Changes In Energy Consumption Research Articles

Toward an Environmentally Friendly Future: An Overview of Biofuels from Corn and Potential Alternatives in Hemp and Cucurbits

Increased energy consumption and climate change, driven by greenhouse gas emissions, pose significant risks to global sustainability. Concerns about using agricultural land for fuel production and its competition with food production have made feedstocks like corn (Zea mays) highly controversial. This study explores the potential of alternative feedstocks, such as hemp (Cannabis sativa) and cucurbits (family Cucurbitaceae), for biofuel production amidst environmental concerns linked to fossil fuel usage. Hemp is widely acknowledged as a promising feedstock for sustainable biorefinery due to its agricultural adaptability and its ability to produce oil and carbohydrates. Cucurbits seeds are characterized by a high oil content, which can be utilized in the food industry or for energy production as biofuel. As a byproduct of cucurbits processing, a significant number of seeds often remains, which constitutes waste. By examining hemp and cucurbit byproducts and waste, which are suitable for bioenergy production, this research highlights the promise these alternative feedstocks hold for the biofuel industry. Utilizing these resources presents a viable route to diminish dependence on fossil fuels and transition toward a more environmentally sustainable energy future.

Joint routing and computation offloading based deep reinforcement learning for Flying Ad hoc Networks

Flying ad-hoc networks (FANETs) consisting of multiple Unmanned Aerial Vehicles (UAVs) are widely used due to their flexibility and low cost. In scenarios such as crowdsensing and data collection, data collected by UAVs are transmitted to base stations for processing and then sent to data centers. Still, the deployment of base stations is costly and inflexible. To address this issue, this paper introduces a position-based Computing First Routing (CFR) protocol designed for efficient task transmission and computation offloading in FANETs. This protocol facilitates task processing during data transfer and ensures the delivery of fully processed results to the data center. Considering the dynamically changing topology of the FANETs and the uneven distribution of the UAVs’ computation power, deep reinforcement learning is used to make multi-objective decisions based on the Q-values computed by the model. FANETs are centerless clusters, and two-hop neighbor tables containing position and computing power information are used to make less costly decisions. Simulation experiments demonstrate that CFR outperforms other benchmark schemes with an approximately 6% higher packet delivery rate, an approximately 21% reduction in end-to-end delay, and about a 34% decrease in total cost. Furthermore, it effectively ensures the completion of task offloading before reaching the destination node. This occurs due to the design of a hierarchical reward function that takes into account dynamic changes in delay and energy consumption, as well as the injection of neighbor computing power information into the two-hop neighbor table.

Modeling and predicting energy consumption of chiller based on dynamic spatial-temporal graph neural network

As an important equipment in the HVAC system, the chiller accounts for 50–60 % of the total energy consumption. According to the characteristics of chiller operation data and using the multivariate time series (MTS) forecasting method, a novel chiller energy consumption prediction model (T-CADGCN) is proposed, which is based on the temporal convolution (TC) and the dynamic graph convolution (DGC) network with a channel attention (CA) mechanism. Two datasets are used to verify the prediction performance of different models. Compared with the actual energy consumption, the average MAPEs of the T-CADGCN model on the measured dataset and simulated dataset are 0.92 % and 0.08 %, respectively. Whereas they are 2.8 % and 0.91 % for LSTM, 11.45 % and 11.74 % for MTGNN_GCA, 12.3 % and 12.63 % for MTGNN, respectively. The results demonstrate that the T-CADGCN model can effectively enhance the prediction performance of chiller energy consumption, followed by LSTM, then MTGNN_GCA, and finally MTGNN. Besides, the prediction performance on different future time steps is also conducted on two datasets. It can be found that all models decrease in prediction accuracy with the increase of the prediction time steps, but the T-CADGCN model can maintain a relatively good prediction performance with high robustness. The changes of in-degree and out-degree in the chiller energy consumption node in the dynamic graph construction module indicate that the factors affecting and being affected by chiller energy consumption change over time. This study can help to predict the energy consumption of chiller accurately and thus contributes to building energy management.

Disparity and driving forces of energy consumption in China's provincial urban residential sector under the carbon neutrality target

China's escalating urbanization contributes significantly to carbon emissions, necessitating an efficient, equitable decarbonization strategy in the urban residential sector. However, an overview of the regional-inequal implications underlying this sector's carbon-neutral pathways is, to our knowledge, still pending. Here, we employ a province-level bottom-up technology optimization model to evaluate energy consumption implications and driving forces, particularly inter-provincial disparities, under various carbon neutrality pathways. We have observed that regional disparities in energy access are largely determined by different carbon cap allocation strategies. Additionally, energy intensity is the key factor influencing changes in energy consumption in scenarios of carbon neutrality compared to scenarios without carbon policies. Furthermore, energy intensity is linked to regional energy consumption disparities across scenarios, with short-term impacts mainly through energy intensity and long-term impacts through energy structure. The study presents a cost-minimizing, equitable carbon neutrality pathway for China's urban residential sector at the provincial level, highlighting that equitable carbon reduction policies promote provincial energy consumption equity compared to cost-efficient strategies. Additionally, the analysis offers guidance for formulating and implementing equitable energy transition policies.

Identification of carbon responsibility factors based on energy consumption from 2005 to 2020 in China

In the process of rapid economic development, the relationship between energy consumption and economic development is close and the coupling relationship is complex. The study proposes a method for the assessment of integrated energy consumption factors. In particular, production and consumption driving factors are expressed in relation to the growth of primary energy consumption responsibility in the energy supply chain system. Specifically, based on previous research, the study builds a LMDI decomposition model based on the primary energy consumption responsibility - embodied energy (PECREE) formula. By analyzing the impact of changes in the final commodity quantity, value-added allocation factor, energy intensity, end-use energy consumption structure and primary energy consumption responsibility conversion factor on primary energy consumption responsibility, the study systematically reveals the linkage relationship between product production and consumption on energy consumption changes, and reveals the related driving mechanism of primary energy consumption responsibility growth. We choose China's energy supply system from 2005 to 2020 as a case study, focusing on the impact of changes in economic sectors on primary energy consumption responsibility, and provide a supplementary explanation from the commodity consumption side. The results show that the change of final use quantity is the most important factor causing the increase of primary energy consumption responsibility, but the impact is decreasing year by year. The impact of energy intensity change on primary energy consumption responsibility is mainly dominated by economic sectors with high energy consumption and high energy intensity. The impact of value-added allocation factor requires systematic consideration of energy intensity of economic sectors. The increase of the proportion of electricity in the end-use energy consumption structure of the economic sector promotes the growth of its primary energy consumption responsibility. The reduction of primary energy consumption responsibility conversion factor for electricity is one of the factors that inhibits the growth of primary energy consumption responsibility. Finally, we believe that while meeting the needs of social development and production and life of the people, China is already on the road of adjustment and transformation of its industrial structure and energy structure, and will continue to make progress in the future.

Comprehensive investigation of capacitive deionization cells by response surface methodology

Freshwater resources are limited; thus, different technologies for desalination of brackish and seawater have been developed. Capacitive deionization (CDI) is one of the most promising desalination technologies owing to its low cost, simplicity, nonmoving parts, and low energy consumption. However, its energy consumption significantly increases with desalination of high-salinity water. In this regard, a good understanding of the effects of different process parameters on CDI energy consumption is crucial for further performance improvement. Therefore, the surface response methodology (RSM) was adopted to systematically study the effects of applying voltage, Stern layer capacity, volume and concentration of feed water, and volume of micropores on energy consumption and desalination time. The results showed that the Stern layer capacity and volume of micropores reduced desalination time by approximately 25% without significant changes in energy consumption. With the design of an improved electrode (improving the micropore structure for ion adsorption), the desalination time (Cycle) was reduced and a slight reduction in energy consumption was achieved. It can be claimed that any improvement in the physical (porosity) and electrical properties of the electrodes, results in an important increase in the performance of the CDI system.

Impact of cannabidiol on brain glucose metabolism of C57Bl/6 male mice previously exposed to cocaine.

Despite evidence of the beneficial effects of cannabidiol (CBD) in animal models of cocaine use disorder (CUD), CBD neuronal mechanisms remain poorly understood. This study investigated the effects of CBD treatment on brain glucose metabolism, in a CUD animal model, using [18F]FDG positron emission tomography (PET). Male C57Bl/6 mice were injected with cocaine (20 mg/kg, i.p.) every other day for 9 days, followed by 8 days of CBD administration (30 mg/kg, i.p.). After 48 h, animals were challenged with cocaine. Control animals received saline/vehicle. [18F]FDG PET was performed at four time points: baseline, last day of sensitization, last day of withdrawal/CBD treatment, and challenge. Subsequently, the animals were euthanized and immunohistochemistry was performed on the hippocampus and amygdala to assess the CB1 receptors, neuronal nuclear protein, microglia (Iba1), and astrocytes (GFAP). Results showed that cocaine administration increased [18F]FDG uptake following sensitization. CBD treatment also increased [18F]FDG uptake in both saline and cocaine groups. However, animals that were sensitized and challenged with cocaine, and those receiving only an acute cocaine injection during the challenge phase, did not exhibit increased [18F]FDG uptake when treated with CBD. Furthermore, CBD induced modifications in the integrated density of NeuN, Iba, GFAP, and CB1R in the hippocampus and amygdala. This is the first study addressing the impact of CBD on brain glucose metabolism in a preclinical model of CUD using PET. Our findings suggest that CBD disrupts cocaine-induced changes in brain energy consumption and activity, which might be correlated with alterations in neuronal and glial function.

Experimental investigation, performance analysis, and optimization of hot air convective drying of date fruits via response surface methodology

A laboratory-scale hot air convective dryer (HACD) was used to dry date fruits surplus at 0.5 ± 0.02 kg kg−1 to a safe moisture level of 0.35 kg kg−1 under drying temperatures (T) of 50–70 °C with airflow rates (Q) of 0.18 and 0.28 m3 h−1. Kinetics, modeling, energy, economic feasibility, and color difference of the date fruits were analyzed and then optimized using response surface methodology (RSM) with two drying factors and eight responses. The drying time to reach the desired moisture level ranged from 9 to 28 h under different drying conditions. The proposed mathematical model expressed the moisture behavior over drying time with the highest R2 = 0.9998 and 0.9999, respectively, for 0.18 and 0.28 m3 h−1. Effective moisture diffusivity (Deff) was calculated using the starting accessibility parameter and varied between 9.9 × 10−8 and 3.3 × 10−7 m2 s−1. The energy analysis revealed that the specific energy consumption (SEC) varies between 22.04 and 43.05 kWh.kg−1 and the drying efficiency (η) are 16.91, 16.14, and 25.38% for 50, 60, and 70 °C, respectively. The RSM optimization using a multi-level design with two centurium points denoted the optimum responses for the highest drying temperature. The lowest payback period (Pb) of the drying system was estimated to be 0.45 years for T = 70 °C and Q = 0.28 m3 h−1. The optimum drying time, Deff, total energy consumption (Et), SEC, η, and total color change (ΔE) were estimated to be 9 h, 3.3 × 10−7 m2 s−1, 33.91 kWh, 22.04 kWh.kg−1, 27.69%, and 1.69, respectively.

A life cycle assessment of clinker and cement production in Ethiopia

Cement production is a major consumer of energy and the largest source of industrial CO2 emissions. This study aims to perform an environmental life cycle assessment of clinker and cement production in Ethiopia, using ReCiPe impact assessment method. Inventory data (material, energy, and transportation) is collected from seven major Ethiopian cement industries. The midpoint analysis identified nine hotspot environmental concerns: global warming, ozone formation (human health and terrestrial ecosystem), particulate matter formation, terrestrial (acidification and ecotoxicity), freshwater eutrophication, human carcinogenic toxicity, and fossil resource scarcity. Human health emerged as the most significantly affected endpoint damage category by the midpoint impacts. Among the process stages included in clinker system boundary, clinker production phase (kiln emissions) is a significant contributor to the total score of the hotspot impacts, ranging from 60.7% to 91.8%. The clinker system is responsible for over 81.03% of the overall environmental burden of cement. The sensitivity analysis reveals that a 5% change in kiln energy consumption and transportation burden could lead to a reduction in hotspot impacts ranging from 1.8% to 5%. To foster reliability of this study, uncertainty analysis is also conducted. Overall, the findings indicate the need to enhance environmental sustainability in Ethiopian cement production.

The impact of ICT infrastructure, technological innovation, renewable energy consumption and financial development on carbon dioxide emission in emerging economies: new evidence from Vietnam

PurposeThis study is intended to analyze the impact of information and communication technology (ICT) infrastructure, technological innovation, renewable energy consumption and financial development on carbon dioxide emissions in emerging economies.Design/methodology/approachThe present study adopts the autoregressive distributed lag (ARDL) cointegration technique for the annual data collection of Vietnam from 1990 to 2020.FindingsThe results of the study unveil that renewable energy consumption, the interaction between renewable energy consumption and ICT infrastructure and financial development have significant predictive power for carbon dioxide emissions. In the long term, renewable energy consumption, export and population growth reduce CO2 emissions, whereas the interaction between renewable energy consumption and ICT infrastructure and financial development increases CO2 emissions, while ICT infrastructure does not affect emissions. In the short run, changes in ICT infrastructure contribute to carbon dioxide emissions in Vietnam. In addition, changes in renewable energy consumption, financial development, the interaction between ICT infrastructure and renewable energy consumption and population growth have a significant effect on CO2 emissions. Notably, technological innovation has no impact on CO2 emissions in both the short and long run.Originality/valueThe current study provides new insights into the environmental effects of ICT infrastructure, technological innovation, renewable energy consumption and financial development. The interaction between renewable energy consumption and ICT infrastructure has a significant effect on carbon dioxide emissions. The paper suggests important implications for setting long-run policies to boost the effects of financial development, renewable energy consumption and ICT infrastructure on environmental quality in emerging countries like Vietnam in the coming time.

Relationship between Energy and Economic Growth: Evidence from a Panel Nonlinear ARDL Model

In this article, we study the heterogeneous relationship among energy consumption and economic growth by incorporation gross fixed capital formation, foreign direct investment, international trade and finance development as control variables in a selected sample of 11 countries of the Middle East and North Africa over the period 1980-2020 using a panel nonlinear autoregressive distributed lag model. Empirical results show the presence of an asymmetric long run relationship between energy consumption and economic growth in the group of countries. The Pooled Mean Group estimates indicate that positive and negative changes in energy consumption have positive and significant effects on output growth in the long term, whereas both Mean Group and Difference Fixed Effect estimates report that only negative changes in energy consumption have positive and significant effect on long-term economic growth in the selected sample of countries. In addition, the short run individual effects of energy use on economic growth are positive and significant for only three countries (Jordan, Saudi Arabia, and Tunisia). These results are very important for the design of energy policies and sustainable economic growth. Energy-saving policies are suitable for long-run economic growth for the entire sample, while they are only suitable for eight countries out of eleven in the short-run.

Municipal solid waste incineration bottom ash recycling assessment: Carbon emission analysis of bottom ash applied to pavement materials

Inspired by the concept of conserving resources, bottom ash has been increasingly utilized in road construction as a readily available waste material. This study sought to address this gap by quantifying the carbon benefits associated with bottom ash application. Through a comprehensive analysis grounded in life cycle theory, author evaluated the changes in energy consumption and carbon emissions before and after the integration of bottom ash into the road construction process. The analysis employed the raw material production stages of different pavement materials as the system boundary. By determining the optimal amount of bottom ash and calculating the associated values, the study proposed an efficient application scheme for utilizing bottom ash in urban engineering construction projects. The results showed that bottom ash had the least carbon reduction effect in permeable bricks, followed by cement concrete and cement stabilized macadam mixture. The most significant effect was observed in asphalt mixtures. However, energy consumption increased with the addition of bottom ash in asphalt mixtures, whereas it decreased in the other three products. The carbon reduction after applying bottom ash was predicted based on the annual growth rate of urban road construction. Under the premise of maintaining the non-destructive application performance of asphalt mixtures, the optimal carbon reduction benefit for a 20% addition of bottom ash was approximately 17,369.9 tons. In cement-stabilized crushed stone mixtures, the optimal carbon reduction benefit ws achieved with a bottom ash content of 30%, approximately 15,797.7 tons. The practice of utilizing bottom ash in road works was significant in terms of maximising the benefits of carbon reduction.

An improved deep Q-network approach for charging sequence scheduling with optimal mobile charging cost and charging efficiency in wireless rechargeable sensor networks

Mobile charging is a practical solution to overcome energy constraints in wireless rechargeable sensor networks (WRSNs). It utilizes a mobile charger (MC) to charge sensors based on the wireless power transfer technique via a charging scheduling scheme. However, how to schedule MC to achieve higher charging efficiency with less mobile charging cost, while ensuring network functionality remains a challenge. Specifically, the mobile charging cost is the total energy consumed by the MC during the charging task. This paper studies the charging sequence scheduling problem with optimal mobile charging cost and charging efficiency (CSCE), considering dynamic changes in sensor energy consumption. Moreover, we model the CSCE as a multi-objective mixed integer nonlinear programming. To address this issue, we propose an improved deep Q network approach for CSCE (IDQN-CSCE), where the MC acts as an agent to explore the WRSN and determine a charging policy based on the charging demand of sensors. IDQN-CSCE employs Q-learning and deep Q-networks (DQN) to train the Q-table and DQN network simultaneously. Charging actions are selected based on probability, and the probability of using Q-learning decreases as iteration steps increase. Additionally, we design a novel reward function consisting of a weighted sum of rewards and penalties fed back by the environment after performing a charging action. Simulation results show that IDQN-CSCE achieves higher charging efficiency than other baseline approaches with less mobile charging cost.

‘Greening’ an Oil Exporting Country: A Hydrogen, Wind and Gas Turbine Case Study

In the quest for achieving decarbonisation, it is essential for different sectors of the economy to collaborate and invest significantly. This study presents an innovative approach that merges technological insights with philosophical considerations at a national scale, with the intention of shaping the national policy and practice. The aim of this research is to assist in formulating decarbonisation strategies for intricate economies. Libya, a major oil exporter that can diversify its energy revenue sources, is used as the case study. However, the principles can be applied to develop decarbonisation strategies across the globe. The decarbonisation framework evaluated in this study encompasses wind-based renewable electricity, hydrogen, and gas turbine combined cycles. A comprehensive set of both official and unofficial national data was assembled, integrated, and analysed to conduct this study. The developed analytical model considers a variety of factors, including consumption in different sectors, geographical data, weather patterns, wind potential, and consumption trends, amongst others. When gaps and inconsistencies were encountered, reasonable assumptions and projections were used to bridge them. This model is seen as a valuable foundation for developing replacement scenarios that can realistically guide production and user engagement towards decarbonisation. The aim of this model is to maintain the advantages of the current energy consumption level, assuming a 2% growth rate, and to assess changes in energy consumption in a fully green economy. While some level of speculation is present in the results, important qualitative and quantitative insights emerge, with the key takeaway being the use of hydrogen and the anticipated considerable increase in electricity demand. Two scenarios were evaluated: achieving energy self-sufficiency and replacing current oil exports with hydrogen exports on an energy content basis. This study offers, for the first time, a quantitative perspective on the wind-based infrastructure needs resulting from the evaluation of the two scenarios. In the first scenario, energy requirements were based on replacing fossil fuels with renewable sources. In contrast, the second scenario included maintaining energy exports at levels like the past, substituting oil with hydrogen. The findings clearly demonstrate that this transition will demand great changes and substantial investments. The primary requirements identified are 20,529 or 34,199 km2 of land for wind turbine installations (for self-sufficiency and exports), and 44 single-shaft 600 MW combined-cycle hydrogen-fired gas turbines. This foundational analysis represents the commencement of the research, investment, and political agenda regarding the journey to achieving decarbonisation for a country.

Investigation of greenhouse emission inventory from transport system functioning in large and medium cities

The object of the research of the article is transport system of a city. The paper reviews the necessity to assess the greenhouse emissions in the city and proposes the methods for greenhouse emissions inventory of the urban transport system. The proposed approach is aligned with guidelines for the development of Sustainable Energy and Climate Action Plan (SECAP) of Mayor Covenants of European Union (EU). The methodologies outlined in the paper allow to estimate annual greenhouse emissions from transport sector. The SECAP defines transport sectors based on ownership and functioning as following: municipal transport fleet, public transport, and private and commercial transport. The paper proposes the methodology to estimate direct and indirect emissions in each of the described sector based on the information that is typicaly available to municipalities in Ukraine. The assessment is conducted on disaggregated level for different fuel types (diesel, petroleum, natural gas, biofuel, electricity, etc.) and separately for each fleet type (buses, trucks, passenger vehicles, specialized machinery). Total CO2 emissions are then estimated by multiplying the amount of fuel consumed by the emission factor for each fuel type and vehicle type. Information of fuel consumption is estimated based on annual milage and estimated based on available data and number of assumptions proposed in the paper. The proposed methodology for greenhouse emissions inventory for the transport sector allows to analyze available data, recommendations for data collection and a methodology for determining CO2 emissions from the operation of the transport system with sufficient accuracy of calculations. Based on these results, it is possible to forecast changes in energy consumption and emissions in the transport sector as a result of various interventions. For example, in Zhytomyr city the results of inventory were used to develop a set of measures, which include updating the rolling stock of electric transport; increasing the energy efficiency of the power grids of the transport system, developing cycling infrastructure.

Изменение климата как фактор региональных изменений в потреблении энергоресурсов (природного газа, электроэнергии)

На основе анализа климатических показателей и данных тепло- и электроэнергетики предприятий Лабинского района Краснодарского края выявлены тренды динамик полезного отпуска природного газа и электроэнергии потребителям. Определены взаимосвязь и перспективы расхода энергоресурсов с учетом общерегио-нальных изменений климата, а также в разрезе и по сезонам местных климатических особенностей рассматриваемого района. Многолетние данные были сформированы по приземной температуре воздуха в ФГБОУ «Краснодарская ВС» и на Метеостанции Ла-бинского филиала КЦГМС ФГБОУ «Северо - Кавказское УГМС»; по отпуску, расходу га-за и электроэнергии потребителям частного сектора и юридическим лицам в МУП г. Лабинска «Тепловые сети», в филиале г. Лабинска АО «Газпром газораспределение Краснодар», в Лабинском филиале АО «НЭСК», в Лабинском филиале ПАО «Россети Ку-бань». Установлено, что изменение климата ведет к неоднозначной динамике расхода (потребления) энергоресурсов, а именно с повышением приземной температуры воздуха существует устойчивая тенденция экономии ресурсов в теплоэнергетики района и, в тоже время она сомнительна для электроэнергетики. The work is devoted to the analysis of climatic data and data of heat and elec-tric power of enterprises on the example of the Labinsky district of the Krasnodar Territory. The article identifies trends in the dynamics of the useful supply of natural gas and electricity to con-sumers of the Labinsk region, determines the relationship and prospects of energy consumption, taking into account, both in general, with regional climate changes, and in the context of sea-sons, taking into account local climatic features of the area under consideration. Long-term da-ta, based on the results of the analysis of which conclusions were drawn, were directly collected: on the surface air temperature in the Krasnodar VS FSUE (hereinafter KVS) and at the Labinsk Weather Station of the North Caucasian UGMS branch of the North Caucasian UGMS FSUE (hereinafter MS Labinsk); on vacation, gas and electricity consumption, private sector consum-ers and to legal entities, in the Municipal Unitary Enterprise of Labinsk “Thermal Networks”, in the Labinsk branch of JSC Gazprom Gas Distribution Krasnodar, in the Labinsk branch of JSC NESK, in the Labinsk branch of PJSC Rosseti Kuban. As a result of the analysis, it was found that climate change leads to ambiguous changes in the dynamics of energy consumption (con-sumption). With an increase in surface air temperature, there is a steady tendency to save re-sources in the district’s thermal power industry, at the same time it is questionable for the elec-tric power industry.

Decomposition of the site-level energy consumption and carbon dioxide emissions of the iron and steel industry.

Identifying the key factors influencing energy consumption and CO2 emissions is necessary for developing effective energy conservation and emission mitigation policies. Previous studies have focused mainly on decomposing changes in energy consumption and CO2 emissions at the national, regional, or sectoral levels, while the perspective of site-level decomposition has been neglected. To narrow this gap in research, a site-level decomposition of energy- and carbon-intensive iron and steel sites is discussed. In this work, the logarithmic mean Divisia index (LMDI) method is used to decompose the changes in the energy consumption and CO2 emissions of iron and steel sites. The results show that the production scale significantly contributes to the increase in both energy consumption and CO2 emissions, with cumulative contributions of 229.63 and 255.36%, respectively. Energy recovery and credit emissions are two key factors decreasing site-level energy consumption and CO2 emissions, with cumulative contributions to the changes in energy consumption and CO2 emissions of -158.30 and -160.45%, respectively. A decrease in energy, flux, and carbon-containing material consumption per ton of steel promotes direct emission reduction, and purchased electricity savings greatly contribute to indirect emission reduction. In addition, site products and byproducts promote an increase in credit emissions and ultimately inhibit an increase in the total CO2 emissions of iron and steel sites.

Development of Virtual Sensor Based on LSTM-Autoencoder to Detect Faults in Supply Chilled Water Temperature Sensor

Supply chilled water temperature (SCWT) is an important variable for the efficient and stable operation of heating, ventilation, and air conditioning (HVAC) systems. A precisely measured value ensured by the continuous reliability of the temperature sensor is essential for optimal control of an HVAC system because temperature sensor faults can affect the chiller operation and waste energy. Therefore, temperature sensor fault-detection strategies are imperative for maintaining a comfortable indoor thermal environment and ensuring the efficient and stable operation of HVAC systems. This study proposes a fault-detection method for an SCWT sensor using a virtual sensor based on a long short-term memory-autoencoder. The fault-detection performance is evaluated considering a case study under various sensor fault scenarios to evaluate changes in indoor thermal comfort and energy consumption after correcting sensor faults detected by the virtual sensor. The results verify excellent fault-detection performance in various fault scenarios (F-1 scores ranging from 0.9350 to 1.000). After correcting the SCWT fault, indoor thermal comfort is steadily maintained without additional energy consumption (indoor set-point temperature unmet hour reduced by a maximum of 105.7 hours, and energy consumption decreased by up to 1.8%).

Does energy consumption matter for climate change in Africa? New insights from panel data analysis

Africa, abundant in natural resources, faces growth challenges due to widespread energy poverty and heightened biofuel use with severe environmental impacts. Recognizing the connection between energy consumption and climate change is crucial for implementing sustainable solutions and establishing guidelines for future energy practices amidst the adverse effects of climate change on the continent's ecosystem. To this effect, the present study examines the effect of energy consumption on climate change in African countries from 2004 to 2019. Employing the Discroll and Kraay estimation technique and the Generalized Method of Moment on secondary data, the results indicate that Energy Use as kg of oil, Fossil Fuel Energy Consumption, and Energy Depletion positively and significantly affect Climate change. At the same time, Renewable Energy Consumption reveals a correcting effect on climate variations in Africa. The results are valid for other climate change measures and are robust to endogeneity control. Based on the results, applicable economic policies are proposed regarding the efficient and optimum use of various energy sources for Africa's clean and sustainable climatic conditions.

Residential load forecasting by a PSO-tuned ANFIS2 method considering the COVID-19 influence

The most important feature of load forecasting is enabling the building management system to control and manage its loads with available resources ahead of time. The electricity usage in residential buildings has increased during the COVID-19 period, as compared to normal times. Therefore, the performance of forecasting methods is impacted, and further tuning of parameters is required to cope with energy consumption changes due to COVID-19. This paper proposes a new adaptive neuro-fuzzy 2 inference system (ANFIS2) for energy usage forecasting in residential buildings for both normal and COVID-19 periods. The particle swarm optimization (PSO) method has been implemented for parameter optimization, and subtractive clustering is used for data training for the proposed ANFIS2 system. Two modifications in terms of input and parameters of ANFIS2 are made to cope with the change in the consumption pattern and reduce the prediction errors during the COVID-19 period. Simulation results obtained by MATLAB software validate the efficacy of the proposed ANFIS2 in residential load forecasting during both normal and COVID-19 periods. Moreover, the performance of the proposed method is better than that of the existing adaptive neuro-fuzzy inference system (ANFIS), long short-term memory (LSTM), and random forest (RF) approaches.