Conventional Energy Consumption Research Articles

Level of Urbanization and Renewable Energy Consumption: The Case of Azerbaijan and Türkiye

Conventional energy consumption requires serious costs, especially for countries that import this type of energy. In this study, one of the countries included in the analysis is considered as energy exporting countries and the other as energy importing countries. The study attempted to measure the relationship between urbanization levels and renewable energy consumption in Türkiye and Azerbaijan for the period 2000-2020. Granger causality test was applied to measure the effect of the relationship between variables in these countries. After the necessary assumption tests were applied, the analysis was started. According to the Granger test results, no causal relationship was found between the variables in both countries.

Development Status and Future Prospects of Photovoltaic Cells

With the rapid development of social economy, the consumption of conventional energy is growing at an amazing rate. The energy shortage crisis and the environmental problems brought by conventional energy will seriously restrict social development and affect the daily lives of residents. Therefore, paying attention to the creation and use of new energy, protecting the environment, improving efficiency, controlling the emission of pollutants and realizing sustainable development have become the main research topics in the new era of the energy sector. Photovoltaic energy has the advantages of economic energy saving, green environmental protection, wide application and sustainability, and is an ideal new energy, that has been developed to the third generation. This paper mainly combs the development process of photovoltaic technology, summarizes the characteristics, advantages and disadvantages of the third generation of photovoltaic technology, analyzes the current situation and prospects of photovoltaic technology development, and analyzes the problems and challenges faced. This research finds that as the economy and technology continue to advance, photovoltaic cell technology is developing rapidly, and the application cost is constantly reduced. The photovoltaic cell industry will get more attention and better development, and its application prospect is very broad. The research of this topic is helpful in enhancing the comprehensive and objective understanding of the development of photovoltaic cell technology, and will provide a valuable reference in order to advance the photovoltaic sector in the future, which has important practical significance.

Performance prediction of a ground source heat pump system using denoised long short-term memory neural network optimised by fast non-dominated sorting genetic algorithm-II

The ground source heat pump (GSHP) is a commonly employed technique that utilises geothermal resources to heat or cool buildings, offering an advantageous alternative for reducing conventional energy consumption. Considering that the output energy of GSHP system is a critical criterion for evaluating the geothermal energy extraction, this study proposes an improved long short-term memory (LSTM) model for predicting the energy output of geothermal heat exchangers (GHE) EGHE-PT and the electrical energy consumption EEC-PT of a GSHP system. The model is trained and validated using a comprehensive real-time monitoring dataset gathered over a four-year period from a three-story residential house situated in Cleveland, Ohio, USA. To process the raw data, the wavelet denoising method is employed, while the fast non-dominated sorting genetic algorithm-II (NSGA-II) is employed to automatically determine the optimal hyperparameters for the LSTM model. Comparative analyses with alternative prediction models demonstrate the superior performance of the Denoised-LSTM-NSGA-II model. The results indicate that the Denoised-LSTM-NSGA-II model yields reasonable prediction for the two performance indicators: EGHE-PT and EEC-PT (with the R2 of 0.91 and 0.89, respectively). Further examination reveals that outdoor temperature holds a significantly high importance rank within the Denoised-LSTM-NSGA-II model. This implies that the proposed method can achieve an acceptable level of accuracy by only utilising weather data from the preceding 5 days. The aforementioned findings present a potentially cost-effective approach for predicting the performance of GSHP system based on limited monitoring data.

A Study on Energy Import Dependency in India

In today's globalized world, the interconnection between the economy and the environment is evident. A nation's prosperity and self-reliance hinge on adopting sustainable methods of energy production. Lack of energy security and self-sufficiency compels a country to import necessary energy resources, significantly impacting both sustainability and the foreign reserves. This paper addresses India's suboptimal utilization of energy resources, focusing on the import dependency in the energy sector due to the prevalent use of fossil fuel-based energy (conventional energy) from 2006 to 2020. Data analysis, incorporating graphical representations of conventional energy import and consumption, highlights the status, growth, and import dependency trends. The findings reveal that India heavily relies on imports for conventional energy resources, particularly crude oil. The paper concludes that the key solution for improving economic and environmental conditions lies in transitioning towards renewable energy resources.

The nexus between green finance and energy consumption in regional comprehensive economic partnership countries.

Green finance has been valued and promoted by Regional Comprehensive Economic Partnership (RCEP) countries for its attribute of supporting green and sustainable development. However, the fossil fuel-centric growth pattern poses a threat to the sustainable development of RCEP countries. The existing literature remains inadequate regarding the relationship between green finance and fossil energy consumption. An in-depth understanding and empirical examination of the nexus between green finance development and fossil energy consumption in RCEP countries is key to successful policymaking and sustainable development. This study proposes a theoretical framework for analyzing the nexus between green finance and fossil energy consumption. Then, a green finance development index is constructed by using the entropy weight method based on green credits, green securities, and green investments. By utilizing method of moment quantile regression (MMQR), panel data of 10 RCEP countries from 2008 to 2020 are investigated. The results demonstrate a strong nonlinear pattern of green finance's impact on reducing fossil energy consumption. Conventional finance and fossil energy consumption from the preceding period significantly promote fossil energy consumption, while green technology serves as a mitigating factor for fossil energy consumption. However, the impacts of education and environmental expenditure on fossil energy consumption are limited and inconsistent. Hence, the relevant practitioners, including governments and policymakers, are encouraged to collaboratively promote the green finance policies, and devise tailor-made strategies based on countries' features. Additionally, this study recommends that the RCEP countries incorporate research and development of green technologies, as well as environmental and educational expenditures, into the policymaking process.

Research on the Application of Heat Pump with Different Coupling Methods in Heating Terminal

Under the dual pressure of energy supply and demand contradiction and increasingly severe environmental pollution, adopting clean energy to replace traditional fossil energy, reducing conventional energy consumption, and achieving clean heating is a necessary measure for China to build a resource-saving and environmentally friendly society. In order to achieve the above goals, heat pump technology is receiving attention and promotion from people. Analyze the application of solar and air source heat pump combined heating systems and water source heat pump and air source heat pump combined heating systems based on the comparison of two coupling methods and temperature distribution at different heating ends.

Towards smart sustainable cities: Does technological innovation mitigate G7 CO2 emissions? Fresh evidence from CS-ARDL

Sustainable cities are considered paradigmatic to achieve COP27 targets, but scant literature has analyzed the association with CO2 emission (CE) mitigation policies, especially for the G7 economies. This study traces the impacts of urbanization, innovation, income, and energy consumption on CE for the G7 countries from 1995 to 2019. The time period is important due to the rapid increase in urbanization, globalization, and, at the same time, technology diffusion. The study is focused on novel and relevant panel data econometric methods that tackle cross-section dependence, non-normality of data, and heterogeneity in slope coefficients. The empirical outcome first confirms the long-run cointegrating relationship among the variables in the presence of structural breaks by employing the advanced panel cointegration test. Technologies related to environmental management and energy consumption are positively linked with CE. However, the role of income per capita is found to be inverse with CE. In contrast, the effect of urbanization in different models is mixed for CE. Innovation and its interaction with urbanization are crucial for limiting CE and promoting sustainable cities in G7 economies. This indicates that urbanization with environment-friendly innovation can help G7 countries foster sustainable cities and achieve COP27 targets in general, specifically the Sustainable Development Goal of sustainable cities. The results were tested for the multiplicative effect of different variables with urbanization and innovation. Regarding policy insights, the study recommends focusing on smart and sustainable cities with environment-related innovations and transitioning towards cleaner energy from conventional energy consumption.

Development of a simplified test for the determination of the Bond Ball Mill Work Index using a modified Hardgrove test

Grinding ores in conventional ball mills demands high energy consumption, resulting in relatively high operating costs in a typical mineral processing industrial plant. Whether in the initial stages of a mining project or during its operation, conducting tests to determine the processing circuit’s energy consumption throughout the life of mine is necessary in order to reduce such high operating costs. Traditional tests used to determine the energy consumption in grinding, like the Bond Ball Mill test, are relatively time-consuming and require significant amounts of sample, e.g., 8–10 kg, which is not always available in the initial stages of a mineral project or geometallurgical studies. Several authors have developed simplified tests to predict ball mill’s energy consumption, typically using the Bond Ball Mill Work Index (BBMWI) as a reference. One option requiring less mass is the Hardgrove mill, traditionally used in the laboratory to determine the energy required to grind coal in roller mills. This test requires 50 g of sample, and the result is based on the amount of fines at the end of grinding to a fixed time. The present study used a modified Hardgrove test to predict the BBMWI. This test involves the measurement of the torque, uses a fixed sample volume with different particle feed sizes from the Hardgrove standard, and determines the specific energy related to the percentage of product passing through the closing size screen of the Bond test, defined as the size specific energy. The results indicated that for a set of 13 samples with the 150 µm closing screen, the average error associated with the predicted BBMWI is within ±2.3%. Further results indicate an error of ±3.1% for 20 samples with the 106 µm closing screen. The correlations presented here are proposed to be validated and/or adjusted for different ores and mines. The proposed test is thus suitable for geometallurgical studies and other applications where relatively small samples are available. In addition, this is a low-cost test, has a quick turn-around time, it is simple to conduct and does not require the handling of heavy loads by operators.

Alternative energy sources and their role in reducing climate change

Several climatic changes have taken place during the few recent years. These changes are due to the continuous increase in the consumption of conventional energy including oil, gas, and coal. The largest percentage of this increase is caused by the major industrial countries whose goal is to achieve remarkable economic benefits at the expense of other countries and our environment. Today, global warming is among the most threatening environmental problems. This phenomenon is linked with other problems which are not easy to resolve, such as desertification, floods, and fires. 
 Accordingly, the international society must make great efforts, and efficient cooperation among all the world’s nations in order to reduce the consumption of conventional energy through finding clean sources of energy, such as solar, water, and wind energy. This is the only way to save our environment from pollution and many other climatic problems

Energy Harvesting for Wireless IoT Use Cases: A Generic Feasibility Model and Tradeoff Study

A battery-less IoT offers a sustainable alternative to battery-powered IoT devices, which produce billions of dead batteries every year. Devices are instead powered by a small supercapacitor, which is recharged by a renewable energy source. However, since IoT devices are often characterized by intermittent periods of high energy consumption followed by periods of reduced activity, conventional average energy consumption models can not be used to assess if an IoT devices can be powered by energy harvesters. Therefore, this paper presents an alternative feasibility evaluation approach that focuses on modeling the worst-case periods with peak energy consumption and short idle times, which pose the highest constraints on the capacitor’s behavior. This approach simplifies the characterization of the wireless technology energy consumption as these worst-case periods can be determined by a few parameters. The methodology is then applied to combinations of popular IoT technologies (LoRaWAN, BLE Mesh, and 6TiSCH) and energy sources (solar, kinetic, and radio frequency energy) for two common IoT use cases. We show that the proposed parameters can be successfully extracted with power measurements for different network configurations and that the Power Management Unit configuration has a non-negligible impact on the communication requirements. Finally, we discuss how to apply the model to other technologies and other use cases.

The ban on the sale of new petrol and diesel cars: Can it help control prospective marine pollution of polycyclic aromatic hydrocarbons (PAHs) in Shandong Province, China?

The constantly increasing amount of road vehicles causes massive exhaust emissions of pollutants, including polycyclic aromatic hydrocarbons (PAHs), necessitating a global responsibility to implement the policy of the ban on the sale of new petrol and diesel cars. Here, we assessed the policy control efficiency on marine pollution of PAHs in China through scenario modeling and prediction models, based on pollution monitoring, risk assessment, and source apportionment of PAHs in typical bays of Shandong Province. The results showed that in 2021, the pollution risk levels were relatively low (HI: 0.008–0.068, M-ERM-Q: 0.001–0.016, IBR: 1.23–2.69, ILCR: 8.11 ×10-6-1.99 ×10-5), and PAHs were mainly derived from traffic emissions (24.9%−35.2%), coal combustion (25.2%−32.9%), petroleum (17.2%−28.9%), and biomass combustion (17.6%−22.8%). In 2050, the predicted decrease of pollution risk values after the implementation of the policy was significant (12%−26%), and the gap between 2021 and 2050 was also significantly huge (18%−85%) without considering possible substitution of conventional energy. Collectively, this study built systematic approaches for assessing prospective marine pollution of PAHs. However, due to the particularity of Shandong Province, i.e., its national predominance of conventional energy consumption, the policy may be more effective when it comes to other coastal areas worldwide, calling for a larger scale research.

An explainable artificial intelligence approach to understanding drivers of economic energy consumption and sustainability

This study aims to optimize energy consumption to achieve sustainability, as there is a necessary foray into the green energy market by switching to net-zero carbon-emitting fuel alternatives. There is a need to decide when to switch to these net-zero fuels from conventional energy sources, which calls for a deeper investigation into the driving factors that lead to harmful energy emissions. This investigation will help monitor and curb such indicators to minimize harmful emissions or determine the opportune time to switch to green energy alternatives if the indicator levels cannot be controlled. This research is motivated by recent studies that consider factors such as per capita carbon intensity and other sector-specific factors such as per dwelling carbon intensity and per value-added carbon intensity. However, these studies do not scientifically quantify the extent to which each element contributes to the final conventional energy consumption. Furthermore, sector-specific consumption indicators also need to be estimated to undertake significant design modifications for developing energy-efficient systems. The consumption is predicted as “total consumption” for the four sectors considered: residential, industry, services, and transportation. Advanced machine learning algorithms such as random forest, gradient boosting, and deep neural network are used for this purpose. The essential factors/drivers for each sector are derived through the explainable artificial intelligence Shapley framework, which scientifically measures the contribution of each factor to energy consumption. Consequently, the major key indicators for each of the four sectors under consideration are identified. Alternatively, the insights gained from the study may prompt a complete switch to alternative energy-efficient sources. The study findings provide valuable insights for both communities and businesses in achieving the goals of net-zero and energy conservation. Additionally, some examples are highlighted to demonstrate how practitioners can implement customized designs under each sector to reduce harmful emissions and promote energy efficiency. Suitable steps for stakeholder engagement and community participation in this regard are also suggested.

Investigating Catalyzed Transesterification for Castor Oil Ethyl Ester Production: A Promising Biodiesel Substitute

Abstract: This study investigates the catalysed transesterification method for producing castor oil ethyl ester (COEE), a biodiesel substitute. The research looks at a number of variables that impact the reaction, including temperature, the ratio of alcohol to oil, the concentration of the catalyst, the amount of mixing, and the purity of the reactant. In the experimental setting, biodiesel is created in a lab and tested on a diesel engine. The outcomes show that the transesterification procedure successfully lowers castor oil's viscosity and raises the fuel's cetane number. According to engine testing, a 20% blend of COEE demonstrates promising performance with conventional diesel fuel-like energy consumption and thermal efficiency. The study emphasises COEE's potential as a superior biodiesel alternative with viability.

Compressible polyaniline-coated sodium alginate-cattail fiber foam for efficient and salt-resistant solar steam generation

Solar steam generation has attracted widespread attention because of its ability to produce clean water through desalination and wastewater treatment without conventional energy consumption. In this work, a polyaniline (PANI)-coated sodium alginate (SA)/cattail fiber (CF) foam for photothermal evaporator is prepared via directional freezing and oxidative polymerization. The SA/CF foam displays desirable water pumping capability because of the lamellar sandwich structure interconnected by porous networks. More importantly, the directional porous network architecture ameliorates the mechanical and salt-resistant performances of the SA/CF foam. The as-prepared PANI@SA/CF foam shows inferior heat conductivity of 0.047 W m−1 K−1 and outstanding light absorption over 96% in solar window. A vapor evaporation rate of 2.04 kg m−2 h−1 under 1 sun illumination is achieved for the PANI@SA/CF evaporator. Furthermore, the PANI@SA/CF foam could be employed in solar-driven freshwater generation from seawater and wastewater with high ion and dye removal rates. The combination of water evaporation and cleaning capabilities of the PANI@SA/CF foam as photothermal materials provide a framework for the exploration of next-generation evaporators in seawater desalination and wastewater treatment applications.

Earnings expectations of grey and green energy firms: Analysis against the background of global climate change mitigation

Promoting the adoption of green energy and reducing the consumption of conventional energy are becoming one of the most critical subjects in our society. Against this background, what are the investors' earnings expectations and how they form their expectations of green and conventional energy firms are still unclear. In this study, we aim to fill these gaps by systematically examining investors' earnings expectations of these firms from 1992 to 2016. We find that market participants are overly optimistic about the future earnings of both grey energy and green firms. However, the earnings optimism about these two types of firms is less potent than the market average. Besides, the earnings expectations of both firms exhibit time-varying pattern that investors are more optimistic after 2008. Surprisingly, no significant difference in earnings expectations between grey and green companies is identified in the studied period. Regression results indicate a positive relation between firm investment (profitability) and earnings optimism of green (grey) firms, indicating that investors overestimate different firm characteristics of these firms when forming their earnings expectations.

Does excessive energy utilization and expansion of urbanization increase carbon dioxide emission in Belt and Road economies?

The phenomenal increase in global temperature and variation in climate change are the replications of nature, alarming governments to limit the emissions of greenhouse gases (GHGs) and adopt green innovation and environmental-friendly clean and green technologies. In this paper, we empirically investigate whether there are any changes in excessive consumption of energy from conventional sources, expansion of urbanization, carbon dioxide (CO2) emissions, and economic growth in six different regions, namely, East Asia (EA), South Asia (SA), Southeast Asia (SEA), Central Asia (CA), Eastern Europe (EE), and the Middle East and North Africa (MENA), under the Belt and Road Initiatives of panel data over the period of 1985 to 2017. The empirical methods include a panel co-integration check, heterogeneity test, panel Granger causality test, pooled mean group (PMG), and augmented mean group (AMG). To verify the outcomes, robustness tests were carried out using the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) approaches. Our results confirm that CO2emissions are primarily influenced by excessive utilization of conventional energy, economic growth, and expansion of urbanization. The findings confirm the co-integrating relationships among the variables in all six regions. Moreover, the panel causality analysis identified a bidirectional causal relationship between energy consumption, economic growth, urbanization, and CO2emissions. While these results can play an instrumental role in formulating CO2emission policies among our selected countries, our research can also assist policymakers and governments in other developing countries implement important policy initiatives. In this regard, the findings suggest that the current environment-related polices of Belt and Road Initiatives (BRI) do not efficiently tackle CO2 emissions. In order to achieve the CO2 emission degradation objective, the Belt and Road countries should restructure their environment-related policies by limiting the consumption of conventional energy and expansion of urbanization. The adaptation and establishment of such a panoramic policy program can assist emerging economies to acquire consolidated and environmentally sustainable economic growth.

Experimental Research on Heating Transfer Improvement of Materials that Change Phase in Tunnels in Cold Regions

In tunnel heating systems, phase change materials may minimize the consumption of conventional electric energy, which is very considerate in the field of tunnel heating in cold regions. Because of the phase change material’s poor heat conductivity, its annual growth rate heat absorption and release is slower; thus, the majority of phase change heat storage systems must improve heat transmission. In this study, a spiral metal ring is implanted in the paraffin to improve heat transmission to achieve this objective using a concentric sleeve-type paraffin heat storage device as a medium. Experiments were performed out in order to determine the effects of heating rate, hot fluid flow rate, and the use of a spiral metal ring on the heat storage and release process of a thermal storage device. In comparison to the paraffin thermal storage device, the embedding of the spiral metal ring accelerates the internal thermal performance of the composite heat storage device, resulting in a more uniform temperature distribution. When the thermal radiation heating rate is 60°C, 65°C, or 70°C during the heat storage process, the heat storage time of the composite heating storage device is reduced by 59.2 percent, 44.4 percent, and 40.7 percent, respectively. When the ambient temperature is 26°C and the heat storage device’s starting temperature is 65°C, the exothermic time is reduced by 22.6 percent.

A Physics-Based Modelling and Control of Greenhouse System Air Temperature Aided by IoT Technology

The need to reduce energy consumption in greenhouse production has grown. Thermal heating demand alone accounts for 80% of conventional greenhouse energy consumption; this significantly reduces production profit. Since microclimate affects crop metabolic processes and output, it is essential to monitor and control it to achieve both quantity and quality production with minimum energy consumption for maximum profit. The Internet of Things (IoT) is an evolving technology for monitoring and controlling environments that have recently been adopted to boost greenhouse efficiency in many applications by integrating hardware and software solutions; therefore, its adoption is thus critical in enabling greenhouse energy consumption minimisation. The first objective of this study is to improve and validate a greenhouse dynamic air temperature model required to simulate or predict indoor temperature. To achieve the first objective, therefore, an existing model was enhanced and a closed loop test experimental data from the IoT cloud-based control system platform deployed in the prototype greenhouse built in Cranfield University was used to validate the model using an optimisation-based model fitting approach. The second goal is to control the greenhouse air temperature in simulation using relatively simple PI and on-off control strategies to maintain the grower’s desired setpoint irrespective of the inevitable disturbances and to verify the potential of the controllers in minimising the total energy input to the greenhouse. For the second objective, the simulation results showed that the two controllers maintained the desired setpoint; however, the on-off strategy retained a sustainable oscillation, and the tuned PI effectively maintained the desired temperature, although the average energy used by the controllers is the same.

Colligating ecological footprint and economic globalization after COP21: Insights from agricultural value-added and natural resources rents in the E7 economies

ABSTRACT While economic globalization and resource utilization continue to foster growth among nations, the Paris Convention adopted at COP21 has highlighted some imminent environmental dangers facing humanity if pertinent collective climate actions are ignored. Thus, this study assesses the links between ecological footprint and economic globalization among the E7 nations in the aftermath of COP21. This study fills the gap in the literature on the E7 bloc by accounting for the unexplored role of agricultural value-added while accounting for the impacts of resources rent, disaggregated energy use, and economic growth towards addressing environmental-related Sustainable Development Goals (SDGs) in the bloc. A combination of estimation techniques, including the Dynamic Ordinary Least Squares (DOLS), Fully Modified Ordinary Least Squares (FMOLS), and Panel-ARDL, was employed to address methodological flaws while analyzing data ranging from 1990 to 2019. The results show that globalization and agricultural activities have exacerbated ecological footprint to the detriment of the E7’s environmental quality. The results further indicate that the expansion of economic activities in the E7 countries has also increased the conventional energy consumption (oil, coal, and gas for electricity generation), thereby translating to environmental deterioration via a higher ecological footprint. Overall, to ensure a sustainable environment in our increasingly globalized world, the study posits that policymakers in the E7 should facilitate proper implementation of environmental damage cost in addition to maintaining strategic resource control measures in order to increase the awareness of the explorers of natural resources and other international organizations of their economic activities beyond business as usual.

An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room

There are no exact criteria for the architecture of openings and windows in office buildings in order to optimize energy consumption. Due to the physical limitations of this renewable energy source and the lack of conscious control over its capabilities, the amount of light entering offices and the role of daylight as a source of energy are determined by how they are constructed. In this study, the standard room dimensions, which are suitable for three to five employees, are compared to computer simulations. DesignBuilder and EnergyPlus are utilized to simulate the office’s lighting and energy consumption. This study presents a new method for estimating conventional energy consumption based on gene expression programming (GEP). A gravitational search algorithm (GSA) is implemented in order to optimize the model results. Using input and output data collected from a simulation of conventional energy use, the physical law underlying the problem and the relationship between inputs and outputs are identified. This method has the advantages of being quick and accurate, with no simulation required. Based on effective input parameters and sensitivity analysis, four models are evaluated. These models are used to evaluate the performance of the trained network based on statistical indicators. Among all the GEP models tested in this study, the one with the lowest MAE (0.1812) and RMSE (0.09146) and the highest correlation coefficient (0.90825) is found to be the most accurate.