dimensions of safety and the work culture created in crisis conditions as perceived by workers. Based on the conducted analysis, the process of adapting employees to the new rules of work organization was reported, including the transition from fear and anxiety to control over threats and negative emotions. The essence of the research was to support institutional memory to consolidate the existing knowledge and use it in the future. [ABSTRACT FROM AUTHOR]

To obtain the price return and price volatility spillovers between renewable energy stocks, technology stocks, oil futures and carbon allowances under different investment horizons, this paper employs a frequency-dependent method to study the dynamic connectedness between these assets in four frequency bands. The results show that, first, there is a strong spillover effect between these assets from a system-wide perspective, and it's mainly driven by short-term spillovers. Second, in the time domain, technology stocks have a more significant impact on renewable energy stocks compared to crude oil. However, through the study in the frequency domain, we find renewable energy stocks exhibit a more complex relationship with the other two assets at different time scales. Third, renewable energy stocks have significant spillover effect on carbon prices only in the short term. On longer time scales, other factors such as energy prices, climate and policy may have a greater impact on carbon allowance prices. Fourth, the spillover effect of the system is time-varying and frequency-varying. During the European debt crisis, the international oil price decline and the COVID-19 pandemic, the total spillover index of the system has experienced a substantial increase, mainly driven by medium, medium to long or long term spillovers. [ABSTRACT FROM AUTHOR]

Our goal is to examine the efficiency of different intraday electricity markets and if any of their price prediction models are more accurate than others. This paper includes a comprehensive review of Germany, France, and Norway's (NOR1) day-ahead and intraday electricity market prices. These markets represent different energy mixes which would allow us to analyze the impact of the energy mix on the efficiencies of these markets. To draw conclusions about extreme market conditions, (i) we reviewed the market data linked to COVID-19. We expected higher volatility in the lockdowns than before and therefore decrease in the efficiency of the prediction models. With our analysis, (ii) we want to draw conclusions as to whether a mix based mainly on renewable energies such as that in Norway implies lower volatilities even in times of crisis. This would answer (iii) whether a market with an energy mix like Norway is more efficient in highly volatile phases. For the analysis, we use data visualization and statistical models as well as sample and out-of-sample data. Our finding was that while the different price and volatility levels occurred, the direction of the market was similar. We could find evidence that our expectations (i–iii) were met. [ABSTRACT FROM AUTHOR]

This paper presents an assessment of the impact of the COVID-19 pandemic on the waste management sector, and then, based on laboratory tests and computer calculations, indicates how to effectively manage selected waste generated during the pandemic. Elemental compositions—namely, C, H, N, S, Cl, and O—were determined as part of the laboratory tests, and the pyrolysis processes of the above wastes were analysed using the TGA technique. The calculations were performed for a pilot pyrolysis reactor with a continuous flow of 240 kg/h in the temperature range of 400–900 °C. The implemented calculation model was experimentally verified for the conditions of the refuse-derived fuel (RDF) pyrolysis process. As a result of the laboratory tests and computer simulations, comprehensive knowledge was obtained about the pyrolysis of protective masks, with particular emphasis on the gaseous products of this process. The high calorific value of the pyrolysis gas, amounting to approx. 47.7 MJ/m3, encourages the management of plastic waste towards energy recovery. The proposed approach may be helpful in the initial assessment of the possibility of using energy from waste, depending on its elemental composition, as well as in the assessment of the environmental effects. [ABSTRACT FROM AUTHOR]

Prices of oil and other commodities have fluctuated wildly since the outbreak of the COVID-19 pandemic. It is crucial to explore the causes of price fluctuations and understand the source and path of risk contagion to better mitigate systemic risk and maintain economic stability. The paper adopts the method of network topology to examine the path of risk contagion between China's and foreign commodities, focusing on the dynamic evolution and transmission mechanism of risk contagion during the pandemic. This research found that among China's commodities, energy, grain, and textiles are net recipients of risk contagion, while chemical products and metals are net risk exporters. Among international commodities, industries have positive risk spillover effects on metals and textiles. During the first phase of the pandemic, China's commodities were the main exporters of risk contagion. However, international industries and metals became the main risk exporters and exerted risk spillover on China's commodities in the second phase of the pandemic. Moreover, based on total volatility spillover index of commodities, the risk contagion among the commodities follows three paths: "interest rate → commodities → money supply", "China's economic expectation → commodities → foreign economic expectation", and "commodities → consumer confidence". [ABSTRACT FROM AUTHOR]

The surveyof traffic intensity is used to obtain information on the number of vehicles on roads during the day. Subsequently, it is possible to derive from this the daily, weekly, and other road traffic intensity information. This survey represents the basis for the calculation of the annual average daily traffic volume and the basic characteristics of traffic flow. The COVID-19 pandemic has caused extensive economic and social damage around the world. These damages have also affected traffic. Changes in traffic behavior have mainly affected the reduction in traffic intensity on road networks. Thanks to the reduction in the demand for transport, there has also been a significant reduction in traffic delays, fuel consumption and emissions. An examination of changes in traffic intensity took place at a selected intersection in 2019, 2020 and 2021. This paper describes the effects of reducing the traffic intensity, fuel consumption and emissions obtained by microsimulation. The results obtained confirmed the reduction in traffic, which also contributed to a significant reduction in vehicle delays. [ABSTRACT FROM AUTHOR]

Schism is the new normal for the bioeconomy concept. Since its proliferation in governments, the concept has been adapted to fit national or regional exigencies. Earlier this century the knowledge-based bioeconomy (KBBE) in Europe was seen as a technical and knowledge fix in the evolving sustainability landscape. At the OECD, the concept was further honed by imagining a future where biotechnologies contribute significantly to economic growth and development. Countries started to make national bioeconomy strategies. Some countries have diverged and made the bioeconomy both much larger and more general, involving a wide variety of sectors, such as industry, energy, healthcare, agriculture, aquaculture, forestry and fishing. Whatever the approach, what seems to be consistent is the need to reconcile environmental, social and economic sustainability. This paper attempts to establish one schism that could have ramifications for the future development of the bioeconomy. Some countries, including some of the largest economies but not exclusively so, are clearly following a biotechnology model, whereas others are clearly not. In the wake of the COVID-19 pandemic, biotechnologies offer outstanding potential in healthcare, although this sector is by no means included in all bioeconomy strategies. The paper also attempts to clarify how biotechnologies can address the grand challenges and the United Nations Sustainable Development Goals. The communities of scientists seem to have no difficulty with this, but citizens and governments find it more difficult. In fact, some biotechnologies are already well established, whereas others are emerging and more controversial. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has impacted electricity consumption patterns and such an impact cannot be analyzed by simple data analytics. In China, specifically, city lock-down policies lasted for only a few weeks and the spread of COVID-19 was quickly under control. This has made it challenging to analyze the hidden impact of COVID-19 on electricity consumption. This paper targets the electricity consumption of a group of regions in China and proposes a new clustering-based method to quantitatively investigate the impact of COVID-19 on the industrial-driven electricity consumption pattern. This method performs K-means clustering on time-series electricity consumption data of multiple regions and uses quantitative metrics, including clustering evaluation metrics and dynamic time warping, to quantify the impact and pattern changes. The proposed method is applied to the two-year daily electricity consumption data of 87 regions of Zhejiang province, China, and quantitively confirms COVID-19 has changed the electricity consumption pattern of Zhejiang in both the short-term and long-term. The time evolution of the pattern change is also revealed by the method, so the impact start and end time can be inferred. Results also show the short-term impact of COVID-19 is similar across different regions, while the long-term impact is not. In some regions, the pandemic only caused a time-shift in electricity consumption; but in others, the electricity consumption pattern has been permanently changed. The data-driven analysis of this paper can be the first step to fully interpret the COVID-19 impact by considering economic and social parameters in future studies. [ABSTRACT FROM AUTHOR]

Vehicle-to-grid services make use of the aggregated capacity available from a fleet of vehicles to participate in energy markets, help integrate renewable energy in the grid and balance energy use. In this paper, the critical components of such a service are described in the context of a commercial service that is currently under development. Key among these components is the prediction of available capacity at a future time. In this paper, we extend a previous work that used a deep learning recurrent neural network for this task to include online machine learning, which enables the network to continually refine its predictions based on observed behaviour. The coronavirus pandemic that was declared in 2020 resulted in closures of the university and substantial changes to the behaviour of the university fleet. In this work, the impact of this change in vehicles usage was used to test the predictions of a network initially trained using vehicle trip data from 2019 with and without online machine learning. It is shown that prediction error is significantly reduced using online machine learning, and it is concluded that a similar capability will be of critical importance for a commercial service such as the one described in this paper. [ABSTRACT FROM AUTHOR]

This paper discusses the impact of COVID-19's vulnerability context on Malawi's Energy Sector and outlines mechanisms for enhancing Malawi's energy-sector resilience based on experiences from a range of stakeholders. The investigation was conducted online by inviting purposively selected stakeholders to create presentations responding to thematic questions. The final sample had 19 stakeholders with representation from policy-makers, regulatory bodies, national grid supply players, off-grid players, development agencies, bankers, professional bodies, civil society, and women's rights bodies. The presentations from the stakeholders highlighted how COVID-19 affects the operation costs of energy systems and implementation of energy systems projects in areas that require stimulus packages to contain energy system delivery costs and prevent disruption of essential services amid the COVID-19 pandemic. These services include stakeholder responses to COVID-19 in the energy sector, the role of digital payments particularly when purchasing electricity units, and the state of third-party service providers such as banks and mobile network operators to enhance preparedness and continuity of operations for the energy sector. Based on the findings in these thematic areas and an application of systems thinking in the analysis, the paper finally makes recommendations on how Malawi and similar low-income countries can strategise to enhance energy systems resilience. [ABSTRACT FROM AUTHOR]

The challenges of the world economy and their societies, after the outbreak of the COVID-19 pandemic have led policy-makers to seek for effective solutions. This paper examines the oil price volatility response to the COVID-19 pandemic and stock market volatility using daily data. A general econometric panel model is applied to investigate the relationship between COVID-19 infection and death announcements with oil price volatility. The paper uses data from six geographical zones, Europe, Africa, Asia, North America, South America, and Oceania for the period 21 January 2020 until 13 May 2021 and the empirical findings show that COVID-19 deaths affected oil volatility significantly. This conclusion is confirmed by a second stage analysis applied separately for each geographical area. The only geographical area where the existence of correlation is not confirmed between the rate of increase in deaths and the volatility of the price of crude oil is Asia. The conclusions of this study clearly suggest that COVID-19 is a new risk component on top of economic and market uncertainty that affects oil prices and volatility. Overall, our results are useful for policy-makers, especially in the case of a new wave of infection and deaths in the future. [ABSTRACT FROM AUTHOR]

The property sector is revitalised to incorporate sustainability, specifically the biophilic design, to encourage human interaction with nature. Thus, there is an urgent need to communicate the biophilic design elements amongst stakeholders, especially the foreign workers, to ensure the project meets the requirement. However, standard project communications management is no longer practical, as the construction industry worldwide has been severely impacted by the Coronavirus Disease 2019 (COVID-19) pandemic. Hence, this paper evaluates the biophilic design elements to be communicated and examines the project communications management breakdown relating to the biophilic design elements in the Malaysian construction industry during the COVID-19 pandemic. Through a systematic literature review (SLR), focus group discussion (FGD) and questionnaire survey on 147 foreign workers, this paper found that from 33 biophilic design element items, only 1 item is categorised as highly critical, whereas another 20 items are categorised as critical. In addition, 43 potential communications management breakdown items from a total of 66 items fall under the critical category. The finding suggests that communication breakdown is caused by the language barrier amongst foreign workers from different ethnicities as well as between foreign workers and the supervisors, since face to face communication is limited during the pandemic. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic represents not just a global health crisis, but may signal the beginning of a new era of economic activity, the potential consequences of which we currently do not fully understand. In this context, the mid-to-long-range impacts of the pandemic on the energy sector have been studied extensively in the last few months. Despite these efforts, the pandemic still raises many open questions concerning the long-term operation and planning of power systems. For instance, how will the pandemic affect the integration of renewable energy sources? Should current power system expansion plans change in light of the COVID-19 pandemic? What new tools should be provided to support system operators during global health crises? It is the purpose of this paper to better understand the many aspects of these open questions by reviewing the relevant recent literature and by analyzing measured data. We point out the main challenges that the pandemic introduced by presenting patterns of electricity generation and demand, frequency deviations, and load forecasting. Moreover, we suggest directions for future research that may assist in coping with the mentioned challenges. We hope that this paper will trigger fruitful discussions and encourage further research on these important emerging topics. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic rapidly reoriented the lives of billions of people across the globe toward working, learning, and subsisting from home. This paper examines the consequences of this disruption of electricity use in Australian households. Using high-frequency electricity monitoring from 491 houses and per-circuit monitoring and in-depth interviews with 17 households, the paper (1) compares changes in energy use before and during COVID-19 lockdown, (2) quantifies the key drivers of changes in energy use experienced by households during lockdown, and (3) tracks households' interactions with energy use feedback. The findings identify significant increases in certain aspects of household electricity use directly related to COVID-19, including increased cooking and digital device use. Yet despite the government mandate requiring a large proportion of the population to remain at home, overall energy use among the majority of Queensland households monitored actually decreased during lockdown versus prior, driven primarily by a reduction in air conditioner use during lockdown as the weather cooled. Further, despite significant quantified and self-reported changes in energy use, users who had energy use feedback installed accessed their dashboards less during lockdown than they did prior. The paper discusses these results in the context of statistics on COVID-19 related energy demand fluctuations elsewhere, and the implications for the provision of energy use information to residents during significant disruptions such as lockdown. [ABSTRACT FROM AUTHOR]

The main objective of the paper is to determine the impact of the COVID-19 pandemic on the level of energy poverty in Poland. In order to achieve such a goal, the first part of the article presents the definition of energy poverty and the nature of its measures, as well as the determinants and policies of the state addressing the issue of energy poverty mitigation. In the second part of the paper, the results of research into the level of energy poverty are analyzed and the variables affecting energy poverty in Poland during the pandemic are determined. It was established on the basis of these results that the present pandemic contributed to the aggravation of financial difficulties in Polish households with regard to financing expenditure on energy carriers. It was found that COVID-19 had a negative impact on the average disposable income of Polish households, which, with the increase in prices and expenditure on energy carriers, led to an increase in the proportion of disposable income spent on energy carriers. The most affected have been the poorest households. Moreover, the long downward trend in the level of energy poverty in Poland has reversed. Thus, it has been proved that COVID-19 has contributed to the intensification of energy poverty in Poland. The theoretical and empirical considerations contained in this paper may be a valuable source of scientific data on the impact of the pandemic on household energy poverty, while public institutions may find them a source of useful information, helping to create effective instruments to mitigate energy poverty in the Polish economy. [ABSTRACT FROM AUTHOR]

The concept of energy transition has been adopted by national governments, international and regional organizations. The COVID-19 pandemic triggered increasing attention to climate policy, energy transition, and economic resilience. This paper looks at energy transition in selected countries with the use of case studies—China, the European Union, India, Russia, Saudi Arabia, and the United States. The aim of the study is to analyze the main approaches to the energy transition in these countries and the barriers which may influence this process. The study applies foresight methods such as trend monitoring, case study, and STEPE methods for barrier analysis. The outcomes include identification of energy priorities in the national context, key directions for future transformation, and selected countries' approaches to the energy transition after the COVID-19 pandemic. All considered countries are taking measures for the energy transition, but the motives, pace, and priorities differ. Their approaches were grouped into ideological (the European Union, the United States) and pragmatic (Saudi Arabia, China, and India). Both groups attribute priority to the gradual shift to clean technologies and carbon-neutral energy generation. Russia occupies an intermediate position due to attention to consumers as well as high interest in diversification of budget revenues and the shift from fossil fuel. The list of barriers to energy transition includes an inappropriate level of technological development, inconsistency of renewable power generation, substantial/rapid increase in rare earth metals prices, economic sectors' dependence on fossil fuels, lack of investment in renewables, restricted access to technologies, trade barriers, lack of political will, use of access to advanced technology market as a political tool, and renewables' dependence on weather regimes, etc. The novelty of the study is in the scope of selected cases that give a broad overview of existing approaches to the energy transition, identification of two energy transition patterns, as well as trends, wild cards, and barriers on the way. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has significantly affected the energy sector. The new behavior of industrial and non-commercial consumers changes the energy consumption model. In addition, the constraints associated with the coronavirus crisis have led to environmental effects from declining economic activity. The research is based on evidence from around the world showing significant reductions in emissions and improved air quality. This situation requires rethinking the energy development strategy, particularly the construction of smart grids as a leading direction of energy development. Evaluating the efficiency of smart grids is a vital tool for disseminating successful experience in improving their management. This paper proposes an approach to a comprehensive assessment of smart grids based on a comparative analysis of existing methods, taking into account the changes that need to be considered after the experience gained from the COVID-19 pandemic. The approach provides an accurate set of efficiency indicators for assessing smart grids to account for the direct and indirect effects of smart grids' implementation. This evaluation approach can be helpful to policymakers in developing energy efficiency programs and implementing energy policy. [ABSTRACT FROM AUTHOR]

On 11 March 2020, the WHO declared the COVID-19 epidemic to be a global pandemic. This was a consequence of the rapid increase in the number of people with positive test results, the increase in deaths due to COVID-19, and the lack of pharmaceutical drugs. Governments introduced national lockdowns, which have impacted both energy consumption and economies. The purpose of this paper is to answer the following question: do COVID-19 lockdowns affect the business cycle? We used the cycle clock approach to assess the magnitude of decrease in electricity consumption in the three waves of the epidemic, namely, April 2020, November 2021, and April 2021. Additionally, we checked the relation between energy consumption and GDP by means of spectral analysis. Results for selected 28 European countries confirm an impact of the introduced non-pharmaceutical interventions on both energy consumption and business cycle. The reduction of restrictions in subsequent pandemic waves increased electricity consumption, which suggests movement out of the economic recession. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) is a fundamental tool for power networks' proper functionality. As large consumers need to provide their own STLF, the residential consumers are the ones that need to be monitored and forecasted by the power network. There is a huge bibliography on all types of residential load forecast in which researchers have struggled to reach smaller forecasting errors. Regarding atypical consumption, we could see few titles before the coronavirus pandemic (COVID-19) restrictions, and afterwards all titles referred to the case of COVID-19. The purpose of this study was to identify, among the most used STLF methods—linear regression (LR), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN)—the one that had the best response in atypical consumption behavior and to state the best action to be taken during atypical consumption behavior on the residential side. The original contribution of this paper regards the forecasting of loads that do not have reference historic data. As the most recent available scenario, we evaluated our forecast with respect to the database of consumption behavior altered by different COVID-19 pandemic restrictions and the cause and effect of the factors influencing residential consumption, both in urban and rural areas. To estimate and validate the results of the forecasts, multiyear hourly residential consumption databases were used. The main findings were related to the huge forecasting errors that were generated, three times higher, if the forecasting algorithm was not set up for atypical consumption. Among the forecasting algorithms deployed, the best results were generated by ANN, followed by ARIMA and LR. We concluded that the forecasting methods deployed retained their hierarchy and accuracy in forecasting error during atypical consumer behavior, similar to forecasting in normal conditions, if a trigger/alarm mechanism was in place and there was sufficient time to adapt/deploy the forecasting algorithm. All results are meant to be used as best practices during power load uncertainty and atypical consumption behavior. [ABSTRACT FROM AUTHOR]

Previous analyses of the PV market (and the impact of the pandemic on it) have focused on the market as a whole. The literature does not contain analyses of selected services sectors (e.g., catering, hotel services) in terms of the use of photovoltaics. There are no studies that would show in which segments the demand profile for electricity most closely matches the production from photovoltaic installations (not to mention the impact of the pandemic). The authors analyzed selected service sectors (catering and hotel) in terms of the use of photovoltaics before and during the COVID-19 pandemic. The paper proposes a comparative methodology for the use of photovoltaics for self-consumption, including statistical analyses and calculations of the self-consumption index for representatives of various selected services sectors. The highest value of the self-consumption ratio at the level of 52% was shown for cafes and restaurants (during the pandemic). Surprisingly, in the pandemic, the self-consumption rate increased for restaurants and cafes for the same size of installations (compared to pre-pandemic times). [ABSTRACT FROM AUTHOR]

Aviation has been one of the key engines of the globalization process and, at the same, time one of the industries most affected by the COVID-19 pandemic and the parallel economic crisis. As safety seems to be the key issue at aerodromes, the aim of this article is to analyze what kind of safety hazards have the coronavirus slowdown created and which of them pose the greatest risk for aerodrome safety in the long run. What is more, the goal of the paper is to direct managers' attention to proper crisis management in line with sustainability. The main findings are that all classified hazards cause, according to an expert's assessment, similar safety risk at aerodromes, though limited workforce training and development are perceived as posing the greatest risk. The recommended solution to minimize the occurrence of the hazard is proactive risk management, which means that managers try to anticipate possible hazards in advance and act accordingly, which requires continual controlling combined with internal and external environment analysis and a consistent learning process. That should be accompanied by constant staff development. All these seem to effectively minimize most risks and make ground for a sustainable and safe transport system. [ABSTRACT FROM AUTHOR]