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Fifth-generation (5G) networks offer high-speed data transmission with low latency, increased base station volume, improved quality of service (QoS), and massive multiple-input–multiple-output (M-MIMO) channels compared to 4G long-term evolution (LTE) networks. However, the COVID-19 pandemic has disrupted the achievement of mobility and handover (HO) in 5G networks due to significant changes in intelligent devices and high-definition (HD) multimedia applications. Consequently, the current cellular network faces challenges in propagating high-capacity data with improved speed, QoS, latency, and efficient HO and mobility management. This comprehensive survey paper specifically focuses on HO and mobility management issues within 5G heterogeneous networks (HetNets). The paper thoroughly examines the existing literature and investigates key performance indicators (KPIs) and solutions for HO and mobility-related challenges while considering applied standards. Additionally, it evaluates the performance of current models in addressing HO and mobility management issues, taking into account factors such as energy efficiency, reliability, latency, and scalability. Finally, this paper identifies significant challenges associated with HO and mobility management in existing research models and provides detailed evaluations of their solutions along with recommendations for future research. [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]

In recent decades, the Italian building trade has recorded an increasing interest in the renovation and improvement of both listed and unlisted existing buildings by the Ministry of Culture, growing the debate on adapting historical buildings to current sustainability needs. The Italian National Institute of Statistics (ISTAT) observed the increase in family commuting and the attractiveness of marginal territories. Despite the scarcity of services, one-fifth of Italian small municipalities are attractive according to some indicators, primarily the demographic growth in the last three years. The COVID-19 pandemic has contributed to the re-evaluation of inland areas. Remote working and the new need for open spaces could increase the return to small villages and be further boosted by the fiscal incentives. This paper considers the evolution of last year's real estate market, evaluating whether the regulatory tools for energy retrofit and tax relief meet the trade crisis with a possible benefit of preserving historical buildings. This research cannot ignore the European background; therefore, this paper offers an overview of EU regulatory strategies for energy efficiency recently issued to increase sustainability, focusing on tax credits for improving existing buildings. In conclusion, some considerations are proposed for future in-depth research. [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]

During the COVID-19—related lockdowns (2020–2022), mobility patterns and charging needs were substantially affected. Policies such as work from home, lockdowns, and curfews reduced traffic and commuting significantly. This global pandemic may have also substantially changed mobility patterns on the long term and therefore the need for electric vehicle charging infrastructure. This paper analyzes changes in electric charging in the Netherlands for different user groups during different phases of the COVID-19 lockdown to assess the effects on EV charging needs. Charging needs dropped significantly during this period, which also changed the distribution of the load on the electricity grid throughout the day. Curfews affected the start times of charging sessions during peak hours of grid consumption. Infrastructure dedicated to commuters was used less intensively, and the charging needs of professional taxi drivers were drastically reduced during lockdown periods. These trends were partially observed during a post–lockdown measuring period of roughly 8 months, indicating a longer shift in mobility and charging patterns. [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]

The COVID-19 pandemic has caused disruption to the original order of the global economy and has had an influence on the social and economic growth of countries all over the globe. As a result, the aim of this paper is to explore the consequences of the COVID-19 pandemic on a sample of OECD countries with regard to energy and the economy. For empirical investigation, data from the first quarter of 2010 to the first quarter of 2022 are used, and the system generalized method of moments is applied. The findings reveal that during the COVID-19 pandemic, energy consumption impeded economic growth while economic expansion was the primary driver of energy resource consumption. Furthermore, an examination of heterogeneous effects reveals that economic growth and energy consumption are heterogeneous both before and after the COVID-19 pandemic. To conclude, these findings might provide a contribution to the body of research that has already been undertaken on this subject. [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]

Background: The health crisis due to COVID-19 has changed the habits of social coexistence and therefore has had a significant impact on several economic sectors, including logistics. Nowadays, this sector faces one of the most difficult challenges in history and hence has carried out innovative strategies to adjust to the new normal and guarantee the permanence of the supply chain. In this paper, a novel green logistics technique for planning merchandise deliveries is introduced herein. Methods: This technique is based on a modified version of the Resistive Grid Path Planning Methodology (RGPPM) to establish a path through georeferential locations for delivering merchandise to customers. To do this, multi-connected resistive grids are based at customer locations for searching the path connecting the positions through electronic circuit analysis techniques. Results: Experimental results reveal that the proposed method can find a minimum spanning tree that connects all hand-over points by a continuous path. This finding has represented a reduction of around 45% in the length of the path with respect to the longest path. Conclusions: Finally, this technique is capable of addressing different optimization strategies, locating a merchandise distribution center and exploring environmental standards to reduce fuel consumption. [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]

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]

By using space-based measurements of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) and CO and NO2 from the Tropospheric Monitoring Instrument (TROPOMI), this study investigates the seasonal variation in the characteristics of CO2, CO, and NO2 across major states in the United States. Beyond correlating these trends with natural factors, significant emphasis is placed on human activities, including heating demands, energy usage, and the impacts of the COVID-19 pandemic. Concentration enhancements in observations influenced by anthropogenic emissions from urban regions relative to background values are calculated to estimate gas emissions. Our investigation reveals a strong correlation between NO2 and CO2 emissions, as evidenced by a correlation coefficient (r) of 0.75. Furthermore, we observe a correlation of 0.48 between CO2 and CO emissions and a weaker correlation of 0.37 between CO and NO2 emissions. Notably, we identify the NO2 concentration as a reliable indicator of CO2 emission levels, in which a 1% increase in NO2 concentration corresponds to a 0.8194% (±0.0942%) rise in annual mean CO2 emissions. Enhancement ratios among NO2, CO, and XCO2 are also calculated, uncovering that high ΔNO2: ΔXCO2 ratios often signify outdated industrial structures and production technologies, while low ΔCO: ΔXCO2 ratios are linked to states that utilize clean energy sources. This approach offers a deeper understanding of the effect of human activities on atmospheric gas concentrations, paving the way for more effective environmental monitoring and policy-making. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had significant effects on the energy industry, leading to shifts in energy demand and generation patterns worldwide. This has created challenges for the energy sector, including disruptions in energy markets and the need for robust healthcare and electricity infrastructures. The pandemic has also resulted in changes in residential energy consumption, with some communities increasing their energy usage while others saw a decrease. Additionally, the adoption of virtual education during the pandemic has led to energy savings in educational institutions. The pandemic has also impacted electricity systems and grids, requiring ongoing research to address the challenges posed. In other sectors, such as transport and tourism, the pandemic has necessitated innovative measures to ensure passenger safety and has led to a reduction in greenhouse gas emissions in the tourism sector. Overall, the pandemic has brought about a new era for the energy industry, highlighting the need for coherent energy policies and the transition to clean energy. [Extracted from the article]

The COVID-19 pandemic and global shutdown and work-from-home order for non-essential businesses and employees led to a substantial decline in energy usage in the commercial building sector. However, the magnitude of decline was not equivalent to what would be expected for unoccupied spaces. The energy performance of low/unoccupied commercial buildings, particularly in the context of new minimum requirements to maintain indoor air quality, is an intriguing research question. In this study, we developed a numerical model that measures electricity usage sensitivity to occupancy (ESTO) where we compare the business-as-usual energy performance with unoccupied energy performance. Two years of COVID-time (in addition to a pre-COVID control year) hourly energy use (electricity (plug loads, lighting, and fans), heating, and cooling) using data from 35 commercial buildings (i.e., buildings with HVAC and other building systems typical of commercial rather than residential buildings) are analyzed to quantify those changes. A change point model is used to assess thermal load intensities, change point temperature, and off-season unoccupied baseloads. Finally, we suggest a generic framework for building scoring based on selected performance parameters. Results indicate that the suggested scoring system is robust and replicable and is reliable for ranking buildings within a given portfolio from best- to worst- performing, thus prioritizing buildings that are best candidates for retrofits. [ABSTRACT FROM AUTHOR]

The easiest approach to customer activity forecasting involves using the whole available and applicable population of customers that a certain data set contains. The drawback of this simple technique is twofold: the set could be too big, and it could contain customers of very different peculiarities, which means that customers whose previous behavior is helpful for the forecast and whose one is not are mixed, and while the first performs a good-quality prediction, the second spoils it by adding noise. Hence, if we could choose the customers with good predictability and put aside the others "as a shepherd divideth his sheep from the goats" (Matthew 25:32), we would solve both problems: less data volume and less noise; the principle is like ancient "divide et impera". In our research, we developed the method of customers separation by predictability and its dynamics with the help of LSTM models. Our research shows that (1) customer separation helps to improve the forecasting quality of the whole population due to the decomposition of all clients' time series, and (2) environmental instability such as pandemics or military action can be leveled out with incremental models. [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]

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]

The present paper focuses primarily on investigating whether energy-vulnerable households are more prone to informative, market, and behavioral biases. In this direction, a stated preference approach was used to elicit information about human behavior and cognitive barriers in the context of energy poverty based on both subjective and objective indicators. For the purposes of the survey, a questionnaire was developed that included around 40 questions about housing conditions and information, market, and behavioral barriers related to energy efficiency, energy vulnerability, etc., and specific survey hypotheses were tested employing non-parametric tests. The survey was carried out between November 2020 and January 2021 involving residents of Metsovo, a mountain settlement in Greece. In total, 303 participants took place in the survey through personal interviews, which were conducted remotely due to the COVID-19 pandemic-related distancing measures, using a video platform. The analysis shows that households that face thermal discomfort or are in arrears on energy bills seem to be more prone to certain behavioral and other biases. This conclusion is not confirmed for households that face condensation, mold and damp problems or are classified as energy-poor under the "ten percent rule". The main conclusion drawn is that the income status of the household plays a greater role compared to its classification as energy vulnerable. Nevertheless, the findings of the study need to be confirmed by future research, because the research specifically on how energy poverty affects people's decision making is extremely limited. In any case, the results are worrisome and illustrate the need for more effective energy poverty policies that will take into account the effects of scarcity on household decision making. [ABSTRACT FROM AUTHOR]

The e-commerce industry has been developing extremely dynamically for many years. This development was intensified during the COVID-19 pandemic. According to the research conducted by the authors of this paper, in Poland, during the pandemic, the number of delivered parcels increased 20–100%, depending on the courier company. The research of the authors of this article focused on the energy efficiency of the last mile, which is very important for the efficiency of the entire delivery process to customers. As the authors calculated, the last mile can consume over 70% of energy of the whole distribution channel. The article presents the results of research concerning the energy efficiency of deliveries performed by couriers and express companies in Poland. Two models of distribution used Poland have been compared—direct deliveries to final customers, and deliveries to parcel lockers. The research methods are interviews with the managers and couriers, analysis of the literature, and the simulation method. According to the results of the simulations performed by the authors, distribution with the use of parcels lockers can help reduce the consumption of fuel even by 74–87% per parcel or 36% per m3. Apart from this, the authors calculated the impact of scale of operations on the energy efficiency of the transport processes on the last mile, which is an indirect effect of the growth of the e-commerce market, caused by the pandemic. Based on the results of the original research of the authors, it can be assessed that the growth of the number of the delivered parcels during the pandemic resulted in the consumption of fuel per one parcel being reduced in some cases by over 36%. The novelty of the authors' research is that the conducted simulations regarded not only the efficiency of the processes, but also the energy consumption in delivering parcels at the last mile and during the pandemic. [ABSTRACT FROM AUTHOR]

Energy audits are a time-consuming and expensive initial step in the building retrofit process. Virtual energy audits purport to be an alternative that remotely identifies energy efficiency measures (EEMs) that may reduce electricity consumption and offset operational costs to businesses operating during and after the COVID-19 pandemic. This case study reviews virtual energy audits as a means to benchmark energy use and estimate cost savings from future EEMs. A novel feature was the estimation of energy costs associated with increasing ventilation to improve indoor air quality. The authors analyzed ten virtual energy audits performed in Honolulu, Hawai'i, over a two-week period that used existing building information and electricity use data to estimate a potential 9% to 41% annual electricity use reduction per building and a 24 MWh to 1195 MWh reduction, respectively. This paper makes a significant contribution through its assessment of virtual energy audits as a step beyond benchmarking, which has merit as an educational tool to motivate business owners to reduce energy use and improve indoor air quality. Further evaluation and improvements are suggested to study how often the virtual energy audits instigate action, how they compare with in-person audits, and their potential for use at a large scale. [ABSTRACT FROM AUTHOR]

Energy system models for the analysis of future scenarios are mainly driven by the set of energy service demands that define the broad outlines of socio-economic development throughout the model time horizon. Here, the long-term effects of the COVID-19 pandemic on the drivers of the industrial production in six energy-intensive subsectors are addressed using Vector AutoRegressive models. The model results are computed either considering or not considering the effects of the pandemic. The comparison to established pre-pandemic trends allows for validating the robustness of the selected model. The anticipated effect of the pandemic to 2040 shows a long-term reduction by 3% to 10%, according to the different subsector, in the industrial energy service demand. When the computed service demands are used as input to the TIMES-Italy model, which shows good capability to reproduce the energy consumption of the industrial sectors in the period 2006–2020, the impact of the pandemic on energy consumption forecasts can be assessed in a business-as-usual scenario. The results show how the long-term effects of the shock caused by the pandemic could lead, by 2040, to a total industrial energy consumption 5% lower than what was foreseen before the pandemic, while the energy mix remains almost unchanged. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused changes in electricity demand and, consequently, electricity consumption profiles. Given the rapid changes in energy prices, it is significant from the perspective of energy companies, and forecasting consumed energy volume. A necessity for accurate energy volume planning forces the need for analyzing consumers' behaviors during the pandemic, especially under lockdowns, to prepare for the possibility of another pandemic wave. Many business clients analyzed in the paper are economic entities functioning in sectors under restrictions. That is why analyzing the pandemic's impact on the change in energy consumption profiles and volume of these entities is particularly meaningful. The article analyzes the pandemic and restrictions' impact on the total change of energy consumption volume and demand profiles. The analysis was conducted basing on data collected from a Polish energy trading and sales company. It focused on the energy consumption of its corporate clients. Analyzed data included aggregated energy consumption volumes for all company's customers and key groups of economic entities under restrictions. The analysis demonstrates the influence of pandemic restrictions on energy consumption in the group of business clients. Significant differences are observable among various sectors of the economy. The research proves that the largest drops in energy consumption are related to shopping centers and offices. Altogether, the restrictions have caused a 15–23% energy consumption drop during the first lockdown and a maximum 11% during the second against expected values. [ABSTRACT FROM AUTHOR]

In recent years, multi-robot control systems and service robots equipped with graphical computing have been introduced in various application scenarios. However, the long-term operation of VSLAM calculation leads to reduced energy efficiency of the robot, and accidental localization failure still persists in large-scale fields with dynamic crowds and obstacles. This study proposes an EnergyWise multi-robot system based on ROS that actively determines the activation of VSLAM using real-time fused localization poses by an innovative energy-saving selector algorithm. The service robot is equipped with multiple sensors and utilizes the novel 2-level EKF method and incorporates the UWB global localization mechanism to adapt to complex environments. During the COVID-19 pandemic, three disinfection service robots were deployed to disinfect a large, open, and complex experimental site for 10 days. The results demonstrated that the proposed EnergyWise multi-robot control system successfully achieved a 54% reduction in computing energy consumption during long-term operations while maintaining a localization accuracy of 3 cm. [ABSTRACT FROM AUTHOR]

Reducing energy waste is one of the primary concerns facing Remote Industrial Plants (RIP) and, in particular, the accommodations and operational plants located in remote areas. With the COVID-19 pandemic continuing to attack the health of workforce, managing the balance between energy efficiency and Occupation Health and Safety (OHS) in the workplace becomes another great challenge for the RIP. Maintaining this balance is difficult mainly because a full awareness of the OHS will generally consume more energy while reducing the energy cost may lead to a less effective OHS, and the existing literature has not seen a system that is designed for the RIPs to conserve energy usage and improve workforce OHS simultaneously. To bridge this gap, in this paper, we propose an AI Empowered Cyber Physical Ecosystem (AECPE) solution for the RIPs, which integrates Cyber-Physical Systems (CPS), artificial intelligence, and mobile networks. The preliminary results of lab experiments and field tests proved that the AECPE was able to help industries reduce the corporate annual energy cost that is worth millions of dollars, optimise the environmental conditions, and improve OHS for all workers and stakeholders. The implementation of the AECPE can result in efficient energy usage, reduced wastage and emissions, environment-friendly operations, and improved social reputation of the industries. [ABSTRACT FROM AUTHOR]

Energy is one of the most important aspects of urban development and technological advancements. As its production and consumption are connected to several environmental, social, and economic issues covering all three sustainability pillars, strategic and targeted energy planning is vital to the smooth transition towards a more efficient and greener society. In accordance with the specific priorities of every state, sustainable energy planning should also satisfy the international trends, requirements, and targets, including the global commitments for sustainable development. As of this time, energy transition with further deployment of renewable energy and energy efficiency improvement are the priorities for a sustainable future. However, due to recent global events, a new situation has been established. The COVID-19 pandemic and the ongoing war in Ukraine have caused new circumstances challenging the recognized approaches for an effective sustainable energy strategy. While the global pandemic led to a temporary reduction of energy use and created habits for further savings, the war caused energy security issues, especially for Europe, and an increase in energy prices. Moreover, both questioned the implementation of green energy strategies and policies and initiated energy poverty. In this framework, the perspectives of the criteria, on which the energy planning and the relevant research could lean, are investigated and discussed. [ABSTRACT FROM AUTHOR]

The energy crisis caused first by the COVID-19 pandemic and continued by the Russo-Ukrainian War has demonstrated that energy is a determining factor in the conduct of activities of any state. Several studies have examined the relationship between energy consumption and economic growth, developing various theories, but there is no consensus. This study investigates relationships by analyzing several regression models and choosing the fittest. Then, the verification of its validity is performed through the neural network, which gives greater credibility to the results obtained. Furthermore, using a structural analysis, the investigation is expanded to ranking the impact of sector-specific energy consumption on economic growth. The research includes data from 1995 to 2020 for the European Union (EU) and Romania. The results indicate that short-term energy consumption can have a positive or negative impact on economic growth, both in the EU and Romania. The structural analysis highlights the direct and indirect effects, with different intensities, of sector-specific energy consumption on economic growth. This study is interested primarily in the conditions of uncertainty caused by the COVID-19 pandemic and the Russo-Ukrainian War, to enable the EU and each member state to take effective energy policy measures to ensure their energy security. [ABSTRACT FROM AUTHOR]

The building sector consumes as much as 80% of generated electricity in the UAE; during the COVID-19 pandemic, the energy consumption of two sub-sectors, i.e., commercial (50%) and residential (30%), was significantly impacted. The residential sector was impacted the most due to an increase in the average occupancy during the lockdown period. This increment continued even after the lockdown due to the fear of infection. The COVID-19 pandemic and its lockdown measures can be considered experimental setups, allowing for a better understanding of how users shift their consumption under new conditions. The emergency health measures and new social dynamics shaped the residential sector's energy behavior and its increase in electricity consumption. This article presents and analyzes the identified issues concerning residential electricity consumers and how their behaviors change based on the electricity consumption data during the COVID-19 period. The Dubai Electricity and Water Authority conducted a voluntary survey to define the profiles of its residential customers. A sample of 439 consumers participated in this survey and four years of smart meter records. The analysis focused on understanding behavioral changes in consumers during the COVID-19 period. At this time, the dwellings were occupied for longer than usual, increasing their domestic energy consumption and altering the daily peak hours for the comparable period before, during, and after the lockdown. This work addressed COVID-19 and the lockdown as an atypical case. The authors used a machine learning model and the consumption data for 2018 to predict the consumption for each year afterward, observing the COVID-19 years (2020 and 2021), and compared them with the so-called typical 2019 predictions. Four years of fifteen-minute resolution data and the detailed profiles of the customers led to a better understanding of the impacts of COVID-19 on residential energy use, irrespective of changes caused by seasonal variations. The findings include the reasons for the changes in consumption and the effects of the pandemic. There was a 12% increase in the annual consumption for the sample residents considered in 2020 (the COVID-19-affected year) as compared to 2019, and the total consumption remained similar with only a 0.2% decrease in 2021. The article also reports that machine learning models created in only one year, 2018, performed better by 10% in prediction compared with the deep learning models due to the limited training data available. The article implies the need for exploring approaches/features that could model the previously unseen COVID-19-like scenarios to improve the performance in case of such an event in the future. [ABSTRACT FROM AUTHOR]

Vehicle mileage is one of the key parameters for accurately evaluating vehicle emissions and energy consumption. With the support of the national annual vehicle emission inspection networked platform in China, this study used big data methods to analyze the activity level characteristics of the light-duty passenger vehicle fleet with the highest ownership proportion. We found that the annual mileage of vehicles does not decay significantly with the increase in vehicle age, and the mileage of vehicles is relatively low in the first few years due to the run-in period, among other reasons. This study indicated that the average mileage of the private passenger car fleet is 10,300 km/yr and that of the taxi fleet was 80,000 km/yr in China in 2019, and the annual mileage dropped by 22% in 2020 due to the pandemic. Based on the vehicle mileage characteristics, the emission inventory of major pollutants from light-duty passenger vehicles in China for 2010–2020 was able to be updated, which will provide important data support for more accurate environmental and climate benefit assessments in the future. [ABSTRACT FROM AUTHOR]

Airborne diseases cause high mortality and adverse socioeconomic consequences. Due to urbanization, more people spend more time indoors. According to recent research, air ventilation reduces long-range airborne transmission in indoor settings. However, air ventilation solutions often incur significant energy costs and ecological footprints. The trade-offs between energy consumption and pandemic control indoors have not yet been thoroughly analyzed. In this work, we use advanced sensors to monitor the energy consumption and pandemic control capabilities of an air-conditioning system, a pedestal fan, and an open window in hospital rooms, classrooms, and conference rooms. A simulation of an indoor airborne pandemic spread of Coronavirus (COVID-19) is used to analyze the Pareto front. For the three examined room types, the Pareto front consists of all three air ventilation solutions, with some ventilation configurations demonstrating significant inefficiencies. Specifically, air-conditioning is found to be efficient only at a very high energy cost and fans seem to pose a reasonable alternative. To conclude, a more informed ventilation policy can bring about a more desirable compromise between energy consumption and pandemic spread control. [ABSTRACT FROM AUTHOR]

A comfortable indoor environment contributes to a better quality of life and wellbeing for its occupants. The indoor temperature, lighting, and air quality are the main controlling factors of user comfort levels. The optimum control of the lighting, air conditioners, and air ventilators helps in maximizing the user's comfort level. Nonetheless, the energy consumption of these appliances needs to be taken into consideration to minimize the operational cost and at the same time provide an environmentally friendly system. Comfort level maximization and energy consumption minimization are optimization problems. This issue is becoming more important due to the lifestyle changes caused by the COVID-19 pandemic that resulted in more time spent at home and indoors. Inertia weight artificial bee colony (IW-ABC) algorithms using linearly increasing, linearly decreasing, and exponentially increasing inertia are proposed here for the optimization of the indoor comfort index and energy usage. The multi-objective problem is tackled as a weighted single objective optimization problem. The proposed solution is tested using a dataset of 48 environmental conditions. The results of the simulation show that the IW-ABC performs better than the original ABC and other benchmark algorithms and the IW-ABC with linear increasing inertia weight has the most improved convergence behavior. [ABSTRACT FROM AUTHOR]

This study comprehensively examines the effects of the COVID-19 pandemic on energy consumption in the United States. The purpose of the study is to quantify the effects of lockdowns and pandemic disruptions on energy consumption trends in order to inform policymakers and utilities on how to prepare for such events in the future. The study focuses on 2020 data collected by the Federal government. The effects are quantified using descriptive statistics. State-wise and sector-wise data have been presented using plots and heat maps. Related metrics like COVID case data, GDP, emissions, and expenditures were also presented. The total energy consumption fell by 7.5% in 2020. Besides Alaska, every state saw a decrease in energy, with some as high as 26%. The residential sector had the most states that saw an increase in energy, stemming from lockdowns and working from home. Similarly, petroleum consumption saw a decrease of 11.4% as a result of a decrease in travel. Biomass-related renewable energy generation fell by 23% due to decreased demand, while all other sources increased by 7.3%. Carbon dioxide emissions fell by 10.4%, methane by 2.8%, and nitric oxide by 6.7%. The overall per capita energy expenditure for the country dropped by 18.5%. There was a stronger correlation between GDP and energy consumption than between GDP and COVID case counts. The pandemic did not affect each state or sector evenly. The statistics and correlations presented here can be used in the ongoing effort to study the global impact of the pandemic and prepare for future challenges. [ABSTRACT FROM AUTHOR]

The aim of this article is to show the impact of the COVID-19 pandemic on electricity consumption and, consequently, on decisions regarding the installation of photovoltaic panels using the example of a selected local authority in Poland—the Szemud Municipality. The analysis was conducted in 2022 and covered the years 2019–2021. An attempt was made to explore the factors that may have triggered an increase in the use of solar energy in households and identify the determinants of installing photovoltaic panels in the period under analysis. Previous analyses of the PV market (and the impact of the pandemic on it) have so far focused on the market as a whole, either in macro or global terms, while studies on smaller municipalities have been limited to examining changes in electricity consumption levels during the COVID-19 pandemic and during lockdown. Therefore, a research gap was identified in that there are no studies analyzing the reasons for the shift from conventional to PV-assisted energy in households, with the COVID-19 pandemic as the background of these changes. The literature research showed that there are currently no studies attempting to establish a link between the increased interest in this type of energy by local authorities and the COVID-19 pandemic. The research confirmed the hypothesis of increased interest in household PV during the pandemic. The main conclusions of the study boil down to the need for further support as well as promotion of the use of solar energy. In addition, the results derived from the empirical research indicate the need to take action at a policy level to counter adverse trends regarding undesirable social behavior. [ABSTRACT FROM AUTHOR]

Background: The current COVID-19 pandemic has demonstrated the enormous importance of maintaining good hygienic conditions in everyday indoor environments for the prevention of infectious diseases. This includes sanitization methods capable of significantly reducing the microbial load in the air and on surfaces. However, in line with the ecological transition, alternative systems for environmental sanitization with reduced environmental impact are urgently needed. The photocatalytic reaction using UV-C light-emitting diode (UV-C LED) lamps with short wavelengths, especially in the range of 200–280 nanometers (nm), can significantly reduce the microbial load, safeguarding the environment thanks to reduced energy consumption. The objective of this review is to describe the latest innovations in the use of UV-C LED technology in the sanitization of indoor environments, reporting the fundamental principles on which its activity relies. Methods: Two databases (PubMed, Web of Science), were searched, following PRISMA guidelines. Results: A total of 1348 publications were identified, of which 379 were assessed in detail and, of these, 16 were included in the review. Conclusions: This literature review highlighted that UV-C LEDs irradiation represents a valid, eco-sustainable sanitization method that could be exploited as an alternative to chemical compounds to contain indoor microbiological pollution in living and working environments. [ABSTRACT FROM AUTHOR]

Energy is vital for the proper functioning of the various sectors of the economy and social life. During the pandemic, there have been some changes in these aspects that need to be investigated. The main objective of this article is to identify the direction of change caused by the COVID-19 pandemic in energy consumption and energy intensity in sectors and economic areas in EU countries. The specific objectives are to identify the importance of energy consumption in sectors and areas of the economy in individual EU countries; to determine the dynamics of change and variability during the pandemic in energy consumption in individual sectors and areas of the economy in EU countries, especially during the COVID-19 pandemic; to determine the changes in energy intensity of individual economic sectors and the differences in energy intensity between individual EU countries, including during the COVID-19 pandemic. Using a purposive selection method, all 27 EU Member States were selected for the study on 31 December 2020. The analysed period covered the years 2005–2020. The sources of material were literature and data from Eurostat. Descriptive, tabular and graphical methods, dynamic indicators with a fixed base and variable base, Gini coefficient, coefficient of variation, Pearson's linear correlation coefficient, and multi-criteria analysis were used for analysis and presentation. It was found that the structure of energy consumption had remained unchanged for several years, with transport, industry and households dominating. There were no significant differences between countries. The COVID-19 pandemic reduced energy consumption in all sectors of the economy, the largest in transport and services and the smaller in industry. At the same time, household energy consumption increased. As a result of the pandemic, there was an increase in energy intensity in all sectors of the economy, the largest in industry. Western European countries had a lower energy intensity of the economy than Central and Eastern European countries. There was little change over several years. Countries generally maintained their ranking. The pandemic did not change anything in this respect, meaning that it had a similar impact on individual EU countries. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a huge impact on society. Scientists are working to mitigate the impact in many ways. As a field closely related to human life, building engineering can make a great contribution. In this article, we started with the concept of the smart building as our guide. The impact of COVID-19 on daily energy consumption, information and communication technology, the ventilation of the interior environment of buildings, and the higher demand for new energy technologies such as electric vehicles is an entry point. We discuss how the concept of the smart building and related technologies (refrigeration, measurement, sensor networks, robotics, local energy generation, and storage) could help human society respond to the pandemic. We also analyze the current problems and difficulties that smart buildings face and the possible future directions of this technology. [ABSTRACT FROM AUTHOR]

This study explores the long-term energy use implications of electrification, automation and sharing of road vehicles in British Columbia, Canada. Energy use is first analyzed for the years 1990–2016 for forward forecasting, and hypothetical scenarios ranging from conservative to disruptive, incorporating various effects of road vehicle electrification, sharing and automation, as well as influences of other technology disruptions, such as online shopping and e-learning are presented and used to project the road transportation energy use in B.C. to 2060. Transportation energy use projections are compared to those of the Canadian Energy Regulator (CER). When considering only the effect of vehicle electrification, the scenarios show higher energy savings compared to CER's scenarios. The combined impact of vehicle electrification and automation leads to decreased energy use to 2060 for all scenarios considered. The energy savings for all scenarios, except for the conservative one, are higher than CER's projections. When the effects of vehicle electrification, automation and sharing are merged, all scenarios yield energy savings beyond the CER projections. Inclusion of other technology disruptions and the effects of pandemics like COVID-19 reduce transportation demand and provide further energy savings. The BAU scenario given in this study shows energy use decreases compared to 2016 of 26.3%, 49%, 62.24%, 72.1% for the years 2030, 2040, 2050, and 2060 respectively. [ABSTRACT FROM AUTHOR]

China promptly took the leading step to mitigate the spread of COVID-19, producing the first scientific guidelines assuming health above energy consumption and significantly changing HVAC/AHU operation. The research intended to fulfill the gap by measuring the impact of the guidelines on energy use intensity, CO2 emissions, and energy operation costs related to workplaces. The guidelines are long-term sector and industry trends following occupants' health and safety concerns, and today they are applied to nursing homes. The research extended the study to post-COVID-19 scenarios by crossing those settings with published reports on telework predictions. The methodology resorts to Building Energy Simulation software to assess the Chinese standard large office building on 8 climate zones and 17 subzones between pre- and post-COVID-19 scenarios under those guidelines. The outcomes suggest an upward trend in energy use intensity (11.70–12.46%), CO2 emissions (11.13–11.76%), and costs (9.37–9.89%) for buildings located in "warm/mixed" to "subarctic" climates, especially in colder regions with high heating demands. On the other hand, the figures for "very hot" to "hot/warm" climates lower the energy use intensity (14.76–15.47%), CO2 emissions (9%), and costs (9.64–9.77%). [ABSTRACT FROM AUTHOR]

The European Union has set itself ambitious emission-reduction targets—becoming carbon neutral by 2050. The member states' energy systems are increasingly switching to renewable and decentralized configurations, in line with the EU guidelines. This is accompanied by the support for "prosumers"—entities that both consume and generate renewable energy. In parallel, a number of prosumer support schemes are emerging as a result of the search for optimal development paths. At the same time, the COVID-19 pandemic has significantly affected the power market, causing huge anomalies mainly in demand and billing. The objective of the study was to assess the impact of the proposed changes in the prosumer schemes on the level of benefits prosumers obtained, including the performance of sensitivity analyses reflected in different levels and stability of electricity consumption resulting from extreme situations caused by the COVID-19 pandemic. Numerical computer simulations for five predefined prosumer support schemes were carried out in the R-project environment. The basic conclusion is that the prosumer benefits most from participating in the applicable discount mechanism, in which the unused energy is fed into a network storage, from which the prosumer can take 70% or 80% of the stored volume. The research also allows us to conclude that in Poland the COVID-19 pandemic has had a very significant impact on the level and profile of energy demand due to the introduction of restrictions on selected areas and economic sectors. The reduced demand is particularly visible in services (−58%), tourism and sport (−39%), and education (−19%). The analysis is an important contribution to the search for an optimal model of prosumer market development in Europe. [ABSTRACT FROM AUTHOR]

Although both aggregate and per capita energy consumption in Bangladesh is increasing rapidly, its per capita consumption is still one of the lowest in the world. Bangladesh gradually shifted from petroleum-based energy to domestically sourced natural-gas-based energy sources, which are predicted to run out within next two decades. The present study first identified the determinants of aggregate commercial energy and its three major components of oil, natural gas, and coal demand for Bangladesh using a simultaneous equations framework on an annual database covering a period of 47 years (1972–2018). Next, the study forecast future demand for aggregate commercial energy and its three major components for the period of 2019–2038 under the business-as-usual and ongoing COVID-19 pandemic scenarios with some assumptions. As part of a sensitivity analysis, based on past trends, we also hypothesized four alternative GDP and population growth scenarios and forecast corresponding changes in total energy demand forecast. The results revealed that while GDP and lagged energy demand are the major drivers of energy demand in the country, we did not see strong effects of own- and cross-price elasticities of energy sources, which we attributed to three reasons: subsidized low energy prices, time and cost required to switch between different energy-mix technologies, and suppressed energy demand. The aggregate energy demand is expected to increase by 400% by the end of the forecasting period in 2038 from its existing level in 2018 under the business-as-usual scenario, whereas the effect of COVID-19 could suppress it down to 300%. Under the business-as-usual scenario, the highest increase will occur for coal (3.94-fold), followed by gas (2.64-fold) and oil (2.37-fold). The COVID-19 pandemic will suppress the future demand of all energy sources at variable rates. The ex ante forecasting errors were small, varying within the range of 3.6–3.7% of forecast values. Sensitivity analysis of changes in GDP and population growth rates showed that forecast total energy demand will increase gradually from 3.58% in 2019 to 8.79% by 2038 from original forecast values. Policy recommendations include capacity building of commercial energy sources while ensuring the safety and sustainability of newly proposed coal and nuclear power installations, removing inefficiency of production and distribution of energy and its services, shifting towards renewable and green energy sources (e.g., solar power), and redesigning subsidy policies with market-based approaches. [ABSTRACT FROM AUTHOR]

The identification of critical periods and business cycles contributes significantly to the analysis of financial markets and the macroeconomy. Financialization and cointegration place a premium on the accurate recognition of time-varying volatility in commodity markets, especially those for crude oil and refined fuels. This article seeks to identify critical periods in the trading of energy-related commodities as a step toward understanding the temporal dynamics of those markets. This article proposes a novel application of unsupervised machine learning. A suite of clustering methods, applied to conditional volatility forecasts by trading days and individual assets or asset classes, can identify critical periods in energy-related commodity markets. Unsupervised machine learning achieves this task without rules-based or subjective definitions of crises. Five clustering methods—affinity propagation, mean-shift, spectral, k-means, and hierarchical agglomerative clustering—can identify anomalous periods in commodities trading. These methods identified the financial crisis of 2008–2009 and the initial stages of the COVID-19 pandemic. Applied to four energy-related markets—Brent, West Texas intermediate, gasoil, and gasoline—the same methods identified additional periods connected to events such as the September 11 terrorist attacks and the 2003 Persian Gulf war. t-distributed stochastic neighbor embedding facilitates the visualization of trading regimes. Temporal clustering of conditional volatility forecasts reveals unusual financial properties that distinguish the trading of energy-related commodities during critical periods from trading during normal periods and from trade in other commodities in all periods. Whereas critical periods for all commodities appear to coincide with broader disruptions in demand for energy, critical periods unique to crude oil and refined fuels appear to arise from acute disruptions in supply. Extensions of these methods include the definition of bull and bear markets and the identification of recessions and recoveries in the real economy. [ABSTRACT FROM AUTHOR]