Energy Consumption Habits Research Articles

Smart Grid in the Arctic City

This article presents the evolution of a smart grid in an artic city, and we analyze its development using the Smart Grid Reference Architecture (SGAM) and from historical, technological, and social points of view in an Arctic context. We illustrate the emergence of smart grid application and its relation to energy consumption habits and sustainability. The study includes observations from empirical research conducted using a mixed methods approach. This included two months of organizational ethnographies consisting of interviews with specialists at an electricity utility, shadowing the workers, participant observation in the control center, and a questionnaire survey among local residents. Three stages of transformation in the electricity network were observed: 1) the conversion from analog to digital in network operations, with residents as passive receivers of electricity; 2) introducing digital communications and a digital local area network that allowed residents to report consumption; 3) the electrical network as an evolving smart grid with a digital platform enabling two-ways communication among network actors. People became more active when smart grid applications were introduced in the electricity network and middle-aged men can now better manage their energy consumption, even though their motivation is financial, rather than environmental. At the same time, unplugging from the smart grid becomes increasingly difficult.

Raising user awareness through unsupervised clustering of energy consumption habits

Climate change mitigation requires the urgent reduction of Greenhouse Gas (GHG) emissions, with the building sector as a significant contributor. This study develops a system to identify appliance profiles from smart meter data, enhancing energy consumption awareness and management. These profiles provide valuable insights into users’ consumption patterns and habits, enabling more accurate load consumption prediction and effective appliance scheduling strategies.The proposed approach employs feature extraction techniques to characterise energy consumption profiles, followed by k-means clustering to identify distinct appliance profiles. Eleven representative features are identified, offering comprehensive insights into occupants’ energy usage habits.The evaluation with real-case data shows accurate consumption cycle approximations for each profile, with errors consistently below 10%. Performance assessment using classification metrics indicates well-characterised and representative profiles, outperforming state-of-the-art methods with average values exceeding 0.88 for all considered metrics. This system helps raise occupants’ awareness of appliance energy usage and facilitates optimised scheduling through an Energy Management System (EMS). By promoting more efficient energy consumption, the proposed approach contributes to overall energy reduction and, consequently, lower GHG emissions in the building sector.

Rationalization of Energy Expenditure: Household Behavior in Poland

Background: The implementation of the EU climate and energy policy, along with changes in the legal environment, has led to a significant increase in energy prices in Poland. Consequently, energy expenditures are now a larger part of household budgets. These rising energy costs and the evolving legal landscape are compelling households to invest in energy-saving solutions and modify their energy consumption habits. This article aims to identify the activities of households in Poland regarding the rationalization of energy expenditures. It formulates the following research hypothesis: households invest in energy-saving appliances to rationalize energy expenditures and/or change their behaviors to reduce energy consumption. Methods: The paper is based on primary research conducted using an online questionnaire survey on a sample of 331 respondents in Poland in March and April 2023. Results: A classification tree algorithm was used to identify the level of investment activities and behavioral changes made by households to reduce energy expenditures. The authors found that low-income households and people who fear further energy price increases are the first of all to change their behaviors for more energy-efficient ones. Medium- and high-income households take investment measures. They replace household appliances with more energy-efficient ones and install heat pumps and photovoltaic panels. These investments are motivated by responsible consumption, environmental protection, cleanliness, and the ease of use of the appliances.

Short-term multi-energy consumption forecasting for integrated energy system based on interactive multi-scale convolutional module

Accurate consumption forecasting is of great importance to grasp the energy consumption habits of consumers and promote the stable and efficient operation of integrated energy system (IES). To this end, this paper proposes an interactive multi-scale convolutional module-based short-term multi-energy consumption forecasting method for IES. Firstly, based on multi-scale feature fusion and multi-energy interactive learning, a novel interactive multi-scale convolutional module is proposed to extract and share the coupling information between energy consumption from different scales without increasing network parameters. Then, a short-term multi-energy consumption forecasting method is proposed, where different forecasting network structures are selected in different seasons to make full use of seasonal and coupling characteristics of the energy consumption, thus enhancing prediction performance. Furthermore, a Laplace distribution-based loss function weight optimization method is proposed to dynamically balance the loss magnitude and training speed of joint forecast tasks more robustly. Finally, the effectiveness and superiority of the proposed method are verified by comparative simulation experiments.

Energy literacy among young adults in the European countries

Energy literacy is an essential prerequisite for informed and sustainable energy consumption habits. Our paper focuses on factors influencing young adults’ attitudes and behaviours and their knowledge of sustainable energy consumption practices. The research was conducted in five European countries, Austria, Croatia, Greece, Slovenia and Poland, and data were collected from 219 young adults between the ages of twenty-nine and thirty-nine. The methods used are statistical analyses, including principal component analysis, a technique for analysing large data sets and identifying influencing factors. We used a statistical software package (SPSS) for the analyses. Our analysis revealed five significant factors influencing the energy literacy of young adults. The key factors were education and awareness, investment in energy efficiency, age and gender, climate change and environmental protection, sustainable lifestyle and social environment. Policymakers, educators and other stakeholders can work towards creating a more energy-literate and sustainable society by understanding the key factors that influence energy literacy.

Driving forces and typologies behind household energy consumption disparities in China: A machine learning-based approach

Establishing an intuitive link between driving factors of household energy consumption activities and inequalities is important for the understanding of household heterogeneity in energy consumption behaviours. This paper proposes a novel typology framework based on machine learning approaches and data from 3637 Chinese households in 2014 from 85 cities. Activity-based energy consumption was measured, highlighting inequalities across activities, regions and household types. The results showed significant energy consumption disparities between urban/rural and north/south households, especially in cooking, space heating and vehicle activities. By identifying driving factors of energy consumption, a new household typology classified samples into 6 (all), 6 (urban) and 7 (rural) types. Within these types, households with similar demographic structures, lifestyles and energy consumption habits were clustered. Demographic structure, region, and primary energy demand were used as the basis for the typology. The findings demonstrated how household lifestyle differences explained the cause and underlying driving factors of urban-rural energy consumption inequalities and provided suggestions for city-by-city and type-by-type measurements to support effective low-carbon transformation in cities.

Day-ahead optimization of integrated electricity and thermal system combining multiple types of demand response strategies and situation awareness technology

Under the dual pressure of energy shortage and environmental pollution, relying only on increasing the installed capacity of units and line transmission capacity cannot cope with the conflict between the growth of power demand and the difficulty of grid expansion in the long run. Demand response conducts users to change their energy consumption habits through system-issued electricity prices or incentives, so that the demand of the load side can be adjusted flexibly, which can further enhance the consumption of wind power and improve system economics. Based on the background of diversified energy use, this paper proposes a day-ahead optimal scheduling strategy for integrated electricity and thermal system considering multiple types of demand response. Firstly, the dispatch framework of integrated electricity and thermal system with the situation awareness technology is constructed to address uncertainties of Renewable Energy Sources, thus helping system mitigate uncertain risks. Secondly, the demand response mechanism of power system and regional thermal inertia of thermal system are modeled, respectively, to uncover the principles of load regulation of different energy systems; Then, a day-ahead optimal scheduling model for the integrated thermal and electricity system is developed, and the consumption evaluation index is integrated to indicate energy utilization efficiency; Finally, a combined electric-heat system model with 39-node grid and 6-node heat network is developed, and the positive effects of considering multiple types of demand response and situation awareness technology on promoting the consumption of renewable energy and improving the energy efficiency of the system are verified through the case study.

A Haavelmo grey model based on economic growth and its application to energy industry investments

The energy industry is a major source of greenhouse gas emissions, and energy investment is an important regulatory tool to encourage the energy industry to actively respond to climate change and achieve low-carbon development. Therefore, it is of great practical significance to correctly understand the important role of the energy industry, to predict energy investments objectively and accurately, to achieve scientific and rational investment, and make policy recommendations for the energy production and consumption revolution. In this paper, the Haavelmo model of economic growth is introduced into the energy system, using the characteristics of the continuous form of the model to establish the differential equations for the dynamics of fixed asset investment in the energy industry, and Haavelmo's grey prediction model using the grey difference information principle. Meanwhile, the Python program is used to solve the parameters of the new model, and the mathematical transformation is used to find the time response equation of the new model, and the modeling steps and the modeling flow chart of the model are obtained. Finally, the new model will be applied to two types of energy investments in China: total energy industry investment and investment in electricity, steam, hot water production, and supply industry. Both types of energy use the same modeling object and forecast object, and six cases are compared with three grey forecasting models from different perspectives, and their results show that they are much better than the other three grey forecasting models, demonstrating the effectiveness of the new model to effectively forecast energy investments and improve the efficiency of energy industry investments, cultivate healthy and environmentally friendly energy consumption habits.

DEPLATFORMING THE SMART CITY: GIVING RESIDENTS CONTROL OVER THEIR PERSONAL DATA

Smart city platforms–encompassing mobile apps, cameras, sensors, algorithms, and predictive analytics—function as surveillance tools. Specifically, these Internet-connected devices and services generate troves of data on residents, including real-time geolocation, energy consumption habits, travel patterns, mobile device identifiers, Internet browsing history, phone contacts, credit card numbers, and much more. The proposed project is focused on the City of Long Beach’s vision to use data in ethical ways that avoid reinforcing existing racial biases and discriminatory decision-making. When fully implemented, this digital rights platform will operationalize both privacy and racial equity as priorities for all deployments of smart city technology. First, the platform will feature text and the open-source iconography that visually conveys how the City of Long Beach uses specific technologies, what data the devices collect and how the City utilizes that data. We plan to strategically deploy these information points across Long Beach, physically adjacent to or digitally embedded within civic technologies, e.g., sensors, cameras, small cells, mobile payment kiosks, and a 311 app. The platform will include a feedback application consisting of access (via QR code or hyperlink) to an online dashboard where users may learn additional details, update data collection preferences, and share comments/concerns with local government officials. The ultimate goal is to develop a backend solution that enables residents to opt-out of data collection. The platform will provide residents with a clear understanding of how local government applies predictive and diagnostic analytics to personal data, and will also empower community members by granting them agency.

Low carbon and environmental preservation of residential buildings: MOESOM

Introduction: The crucial transition toward carbon neutrality is developing and adopting low-carbon buildings and communities to achieve the recycling and reuse of resources and to minimize the damage to the natural environment by humans. Energy saving for residential buildings is essential for enhancing cost-effectiveness and redundant energy drain. Considering the increasing attention to energy conservation and the accessibility of sustainable energy sources, common energy-saving solutions expose inherent inadequacies limiting their effectiveness. The ineffectual use of traditional energy sources can result in waste, greater operating costs, and excessive energy consumption in residential structures.Methods: Hence, a Multi-Objective Energy-Saving Optimization Method (MOESOM) has been proposed to optimize energy use and conservation in residential buildings in southern Anhui, China. The proposed approach examines lower operational costs and carbon emissions by using green energy sources and encouraging effective energy consumption habits. The suggested Multi-Objective Energy-Saving Optimization Method technique offers insight into energy saving by utilizing green energy sources and confining energy uses. The multi-objective turns around energy saving and resource usage for decreasing operational costs and averting carbon emissions. Thus, the suggested technique is verified utilizing the Osprey Optimization Algorithm (OOA); the detailed goal is recognized utilizing the multiple objectives described. Based on the progress of low-carbon emissions and energy saving, the number of iterations for augmenting Osprey agents is identified. This agent-based optimization is executed if the novel augmented agent fulfills any of the trailing progression. The emission control level and energy-saving factor are assessed considering the variance between new and old agent progression. This encourages the various objectives to be fulfilled under similar criteria balancing their outcomes.Results and discussion: The output from different Osprey agents is induced for consecutive objectives and optimization factors. Then, the system ensures 8.97% energy savings and 8.04% high objectives compared to the other methods.

Addressing carbon inequity: Examining factors driving the path to just transition

The urgent need to address climate change has led to global efforts towards achieving carbon neutrality. However, it is crucial to recognize the existence of carbon inequity, where certain regions or communities bear a disproportionate burden of carbon emissions compared to others. This paper explores the concept of carbon inequity and highlights the importance of considering driving factors in the pursuit of just energy transition. By examining the socioeconomic, environmental, and policy-related factors that contribute to carbon emissions, a comprehensive understanding of the underlying causes of carbon inequity is gained. The study emphasizes the need to address systemic issues such as economic development patterns, industrial structures, energy consumption habits, and social inequalities that perpetuate carbon inequity. It further discusses the significance of collaborative and inclusive approaches to carbon reduction, focusing on bridging the gap between regions with high and low carbon emissions. Policy implications are derived to guide decision-makers in formulating effective strategies for achieving carbon neutrality while ensuring equity and fairness. By considering both the overarching goal of carbon neutrality and the distributional aspects of carbon emissions, a more holistic and equitable approach can be adopted to accelerate the transition to a sustainable and low-carbon future.

Relationship Between Energy, Fat, and Carbohydrate Consumption Habits in Pregnant Women Chronic Energy Deficiency during the COVID-19 Pandemic: A Systematic Review

Background: Chronic Energy Deficiency (CED) during the COVID-19 pandemic affects the nutritional status of pregnant women. CED is indicated by the size of LiLA < 23.5 cm. This study aims to improve the nutritional status of pregnant women with chronic energy deficiency. Method: The study used a systematic review of relevant articles from databases (Pubmed, ScienceDirect, and Google Scholar) using keywords in Indonesian and English. Nine articles were selected based on the following inclusion criteria: publication from 2017 to 2021, discussing food consumption habits in pregnant women, articles in English or Indonesian, full text, open access, and scientific journals. Results: The results of this study can evaluate the consumption habits of pregnant women CED caused by inadequate food intake and less diverse food. Less food consumption habits in pregnant women are caused by low family income, human resources, education, and lack of family support to consume nutritional food. Conclusion: The study concludes that adequate food intake and good human resource consumption habits can affect pregnant women's nutritional status.

THE RISK OF DEGENERATIVE DISEASES BASED ON ENERGY CONSUMPTION AND EXERCISE HABITS

Obesity triggers the risk of degenerative diseases. An unhealthy diet and insufficient physical activity have been contributing to the rapid increase in obesity prevalence. This study aims at determining the differences in the risk of degenerative diseases based on energy consumption and exercise habits of City Disaster Management Board/DMB members, especially in Denpasar City. This research is an observational study, targeting all members of Denpasar DMB as a population. Samples were taken purposively with a total of 126 people. The risk difference was tested using chi-square, and the estimated risk of degenerative diseases was analyzed using the Mantel-Haentzel odds ratio. The risk of degenerative diseases based on energy consumption shows a significant risk only in the group of people who did not have exercise habits (Q-suare=4.53; p=0.03), with an estimated risk that is also significant (OR=2.48; 95% CI= 1.07 - 5.74). The estimated risk results indicated that exercise habit is a potential confounding variable in the risk of degenerative diseases based on energy consumption. Lowering energy consumption is not effective enough to prevent the increase of degenerative disease risk, and prevention of new risks of degenerative diseases must include regular exercise.

Heating choices and residential willingness to pay for clean heating: Evidence from a household survey in rural China

Due to the severity of climate change, the Chinese government has committed to the carbon neutrality target. Winter heating is a major source of carbon emissions in northern China, and it is urgent to promote the adoption of clean heating in northern rural areas. With indoor-survey data, both two-stage regression and structural equation modeling were used in this study to figure out the exact driving factors and causal relationships of rural households' energy choices considering subjective factors that may influence the energy choices and willingness to pay. Results contradict the energy ladder hypothesis and conclude that it might be caused by energy consumption habits and fuel stacking strategy. Both demographic characteristics and education level have a significant effect on heating energy choices. As for residents' willingness to pay for clean heating, it shows that households’ environmental perceptions, socioeconomic status, and demand for policy subsidies play an important role. Our results make a strong case for policies that shape residents' low-carbon values, promote technological innovation in clean energy, and set reasonable subsidies for clean heating to encourage a clean heating transition.

Students' perception of energy poverty—A comparative analysis between local and exchange university students from Montevideo, Lisbon, and Padua

Energy Poverty (EP) is a growing concern in EU and national policies. Limited research has been conducted on students' perception of EP and vulnerability to EP, especially on how this may be modified if the student is a local or an exchange university student and how this interacts with the season (i.e., summer and winter). Therefore, the present research aims to deepen this understanding by analyzing and comparing students' perceptions of EP and exploring their vulnerability to EP, considering their background and the city they live in, using Montevideo in Uruguay, Lisbon in Portugal, and Padua in Italy, as case studies. To achieve these aims, two populations of university students in each city were surveyed: one of Local Students (LS) and another of Exchange Students (ES). Responses from 295 students to an online survey with 44 questions covering several aspects of EP and energy awareness, such as energy consumption habits, vulnerability drivers, energy equipment, and lived experience in maintaining comfortable internal temperatures, were collected in 2022. Differences between the perception of LS and ES in each city were analyzed, as well as differences in students' perceptions among cities. Although it is difficult to generalize, comfort levels seem to vary according to location, type of students, and season, but according to the results, there seems to be an interaction between these three factors. According to this research, most students did not identify themselves as living in EP, but several populations perceived discomfort in both winter and summer, showing their vulnerability to EP.

A self-cleaning nanoparticle polymer hybrid cooling film in humid environment

Passive daytime radiative cooling (PDRC) technologies that both reflect sunlight and pump heat out of objects on Earth into cold outer space, have attracted considerable attention in recent years. However, conventional PDRC materials face numerous limitations, including costly and complex fabrication methods, as well as performance losses due to surface contamination and humid climates. Here, we present a new radiative cooling structure consisting of a hydrophobic SiO2 nanosphere top layer, a polymethyl methacrylate (PMMA) middle layer, and a metallic Ag bottom layer. The hydrophobic SiO2 nanospheres layer provides a low surface energy nanoscale textured surface for self-cleaning, with a maximum surface contact angle of 151°. The as-prepared film has a high solar reflectivity (∼96.6 %) and a strong broadband infrared emittance (∼88 %). The calculated net cooling power at 27 °C can reach 106.9 W·m−2 and the predicted subambient cooling is 15 °C at night and 10 °C during the day in the dry environment. The outdoor experiments are carried out in a hot and humid city (Xiamen, China) and realize a promisingly subambient cooling of ∼6 °C at night and even ∼3.5 °C during a sunny summer day. These findings could have a substantial impact on energy consumption habits in hot and humid regions around the world.

Intelligent Edge-Based Recommender System for Internet of Energy Applications

Preserving energy in households and office buildings is a significant challenge, mainly due to the recent shortage of energy resources, the uprising of the current environmental problems, and the global lack of utilizing energy-saving technologies. Not to mention, within some regions, COVID-19 social distancing measures have led to a temporary transfer of energy demand from commercial and urban centers to residential areas, causing an increased use and higher charges, and in turn, creating economic impacts on customers. Therefore, the marketplace could benefit from developing an Internet of Things ecosystem that monitors energy consumption habits and promptly recommends action to facilitate energy efficiency. This article aims to present the full integration of a proposed energy efficiency framework into the Home-Assistant platform using an edge-based architecture. End users can visualize their consumption patterns as well as ambient environmental data using the Home-Assistant user interface. More notably, explainable energy-saving recommendations are delivered to end users in the form of notifications via the mobile application to facilitate habit change. In this context, to the best of the authors’ knowledge, this is the first attempt to develop and implement an energy-saving recommender system on edge devices. Thus, ensuring better privacy preservation since data are processed locally on the edge, without the need to transmit them to remote servers, as is the case with cloudlet platforms.

From Passive to Active: The Paradigm Shift of Straw Collection

This paper takes the centralized biogas production project in the energy utilization of straw as a hypothetical item in investigation to discuss the straw collection mode based on the wishes of farmers. Through surveys of farmers in Shandong and Hebei provinces, under the current straw collection price, we found that 85% of farmers have the willingness to actively collect and transport straw, and the longest distance for active transportation is 3.22 km. The willingness of farmers to actively transport is not only affected by personal characteristics, family characteristics, and current energy consumption habits, but also the characteristics of behavioral intervention variables such as knowledge, attitude, and practice of environmental protection also significantly affect the distance of farmers’ active transportation. The behavioral intervention variables of these non-economic factors can be interfered and improved through multiple conventional propaganda tools. Therefore, it is necessary to establish a collection and storage point construction model based on the willingness of farmers to realize the transformation of the straw collection model from passive to active. This method also has an important reference value for most straw energy utilization projects. It will have an important impact on the planning, design, and operation of the project.

Impact of financial inclusion and globalization on environmental quality: evidence from G20 economies.

Sustainable development and addressing climate change are among the most pressing issues faced by countries around the world. This research investigates the dynamic associations between financial inclusion, globalization and CO2 emissions of G20 nations from 2005 to 2018, considering the effects of industrial structure, corruption, green energy utilization and economic growth as control variables. In this study, both financial inclusion and globalization index were measured using principal component analysis (PCA). This study examines long-term associations using cross-sectional augmented autoregressive distributed lag (CS-ARDL) technique that offers more accurate outcomes. In addition, the VECM Granger causality method was applied to find causal relationships between study variables. Findings show that in financial inclusion, globalization has positive significant effect on carbon emissions. Moreover, corruption and economic have positive impact on carbon emissions, and renewable energy shows negative impact on environmental quality. The findings of this research are critical for achieving sustainable development and pollution control goals. Governments need to work to bring into line the financial inclusion goals with renewable energy consumption habits and environmental strategies.

A Collaborative Optimization Model for Integrated Energy System Considering Multi-Load Demand Response

The integrated demand response strategy participates in the coordinated operation of the integrated energy system, which can effectively improve the flexibility and stability of the system operation. This paper adopts a multiple load demand response strategy to guide users’ energy consumption habits. Firstly, the cooperative operation structure of integrated energy system considering comprehensive demand response is designed by analyzing the characteristics of multiple loads. Secondly, according to the interactive relationship between the output exchange power and the demand response adjustment of units, a two-stage collaborative optimization model is established. Finally, results show that considering the demand response of electricity-heat-gas load requires higher output power flexibility of the generators and enhances the ability of the system to participate in the demand response. The overall economic benefit of the system can be improved, but the comprehensive satisfaction of users will be reduced.