Awareness Of Energy Consumption Research Articles

An Overview of the Energy Savings, Utilization, and Emission Reductions of Boilers

Industries and power plants use boilers extensively as a steam generating system. Boilers use a substantial amount of the world's energy supply. Much more fossil fuel can be saved and CO2 emissions can be decreased with even a slight increase in boiler efficiency. This paper outlines the energy consumption of boilers, the methods used to assess their energy efficiency, the losses that have occurred and their reasons, the role that maintenance activities play in preventing heat loss, the quantity of waste energy recovered, and the methods of raising public awareness of energy consumption. On the aforementioned subjects, the most recent published literature has been examined and reported. This includes journal articles, PhD and MSc theses, conference proceedings, reports, and online resources. The high temperature flue gas or boiler exhaust is discovered to be a significant source of energy loss. Also, for a variety of reasons, there are certain other inevitable losses. However, waste heat may be captured and used to provide power, heat, refrigeration effect, and other useful forms of energy utilizing a variety of methods. By doing planned maintenance, one may increase a boiler's efficiency and keep it operating at peak performance. Staff members must regularly participate in education programs and seminars aimed at raising knowledge about energy consumption. As a result, they will be better able to comprehend the significance of the energy utilized in the boiler system and the effects of their activities on it.

Social impact of decarbonization objectives through smart homes: Survey and analysis

The technological development and economic growth of renewable energy are the key solution for almost full decarbonization of the energy systems. The sustainability and energy efficiency, artificial intelligence and connectivity will be the determining elements towards reaching this ambitious goal. Household appliances and tools for controlling energy consumption, temperature, lighting, etc. In the smart home will play a key role to increase awareness of families' energy consumption. IoT (Internet of Things) devices for the smart home allow us to monitor consumption and pay attention to efficiently energy use. However, savings are not the only important element of the smart home. Together with the digital control of the home these are elements that include a fundamental dimension: decarbonization, which is the cornerstone of the energy transition, which must involve an epochal change of mentality to our society where digitalization and people's well-being meet. The paper deals with social analysis of large cities population perception on integrating renewable energy sources and necessary devices to transform existing houses in smart homes towards economic wellbeing and decarbonization of the energy sector. Different classes of respondents were interviewed – owned and rented fome, rural and urban inhabitants, different segments of age, various number of inhabitants in urban areas and the capital, women and men, high-income and low-income, different levels of education – to support or disregard smart home technological development and reveal the different perception towards further usage of smart devices for daily benefits.

A simulation model of the real-world fuel and energy consumption of light-duty vehicles

The European Union has intensified efforts to reduce CO2 emissions from the transport sector, with the target of reducing tailpipe CO2 emissions from light-duty vehicle new registrations by 55% by 2030 and achieving zero emissions by 2035 according to the “Fit for 55” package. To promote fuel and energy consumption awareness among users under real-world conditions the MILE21—LIFE project provided tools such as a self-reporting tool and a find-a-car tool that included the official and representative on-road fuel/energy consumption values. In order to produce representative values, an in-house vehicle longitudinal dynamics simulation model was developed for use in the background of the on-line platform utilizing only a limited amount of inputs. To achieve this, the applied methodology is based on precalculated efficiency values. These values have been produced using vehicle micro-model simulations covering a wide range of operating conditions. The model was validated using measurements from a dedicated testing campaign and performed well for petrol vehicles with an average divergence of −1.1%. However, the model showed a divergence of 9.7% for diesel vehicles, 10.6% for hybrids and 8.7% for plug-in hybrids. The model was also applied to US vehicles and showed a divergence of 1.2% and 10% for city and highway driving, respectively. The application of the developed model presented in this work showed that it is possible to predict real-world fuel and energy consumption with the desired accuracy using a simplified approach with limited input data.

Indoor comfort management and energy consumption in Arctic buildings: Occupant perceptions in an indigenous community of Canada

Arctic and subarctic communities currently face major and unique energy and housing challenges. However, the perceptions, needs, and adaptive strategies of occupants related to energy consumption and comfort in their dwellings are not well known. In this study, 16 semi-structured interviews were conducted in the Inuit community of Quaqtaq (Nunavik, Quebec, Canada) to investigate occupants’ experiences, observations, and perceptions on these topics. The topics covered comfort, window opening, use of curtains, and usage of thermostats, water, and lighting. Furthermore, awareness of energy consumption and renewable technologies was investigated, and occupants shared their vision of the ideal home. It was found that windows and thermostats are often used simultaneously to regulate temperature and that non-uniform temperature distribution and cold floors were often noted by occupants. Due to the limited amount of water available, coordination with neighbors was needed for activities requiring water. Awareness and interest in energy savings and renewable energy varied significantly among participants. Participants mentioned that their ideal home would have better insulation, larger water tanks, and more storage space. These results help to understand occupant behavior, energy consumption patterns, and well-being in houses of northern communities and can guide the development of future housing units.

An Econometric Analysis of the Energy-Saving Performance of the Italian Plastic Manufacturing Sector

In a scenario characterised by mitigation concerns and calls for greater resilience in the energy sector, energy audits (EAs) emerge as an essential mean for enhancing end-use energy consumption awareness and efficiency. Such a tool allows us to assess the different energy carriers consumed in a productive sector, offering insight into existing energy efficiency improvement opportunities. This opens avenues for research to devise an econometrics-based methodology that encapsulate production sites and their environmental essentials. This paper contributes to the literature by exploiting the EAs received by the Italian National agency for New technologies, Energy, and Sustainable Economic Development (ENEA) in 2019 from the Italian plastics manufacturing sector, matched with Italian firm-based data extracted from the Analisi Informatizzata delle Aziende Italiane (Italian company information and business intelligence) (AIDA) database. In particular, we investigate how the implementation of energy efficiency measures (EEMs) is influenced by a set of contextual factors, as well as features relating to the companies and EEMs themselves. The empirical investigation focuses on the EAs submitted to ENEA in 2019, which was strategically chosen due to its unique data availability and adequacy for extensive analysis. The selection of 2019 is justified as it constitutes the second mandatory reporting period for energy audits, in contrast to the 2022 data, which are currently undergoing detailed refinement. In line with the literature, the adopted empirical approach involves the use of both the OLS and logistic regression models. Empirical results confirm the relevance of economic and financial factors in guiding the decisions surrounding the sector’s energy performance, alongside the analogous influence of the technical characteristics of the measures themselves and of the firms’ strategies. In particular, the OLS model with no fixed effects shows that a one-percent variation in investments is associated with an increase in savings performance equal to 0.63%. As for the OLS model, including fixed effects, the elasticity among the two variables concerned reaches 0.87%, while in the logistic regression, if the investment carried out by the production sites increases, the expected percentage change in the probability that the energy-saving performance is above its average is about 187.77%. Contextual factors that prove to be equally influential include the incentive mechanism considered and the traits of the geographical area in which the companies are located. Relevant policy implications derived from this analysis include the importance of reducing informational barriers about EEMs and increasing technical assistance, which can be crucial for identifying and implementing effective energy solutions.

A hybrid weighting method with a new score function for analyzing investment priorities in renewable energy

The transition to renewable energy sources is a critical challenge that emerging economies are facing today. Investment decisions related to the renewable energy transition involve multiple criteria and objectives that need to be analyzed and evaluated before making informed choices. This study develops a score function, the partitioned dual Muirhead mean (PDMM), the q-rung orthopair fuzzy weighted PDMM (q-ROFWPDMM), and a hybrid weighting technique based on Shannon entropy and decision-making trial and evaluation laboratory (DEMATEL) methods for q-rung orthopair fuzzy information. Additionally, a new multi-criteria group decision-making (MCGDM) technique is constructed using these developed approaches. This approach is applied to analyze the investment priorities of the renewable energy transition for emerging economies (E7 countries). It considers twelve criteria in four dimensions: (i) regulations to increase the renewable potential; (ii) supply of renewable materials; (iii) encouragement of private sector investors in the renewable industry; and (iv) awareness of renewable energy consumption. The results of the hybrid weight finding technique indicate that the “reducing market risk” criterion is the most important for investment priorities. The application of the novel MCGDM technique reveals compelling results, indicating that China has emerged as the frontrunner in effectively bolstering renewable energy investment projects. Moreover, a sensitivity analysis is presented, followed by a comparative analysis in which four conventional and three partition based aggregation operators are considered to show the effectiveness of the developed approach. The proposed approach provides a comprehensive analysis of investment priorities for the renewable energy transition, aiding decision-makers in making informed investment choices.

Physics-Informed Time-Aware Neural Networks for Industrial Nonintrusive Load Monitoring

Nonintrusive load monitoring enables the situational awareness of appliance-level energy consumption without installing appliance-specific sensors. It has been researched for over thirty years, with deep learning methods being the state-of-the-art solutions. However, current works mainly focus on the residential scenario, and industrial load disaggregation as a more challenging problem from the appliance type perspective is much less investigated. Nevertheless, industrial loads play an important role in energy savings and climate change mitigation and adaptation. Therefore, this paper focuses on the industrial nonintrusive load monitoring problem and proposes a physics-informed time-aware neural network method for it. Herein, multiple features of industrial loads are considered, and the physics relationship among them is leveraged to improve the learning process explicitly. In addition, a two-dimensional convolutional layer is further proposed to encode the timestamp for feature enhancement. Experiments on real-world industrial data from ten appliances will verify the effectiveness of the proposed method.

Renewable Energy Communities in the Energy Transition Context

Economic, social, and environmental sustainability is becoming increasingly important in territorialdevelopment policies in Europe and internationally. Among the sustainable development goals, the theme ofthe energy transition is particularly important, which translates not only into a move away from energyfrom fossil fuels in favor of renewable ones but also into an improvement in energy efficiency linked toenergy production and greater awareness of energy consumption by citizens. To achieve a paradigm ofsustainable development, combating the problem of resource scarcity and biodiversity loss, as well as theunsustainability of today's consumption and production systems, the Renewable Energy Communities (CER)can be a suitable model to support urban space redevelopment projects. Actions that aim to recover the pre-existing building heritage to give new life to abandoned areas are increasingly necessary for the protection ofthe environment. This encourages the propensity to act responsibly, promoting virtuous circles for theterritories and communities of reference. This work aims to analyze and understand the actual developmentof the eco-friendly model of Energy Communities, understanding its real benefits. In particular, the casestudy of the Energy Community of East Naples is reported which, through the involvement of citizens andbusinesses of the territory, can produce, consume and exchange energy with a view to self-consumption andcollaboration.

Innovative Data-Driven Energy Services and Business Models in the Domestic Building Sector

The market of energy services for the residential sector in Europe is very limited at present. Various reasons can be argued such as the high transaction costs in a highly fragmented market and the low energy consumption per dwelling. The rather long payback time for investments render Energy Services Companies’ (ESCOs) services financially unattractive for many ESCOs and building residents, thus hindering a large potential of energy savings in a sector that is responsible of almost half of Europe’s energy consumption. If the ambitious 2030 and 2050’s decarbonisation targets are to be met, the EU’s residential sector must be part of the solution. This paper offers insights about novel ESCO business models based on intensive data-driven Artificial Intelligence algorithms and analytics that enable the deployment of smart energy services in the domestic sector under a Pay-for-Performance (P4P) approach. The combination of different sources of energy efficiency services and the optimal participation of domestic consumers in aggregated demand response (DR) schemes open the door to new revenue streams for energy service providers and building residents and reduce the hitherto long payback periods of ESCOs services in the sector. Innovative business models for ESCOs and demand flexibility Aggregators are thoroughly described. Especially customised Performance Measurement and Verification protocols enable fair and transparent P4P ESCO contracts. The new human-centric energy and non-energy services increase the energy consumption awareness of building users and deploy behavioural and automated responses to both environmental and market signals to maximise the economic benefit for both energy service providers and consumers, always respecting data protection rules and the consumers’ comfort preferences. The new hybrid business models of P4P energy services make traditional EPC more attractive to energy service providers, with low cost data collection and treatment systems to bring payback periods below 10 years in the residential building sector.

The philosophy of energy consumption in North Africa, contribution of artificial intelligence

Some people in North Africa have been accustomed to getting everything, from the consumption of various goods, to luxurious housing, to travel from one country to another, unaware that the necessities of this way of life are very energy intensive. These countries, too, are unaware of the importance and necessity of opening up to each other in order to work together to raise awareness of energy consumption. Through this research, which is based on a scientific approach combining technology, human and social sciences and statistical studies, we have tried to highlight the importance of logical energy consumption while explaining that the consequences will be unexpected in the future in the event of an energy shortage in North Africa.

Region-of-interest based video coding strategy for rate/energy-constrained smart surveillance systems using WMSNs

Video surveillance systems that involve embedded visual sensor nodes have significant constraints due to their limited energy sources. Reducing the power consumption of the in-node processing and the required bandwidth while maintaining a high QoS is still a challenging task. The difficulty increases when a smart task must be performed on the received video in the destination. In this context, this paper proposes an energy-efficient video coding strategy based on a new and fast region-of-interest (ROI) detection method. The lightweight ROI detection method segments the frame into four regions. And the coding strategy aims to extract two different classes of the ROI for coding and transmission using variable quality levels based on their relevance. Furthermore, the strategy aims to exclude the regions of lower importance and any non-ROI that has insignificant movement. We assess the strategy’s ability to perform object recognition tasks at the destination under quality degradation. The performance results using different datasets demonstrate a better trade-off between awareness of ROI quality, energy consumption and bandwidth savings for the proposed strategy compared to other methods. This results in 96% reduction of bandwidth and 93% reduction in energy for some sequences at the expense of a 1-4 dB decrease in PSNR when compared to MJPEG standard. While the recognition accuracy of the YOLOv3 model at the destination outperforms the other techniques by about 4% to 22%.

Can we lead the way in raising awareness of energy consumption?

Can we lead the way in raising awareness of energy consumption?

γ-Ray-sensitive motif-driven versatile epoxy thermoset: Highly efficient radiation degradation, intrinsic flame resistance and sustainable synthesis

With the growing awareness of energy consumption and environmental pollution, the development of γ-radiation-degradable technology derived from clean energy is of great concern. However, radiation-induced degradation for thermoset materials is limited because γ-radiation-induced radicals are prone to recombination rather than controlled cleavage. Herein, via introducing the radiation-sensitive groups, phenyl imine conjugated NN bonds, we synthesized a radiation-sensitive epoxy monomer from vanillin (a renewable source) and 4-hydroxybenzhydrazide. The thermoset was formed by crosslinking this bio-based epoxy monomer with our reported hardener bearing phenyl imine conjugated NN bonds. When treated by a stimulus of γ-ray radiation, the resulting epoxy thermoset with high contents of sensitive groups could be efficiently degraded at a dose of 10 kGy. Degradation of the thermosets are realized through the controlled cleavage of phenyl imine conjugated NN bonds. Benefiting from this stable conjugated structural design, the epoxy thermosets exhibit excellent mechanical performance and intrinsic flame resistance with a tensile strength over 70 MPa and a limited oxygen index (LOI) of 35.1 %, which are higher than those of traditional robust thermosets made from bisphenol A. This work not only renders a pioneering paradigm of bio-based γ-ray-degradable thermosets but also develops an effective approach to simplify the manufacturing process of fire-safe thermosets, which significantly enhance the versatility of polymer materials.

Secure and Latency-Aware Digital Twin Assisted Resource Scheduling for 5G Edge Computing-Empowered Distribution Grids

Digital twin (DT) provides accurate guidance for multidimensional resource scheduling in 5G edge computing-empowered distribution grids by establishing a digital representation of the physical entities. In this article, we address the critical challenges of DT construction and DT-assisted resource scheduling such as low accuracy, large iteration delay, and security threats. We propose a federated learning-based DT framework and present a Secure and lAtency-aware dIgital twin assisted resource scheduliNg algoriThm (SAINT). SAINT achieves low-latency, accurate, and secure DT by jointly optimizing its total iteration delay and loss function, and leveraging abnormal model recognition (AMR). SAINT enables intelligent resource scheduling by using DT to improve the learning performance of deep Q-learning. SAINT supports access priority and energy consumption awareness due to the consideration of long-term constraints. Compared with state-of-the-art algorithms, SAINT has superior performance in cumulative iteration delay, DT loss function, energy consumption, and access priority deficit.

Single Phase Prepaid Smart Energy Meter System Using an RFID AND Arduino Nano

Abstract: In the present day scenario, the energy consumption awareness plays an important role as its demand is growing day by day. Huge differences in energy production and consumption urges the need to reduce the power wastage. The prepaid electricity system proposed in this paper aims at reducing the wastage of power due to its theft and control its usage on the consumer side. This increases the overall efficiency of the power system. The RFID card enables a consumer to utilize the energy for which he has paid. A microcontroller continuously checks if his balance is within the threshold limit and he is notified when his balance goes below the limits. Once it exceeds the threshold value, the supply is automatically cut off. The user can then recharge by making an online payment whenever necessary. The LCD continuously shows the units of energy consumed.

The Influence of Human Capital and Energy Consumption in Covid-19 Pandemic

The point of the study is to analyze, through cointegration techniques, the influence of human capital and power usage on growth in the economy in Indonesia. In a 21-year data analysis from 2000 to 2020, "autoregressive vectors" were used to represent the causal link between variables. The World Bank contributed the data for this research. In this study, we look at evaluating education, health, energy consumption, and GDP in Indonesia. Education has a noticeable influence on power consumption and health, indicating that education can increase awareness of energy consumption and health. energy usage itself is a substantial contributor to economic expansion in Indonesia. The higher the quality of public education, the more aware people are of the importance of health and wiser in consuming energy in encouraging economic growth in Indonesia.

Ensuring power quality and demand-side management through IoT-based smart meters in a developing country

Population increase and the adoption of new power appliances have significantly increased electrical demands. As a result, the utility (electricity supplier) faces difficulties in maintaining the balance between supply and demand. Further, such supply and demand imbalance leads to frequent load-shedding and a drop in power quality (PQ), predominantly in the developing world. Increasing consumer awareness of energy consumption and implementing efficient demand-side management (DSM) algorithms in a metering instrument can be utilized to avoid such issues. Nowadays, access to the internet in developing nations is increasing rapidly. Hence to solve the existing limitations, the internet of things (IoT)-based smart meter (SM) is proposed and its practical application demonstrated in households in Bangladesh as a case study. The proposed SM primarily serves local and online monitoring, bidirectional data transmission, and DSM at the consumer side by maintaining PQ and peak-clipping. The MySql cloud database is used here for data storage and bidirectional data transmission between consumers and the utility. Web applications are developed for real-time data visualization, enabling consumers to track their hourly, daily, and monthly energy consumption by accessing the web page. The SM data shows that the over-voltage varied (from a nominal 220 V) within 15.45–16.36%, and the under-voltage varied between 10.45% and 11.82% from 220 V. The frequency fluctuations are found to be 2.2% under and 2.4% over the nominal value of 50 Hz (standard is nominal value ± 1%). The experimental result showed that the proposed IoT-based SM could ensure the smooth operation of electrical home appliances by maintaining PQ-related parameters (voltage and frequency) within a standard limit. Additionally, the proposed SM also helps to maintain the maximum pre-defined demand of a household during peak times through an appropriate load-clipping algorithm. The utility company can remotely define peak time hours and maximum peak demands when necessary. The real-life demonstration of the SM's operation advocated that this type of IoT-based SM could be easily adapted to maintain the balance between supply and demand through DSM application, and increase consumers' awareness of energy consumption in developing countries.

Parental Attitudes and Awareness of Sustainable Energy Consumption in the Opinion of Young People with Autistic-Spectrum Disorder (ASD); Implications for the Family Budget

Energy efficiency through energy saving is a key trend in the modern world. Analyses have been conducted by scientists in different fields on various target groups that influence sustainable energy consumption. The aim of the research presented in this article is to determine the association between parental attitudes and awareness of sustainable energy consumption in the perception of young people with ASD (13–17 years old). This was a pilot study. The diagnostic-survey method was used, along with The Scale of Parental Attitudes by M. Plopa and an authored survey questionnaire. Deliberate random sampling was applied. A statistical analysis showed a relationship between the acceptance attitude of both parents and the autonomy attitude of one parent—the father—and awareness of sustainable energy consumption among young people with Autistic Spectrum Disorder (ASD). In addition, a critical literature review confirmed the necessity of combining energy awareness and financial awareness, as each aspect, in terms of energy savings, reflects the financial situation of the household and influences the household budget. This is extremely important in the case of vulnerable groups such as households with a person with a disability (e.g., children with ASD). This research is interdisciplinary and covers important scientific fields such as pedagogy, psychology, and economics.

Is high adaptation to the Mediterranean diet effective in increasing ecological footprint awareness? A cross-sectional study from Turkey.

The effect of diet type on sustainable environment is known. It is important to understand the adaptation of individuals with high ecological footprint awareness to the Mediterranean type diet, especially under the assumption that the Mediterranean diet will reduce the ecological footprint. In the study, it is aimed to examine the relationship between the level of adherence to the Mediterranean diet and Ecological Footprint awareness of adults. The sample of the study consisted of 395 individuals with a mean age of 26.2 ± 8.7 years: 293 females (74.2%) and 102 males (25.8%). As the Mediterranean Diet Adherence Scale (MDAS) score of individuals increased, the Ecological Footprint Awareness Scale score also increased. Similarly, a positive correlation was found between the participants' MDAS score and the awareness of food (r=0.157; P=0.002), energy (r=0.133; P=0.008), waste (r=0.149; P=0.003) and water consumption (r=0.101; P=0.045), which are sub-factors of the Ecological Footprint Awareness Scale. As far as is known, this cross-sectional study is one of the first studies on compliance with the Mediterranean diet and awareness of the ecological footprint. The findings of the study show that as individuals' compliance with the Mediterranean diet increases, their awareness of their ecological footprint will also increase. To reach a conclusion on this issue, future studies should evaluate the relationship between adherence to the Mediterranean diet and awareness of food-environmental impact on different samples. © 2021 Society of Chemical Industry.

Enhancement for Content Caching and Delivery with Energy Consumption Awareness in Wireless Networks

The advanced capabilities of smart mobile devices for mobile network users have led to an explosive increase in the loads of networks, mainly driven by the increasing demand for multimedia services. Content caching in users’ devices can potentially enhance both the quality of service for mobile users and network performance. Upon users’ requests and their mobility models, a network can perform proactive caching to reduce network congestion. Furthermore, implementing an optimal caching method can increase the offloading probability, leading to an overall enhanced performance of the network. In this paper, we present an enhanced energy-based content caching and delivery approach for D2D assisted wireless networks. A cost problem is formulated for optimal caching by considering two important constraints establishing D2D links: Signal-to-Noise Ratio (SNR) and Energy Consumption (EC). An effective optimization algorithm, the Frequency Searching Adaptive Bat Algorithm (FSABA), is used to enhance the cached and delivered content, minimizing the total network cost. The system performance is analyzed and evaluated in terms of total network gain, offloading probability and average transmission delay. To validate the proposed enhancement caching approach, extended simulations are provided.