ABSTRACT
The pandemic scenario caused by Covid-19 generated negative impacts. Covid-19 has made it clear that our daily lives depend to a high degree on access to energy. Therefore, now more than ever, it is necessary to promote new activities such as local food production, but also local energy capture. This article is an attempt to expose and quantify the benefits of a renewable energy transition in Ecuador post Covid-19 and post-oil. The generation, consumption, and reserves of oil in Ecuador were characterized, and the concept of energy transition was applied to evaluate the possibilities of integration of renewables, the progressive exit of thermal power plants, and future energy strategies. The year 2015 was taken as a basis and it was determined that energy use was 154.0 TWh / year, which corresponds to an end-user of approximately 147 TWh / year. The objective was to reduce this end-use demand to 80.0 TWh/year by 2055 through the integration of renewables and energy efficiency, for which 5 transition phases were planned until a 100% renewable system was obtained. It is concluded that the energy transition in Ecuador is technically possible and economically viable, without giving up the energy well-being that we currently enjoy. However, results show that even 100% renewable is not enough to face climate change.
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There is a growing concern about Indoor Environmental Quality (IEQ) in buildings as humans are spending longer in indoor environments, whether this is associated or not with climate change and vulnerability to extreme weather events. In the wake of the COVID pandemic, the need for indoor air quality control is likely to increase, the result of many adaptations in home environments to switch to remote work. In hot countries in the Global South, one of the alternatives is split A/C units with limited air renewal. While, odorless and colorless CO2, commonly generated by occupants through respiration, is among the relevant indoor air pollutants. The purpose of this study is to evaluate a low-cost, responsive air-renewal system in a climate chamber equipped with a standard split A/C unit. The results show the system's feasibility in curbing IAQ concerns and also highlight the risk of negative impacts on indoor thermal conditions and on energy consumption on using A/C. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Residential energy consumption forecasting has immense value in energy efficiency and sustainability. In the current work we tried to forecast energy consumption on residences in Athens, Greece. As a proof of concept, smart sensors were installed into two residences that recorded energy consumption, as well as indoors environmental variables (humidity and temperature). It should be noted that the data set was collected during the COVID-19 pandemic. Moreover, we integrated weather data from a public weather site. A dashboard was designed to facilitate monitoring of the sensors' data. We addressed various issues related to data quality and then we tried different models to forecast daily energy consumption. In particular, LSTM neural networks, ARIMA, SARIMA, SARIMAX and Facebook (FB) Prophet were tested. Overall SARIMA and FB Prophet had the best performance. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
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Community greenhouses are important for the production of local food and reduction of food supply insecurities within cities. As we've seen with Covid-19, pandemics highlight the criticality of local food access to underprivileged urban districts. Since almost 60 % of the energy used in greenhouses is spent in heating and cooling, ground heat exchangers (GHEs) can play a significant role in supplying temperature regulation, but geothermal heat pump systems tend to be expensive for community organizations. An efficient way to reduce GHEs installation costs is to dig trenches to install the system horizontally and cover a part of heating and cooling loads only. In order to ensure cost effectiveness and optimize operations, this type of system was studied for urban greenhouses where ground space can be limited. Sizing calculations were performed for GHEs of a 7.62 m x 15.24 m greenhouse located on the Island of Montreal where the annual, monthly, and hourly energy consumption were estimated from previous building simulations. Three scenarios were used to specify sizing of the system in terms of excavation dimensions and percentage of the greenhouse peak loads covered;(1) number and length of trenches required for a horizontal GHE (HGHE) covering 100% of cooling and heating loads;(2) number and length of trenches required for an HGHE to cover 100% of peak heating loads and 60% of peak cooling loads and;(3) the percentage of heating and cooling peak loads that can be covered by an HGHE located under the greenhouse with similar dimensions (around 116 m2). Estimated excavation dimensions for cases 1 and 2 are 51.8 m x 8 m (414.4 m2) and 40.8 m x 8 m (326.4 m2). Estimated percentage of peak loads covered for case 3 is 40% of heating peak loads and 30% of cooling peak loads. © 2022 Geothermal Resources Council. All rights reserved.
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In recent work, a Hierarchical Bayesian model was developed to predict occupants' thermal comfort as a function of thermal indoor environmental conditions and indoor CO2 concentrations. The model was trained on two large IEQ field datasets consisting of physical and subjective measurements of IEQ collected from over 900 workstations in 14 buildings across Canada and the US. Posterior results revealed that including measurements of CO2 in thermal comfort modelling credibly increases the prediction accuracy of thermal comfort and in a manner that can support future thermal comfort prediction. In this paper, the predictive model of thermal comfort is integrated into a building energy model (BEM) that simulates an open-concept mechanically-ventilated office space located in Vancouver. The model predicts occupants' thermal satisfaction and heating energy consumption as a function of setpoint thermal conditions and indoor CO2 concentrations such that, for the same thermal comfort level, higher air changes per hour can be achieved by pumping a higher amount of less-conditioned fresh air. The results show that it is possible to reduce the energy demand of increasing fresh air ventilation rates in winter by decreasing indoor air temperature setpoints in a way that does not affect perceived thermal satisfaction. This paper presents a solution for building managers that have been under pressure to increase current ventilation rates during the COVID-19 pandemic. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.
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As the operation of buildings accounts for around 30% of global CO2 emissions, reducing their energy consumption is considered crucial for climate change mitigation. Aware of this significance, the sustainable HCI (SHCI) community has conducted research on energy consumption for over 15 years. However, compared with domestic environments, commercial organisations are comprised of complex mixed-use buildings, and the socio-technical understanding of space and resulting energy use are relatively under-explored. In this late-breaking work, we present the initial findings of a longitudinal analysis that uses building energy data from a period covering the COVID-19 lockdown measures to help identify the energy associated with these buildings and their users. Viewing the pandemic as a unique, grand-scale 'energy intervention', the resulting consumption patterns are used to inform questions about leverage points for achieving change, stakeholder agency vs. infrastructure demand;and highlight the importance of putting energy data in context. © 2023 Owner/Author.
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Owing to the COVID-19 pandemic, many companies have introduced working from home to avoid the risk of infection. In this study, we conducted questionnaire surveys and analysed the building energy management system (BEMS) in an office building where the number of employees working from home increased after the onset of the pandemic. The influence of working from home on the indoor environment satisfaction and the variability in energy consumption at home and office was determined. The indoor environment satisfaction was significantly higher when working from home than when working at the office. In 2020, the total energy consumption at home and office decreased by 30% in April and increased by 22% in August compared to the previous year. To work from home while saving energy regardless of the season, it is necessary to reduce office energy consumption by decreasing the number of workers present at the office. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.
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Concerns about the effects of global warming and predicted rising sea levels are radically changing government policies to lower carbon emissions using sustainable green technologies. The United Kingdom aims to reduce its carbon emissions by 78% by 2035 and achieve net zero by 2050. This is a major driver for energy management and is influencing development of buildings which use autonomous smart technologies to assist in lowering carbon footprints. These Smart Buildings use digital technologies by connecting sensor data with intelligent systems which can be monitored remotely to provide more efficient facilities management. The data harvested and transmitted from the IoT sensors provides a key component for Big Data Analytics using techniques such as Association rule mining for intelligent interpretation which can assist facilities management becoming more agile regarding office space utilization. The shift toward hybrid working particularly instigated by the COVID-19 pandemic and recent energy supply concerns caused by the Ukraine crisis presents facilities management with opportunities to optimize their space, reduce energy consumption, and allow them to identify commercial opportunities for the unused space throughout the building. This chapter discusses the use of association rules for data mining derived from a simulated dataset for an investigative analysis of office workflow patterns for facilities management operations, resource conservation, and sustainability. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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COVID-19 has made working from home (WFH) a widely prevalent mode of work, resulting in highly complex changes of energy and water consumption in buildings. To understand these changes, this study applies the concept of water-energy nexus (WEN) in the analysis of energy and water data in residential and non-residential buildings in Ontario, Canada, before and during the pandemic. The study found the overall energy and water consumption of buildings exhibited a decreasing trend, with the most significant change found in water consumption. Energy and water consumption increased in residential buildings but decreased in non-residential buildings;the changes in energy and water consumption were synchronized over the WFH period. This study also elucidated that changes were related to the demographic and job attributes. When dealing with the peak load of residential consumption with a high consumption benchmark, due consideration should be given to the stronger synchronization of the two types of resources to improve the resilience of residences to cope with the uncertainty of unexpected large-scale public health crisis. Applying WEN to building resource consumption during WFH for the first time, the findings shed light on the need to enhance integrated water and energy management. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
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To achieve carbon emission reduction target (CERT) by 2030 and carbon-neutrality in 2050, it is important to actively reduce the emission gap in the private building sector. However, the ongoing COVID-19 pandemic and the Russian-Ukraine war are threatening the green remodeling policy (GRP) worldwide. Therefore, this study analyzed energy consumption savings, GHG emission reduction, and net present value when applying green remodeling to a private building to predict whether or not the current GRP could achieve 2030 CERT and 2050 carbon-neutrality. The main findings are as follows. First, yearly electricity and gas consumption of 84.97 m2 type households can be reduced by 6.19% and 15.58% through green remodeling. Second, based on the energy saving, yearly GHG emission can be reduced about 0.34tCO2eq. Third, the economic feasibility of green remodeling cannot be achieved via the current policy, and NPV17 decreases up to USD-51,485 depending on the credit loan interest rate and the green remodeling interest subsidy program. In other words, it is difficult to reach 2030 CERT and 2050 carbon-neutrality via the current policy. Therefore, the South Korean government is required to reorganize financial policies, establish active systems, increase public awareness of the policy, and improve energy efficiency technology. © 2023 Elsevier B.V.
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UV-LED irradiation has attracted attention in water and wastewater disinfection applications. However, no studies have quantitatively investigated the impact of light intensity on the UV dosage for the same magnitude of disinfection. This study presents a powerful 280 nm UV-LED photoreactor with adjustable light intensity to disinfect municipal wastewater contaminated with E. coli, SARS-CoV-2 genetic materials and others. The disinfection performance of the 280 nm LED was also compared with 405 nm visible light LEDs, in terms of inactivating E. coli and total coliforms, as well as reducing cATP activities. The results showed that the UV dose needed per log reduction of E. coli and total coliforms, as well as cATP, could be decreased by increasing the light intensity within the investigated range (0–9640 µW/cm2). Higher energy consumption is needed for microbial disinfection using the 405 nm LED when compared to 280 nm LED. The signal of SARS-CoV-2 genetic material in wastewater and the SARS-CoV-2 spike protein in pure water decreased upon 280 nm UV irradiation. © 2023 by the authors.
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The Covid-19 outbreak has resulted in new patterns of home occupancy, the implications of which for indoor air quality (IAQ) and energy use are not well-known. In this context, the present study investigates 8 flats in London to uncover if during a lockdown, (a) IAQ in the monitored flats deteriorated, (b) the patterns of window operation by occupants changed, and (c) more effective ventilation patterns could enhance IAQ without significant increases in heating energy demand. To this end, one-year's worth of monitored data on indoor and outdoor environment along with occupant use of windows has been used to analyse the impact of lockdown on IAQ. Moreover, using on-site CO2 data, monitored occupancy and operation of windows, the team has calibrated a thermal performance model of one of the flats to investigate the implications of alternative ventilation strategies. The results suggest that despite the extended occupancy during lockdown, occupants relied less on natural ventilation, which led to significantly higher CO2 and PM10 concentrations. However, simple natural ventilation patterns or use of mechanical ventilation with heat recovery proves to be very effective to maintain acceptable IAQ. © International Building Performance Simulation Association, 2022
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Decarbonization, decentralization, and digitalization are the prominent paths for the energy sector in the future. The rise of smart meters across consumers, and industries led to a massive collection of fine-grained energy and electricity consumption-related data. A data science challenge is to analyze the Smart Meter data for the benefit of both the energy providers and the consumers. In this paper, An attempt has been made to analyze the smart meter collected from the IIT Hyderabad campus and presented the analysis into descriptive, predictive, and prescriptive analytics. The data collected from more than 50 meters over a period of one year have been analyzed and results obtained. Interesting trends such as the impact of COVID-19 on campus energy consumption have been examined. The framework for energy data analytics presented in this paper will be useful for any campus in general, and the recommendations presented will save energy expenses. © 2023 IEEE.
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This case study examines the Indian Institute of Technology Gandhinagar (IITGN) campus's monthly energy consumption profile in detail to understand how it varies according to academic calendar, seasonal variability, and the recent COVID 19 pandemic. In addition, a detailed assessment of the electricity bill and its sub-component calculations are intended to understand how the energy consumption pattern affects the overall monthly electricity bill. From this study, it is observed that the energy consumption of academic areas, hostel areas, and chiller plants account for 80-90% of total energy consumption. The on-site solar PV energy generation at IITGN campus accounts for 1014% of total monthly energy consumption, which varies greatly by season. The analyses performed in this paper were inferred by three years of historical data of actual energy consumption and monthly electricity bills. Based on the analysis presented in this paper some recommendations towards the energy conservation measures are also given. © 2022 IEEE.
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This study measures the total factor carbnon dioxide (CO2) emissions performance of the metal industry, iron and steel, nonferrous metal, and metal processing industries in 39 Japanese prefectures from 2008 to 2019. The true fixed-effects panel stochastic frontier model identifies regional carbon efficiency as well as the inefficiency determinants. The main results are as follows. First, a decrease in the coal ratio and an increase in the electricity ratio in total energy consumption improves efficiency. This result suggests that electrification in the metal industry, especially conversion from blast furnaces to electric furnaces in the iron and steel industry, contributes to reducing carbon emissions. Second, industrial agglomeration improves carbon emissions performance in the metal industry. This implies that agglomeration and decarbonization policies focusing on there are more effective, rather than a uniform national policy. Third, compared to the cumulative CO2 emissions over the sample period, 49,017 × 103 tons, the cumulative CO2 mitigation potential is 29,703 × 103 tons, indicating that CO2 emissions can be reduced by 60.6% without affecting the output. Forth, to examine the green economic recovery with efficiency in Japan's metal industry after COVID-19, we present a simple scenario analysis where a k% replacement coal ratio with an electricity ratio in total energy consumption, assuming that each prefecture will achieve the maximum CO2 emission amount during the sample period. By replacing 10% of the coal ratio with the electricity ratio, CO2 emissions can be reduced by 23.0%. In the case of a 20% replacement, CO2 emissions can be reduced by 33.0%. Our results show that Japan's targets in the post-COVID-19 green recovery process should be a decrease in coal consumption, an increase in electricity, and industrial agglomeration. © 2023 Elsevier Ltd
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Most office buildings regulate their indoor climate through conservative setpoints and schedules based on maximum occupancy. However, the occupancy of most office buildings rarely exceeds 50%. Buildings' lack of adaptability to partial occupancy exacts a toll on their energy use by providing building services excessively and inefficiently. Largely vacant office buildings left in the wake of the COVID-19 pandemic present a natural experiment to evaluate the extent of this problem. Three-parameter univariate changepoint models were employed to estimate the change in energy use before and during the pandemic in two institutional office buildings: one with occupancy-based ventilation, and one with traditional ventilation. A calibrated energy model was developed for the traditional building to determine how much energy could have been saved if occupancy-based ventilation was implemented. It was found that the building could have saved up to 32% and 12% for heating and cooling, respectively, in the pre-pandemic period alone. © International Building Performance Simulation Association, 2022
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Ventilation improves indoor air quality and reduces airborne infections. It is particularly important at present because of the COVID-19 pandemic. Commercially available ventilation facilities can only be instantly turned on/off or at a set time with adjustable air volumes (high, middle, and low). However, maintaining the indoor carbon dioxide concentration while reducing the energy consumption of these facilities is challenging. Hence, this study developed clustering algorithms to determine the carbon dioxide concentration limit thus enabling real-time air volume adjustment. These limit values were set using the existing energy recovery ventilation (ERV) controller. In the experiment, dual estimation was adopted, and the constructing building energy models from data were sampled at a low rate to compare that the ventilation facilities are only turned on/off. In addition, switching control is closely related to fuzzy control;that is, fuzzy control can be considered a smooth version of switching control. The experimental results indicated that the limits of 600 and 700 ppm were suitable to effectively control the real-time air volume based on the ERV operation. An ERV-based carbon dioxide concentration limit reduced the energy consumption of ventilation facilities by 11%implications of this study. IEEE
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An emergency caused by the COVID-19 pandemic affected the rethinking of existing healthcare systems. The increased need for hospital beds appeared short after the outbreak of the pandemic and the solution was to adapt the existing buildings, primarily public ones. Among all, sports buildings, i.e. sports halls were successfully used around the globe for conversion into hospitals. Topic of the paper was to investigate whether sports halls in Serbia, which were also used as temporary hospitals, are suitable for conversion in terms of energy consumption needed for achieving thermal comfort. Two case studies were analysed. The energy simulations were done using the DesingBuilder software. The results of thermal comfort summary and energy consumption led to the conclusion that this building type in Serbia could be successfully used for hospital purposes. Although the multiple increase in energy consumption was noted during the heating period, the results were within the limits required by both national and international standards. © 2023 Society of Thermal Engineers of Serbia Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions
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Accurate energy consumption prediction is critical for proper resource allocation, meeting energy demand, and energy supply security. This work aims at developing a methodology for accurately modeling and predicting electricity consumption during abnormal long-lasting events, such as COVID-19 pandemic, which considerably affect consumption patterns in different types of premises. The proposed methodology involves three steps: (A) selects among multiple models the most accurate one in energy consumption prediction under normal conditions, (B) uses the selected model to analyze the impact of a specific abnormal event on energy consumption for various classes of premises, and (C) investigates which features contribute most to energy consumption prediction for abnormal conditions and which features can be added to improve such predictions.We use COVID-19 as a case study with datasets obtained from Fort Collins Utilities, which contain energy consumption data for residential and different sizes of commercial and industrial premises in the city of Fort Collins, Colorado, USA. We also use temperature records from NOAA and COVID-19 public orders from Larimer County.We validate the methodology by demonstrating that the methodology can help design a model suited for the pandemic situation using representative features, and as a result, accurately predict the energy consumption. Our results show that the MLP model selected by our methodology performs better than the other models even when they all use the COVID-related features. We also demonstrate that the methodology can help measure the impacts of the pandemic on the energy consumption. © 2022 IEEE.
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The increasing dependence on renewable energy particularly solar Photovoltaic (PV) to supply energy consumption needs in Jordan has placed operational challenges on the power system operator to cope with the significant drop in the system's net-demand and the reduction in synchronous inertia. These challenges were not expected to become critical until the penetration of renewables increases to meet future national energy targets in the forthcoming years. However, the adoption of lockdowns to restrict the outbreak of COVID-19 combined with PV injections reduced the system's net-demand particularly during daytime in spring 2020 like expected levels in the future with high PV penetration. Thus, the implications of future significant penetration of renewables on system security could be better understood based on the operating conditions during lockdowns. In particular, it is important to assess the system's frequency adequacy during emergency events that might be occurred whilst running a low-inertia power system. To do so, this paper provides a detailed dynamic frequency analysis of the Jordanian power system during lockdowns using Power Factory software. The results highlight the importance of energy curtailment of renewables to maintain adequate level of synchronous inertia to maintain security when the system is islanded without interconnections to neighboring countries. However, deciding the proper level of curtailment requires performing dynamic analysis to ensure that both the Rate of Change of Frequency (RoCoF) and the minimum frequency level during generation contingency events will not trigger the Under Frequency Load Shedding (UFLS) relays. © 2022 IEEE.