Understanding Of Energy Consumption Research Articles

Redefining realistic and stochastic occupancy schedules and patterns for residential buildings in Jordan

Occupant behavior significantly influences building energy consumption, underscoring the importance of understanding the interplay between energy usage and occupant behavior. This integration is vital for evaluating how occupants and their interactions impact building energy consumption patterns. Despite widespread recognition of the importance of behavior-centric approaches in achieving sustainable architecture, there remains a gap in understanding human behavior, particularly in performance-based design. This study presents realistic and stochastic occupancy schedules for residential buildings in Jordan. This was achieved using a mixed-method approach involving a survey, data classification, clustering processes, and a co-simulation approach combining Rhino Grasshopper and Python. The results contributed to refining the general characteristics of each cluster within different age groups, encompassing various patterns of interaction, tolerance levels or levels of sensing, energy conservation consciousness, and occupancy patterns. A significant correlation was also observed between age groups, occupancy schedules, and energy consumption behaviors. The simulation also evaluated the effects of stochastic behavior patterns on the prediction of the energy performance of residential buildings. The results confirm the significant variation in energy use intensity levels between realistic occupancy schedules and fixed schedules for residential buildings. The results also prove that using realistic occupancy schedules for each age group significantly affects energy consumption behavior through occupant-based behaviors. This confirms the pivotal role of dynamic schedules and occupant behaviors in understanding and shaping energy consumption patterns and realistically determining the efficiency of building systems.

Localized environmental governance for carbon dioxide removal in small island developing states: Challenges and opportunities

Accelerating the net-zero economy requires understanding energy consumption patterns, renewable technology attitudes, and acceptance of sustainable energy systems. This study explores how behavioral modification campaigns reduce residential energy use, promote energy-efficient products, and improve consumption patterns. Decentralization and digitization of energy infrastructure have spawned new markets and business models, giving customers more energy control and improving efficiency. The unique regulatory structure of Ireland's single power market makes it a compelling case study. The study examines Irish consumers' sustainable energy beliefs and behaviors from 1992 to 2022 using a 40-question survey. The data show that smart energy solutions for carbon-conscious families have pros and cons. Consumer knowledge of environmental issues and willingness to accept new energy technology are high, but cost and perceived complexity remain impediments to wider implementation. The research emphasizes technical innovation and behavioral change in energy reduction and greener energy promotion. These findings can help engineers, industry stakeholders, and politicians reach climate policy goals and carbon neutrality. Policymakers should support smart energy adoption, reduce cost barriers, and simplify the process. A fair and sustainable net-zero economy transition requires consumer education and decentralized energy market policies. This research underscores the importance of customized policies that leverage technology and behavior to mitigate the effects of climate change.

Estimating Energy Needs for Climate-Controlled Greenhouses in Syria with a Software Tool

Amid the current conditions in Syria, the study of energy consumption within plastic greenhouses emerges as a fundamental element in the agricultural economy, especially in areas subject to extreme climate variations. With many thermal power stations ceasing operation due to conflicts and the diminishing sources of energy, understanding energy consumption becomes more urgent to enhance productivity and reduce costs. Successful management of protected agriculture requires in-depth knowledge of weather dynamics and the optimal environmental conditions for crops. To implement effective management of plastic greenhouses, it is essential to recognize how climatic fluctuations affect plant growth and production throughout the various seasons. Heating systems form a significant part of the costs in constructing plastic greenhouses, and deficiencies in these systems can lead to negative impacts on quality, quantity, duration of cultivation, and production volume. Therefore, accurately calculating heating costs is crucial for reducing operational expenses. This study included the development of a computer program to determine the heating needs of plastic greenhouses, considering various factors such as the geographical location of the greenhouse, crop type, covering materials, heating system used, and land area. The results showed that Syria needs 4.56 megawatts of energy for the greenhouses, with the Tartus Governorate consuming the largest share, with energy consumption rates in Tartus, Latakia, Homs, and Damascus countryside amounting to 3.6, 0.3, 0.51, and 0.19 megawatts, respectively. The crops of tomatoes, vegetables, strawberries, and tropical plants consumed 2.2, 1.66, 2.21, and 0.244 megawatts of energy, respectively. This study is an important step towards achieving sustainable and efficient agriculture that contributes to supporting the economy and protecting the environment in Syria.

Development of a georeferenced atlas of energy renovation in residential buildings using GIS tool

Abstract This paper presents a method for modelling the energy needs for heating the residential building stock on a macro scale. The method is based on the use of Geographic Information Systems (GIS) tools to manage various existing geolocalised databases and the collection of field data. It combines the calculation of energy demand with the estimation of solar gains received by the building envelope. In order to estimate the heat energy balance, a description of the building geometry is necessary. The geometrical characteristics of the envelope are extracted from the building entities information available in the spatial database. These parameters are then enriched and cross-referenced with other data gathered from the government’s complex taxation files. The TABULA buildings typology was chosen because it is an exhaustive European classification adapted for use in GIS utilities. The objective of this work is to produce a georeferenced atlas that can identify potential energy savings on large zones, such as urban areas. An understanding of energy consumption at this scale is crucial for the effective management of massively scaled energy-saving solutions. The atlas will promote energy efficiency in the residential sector and guide environmental policy measures by identifying priority areas for renovation.

Comparative Study of Energy Consumption Intensity: A Case Study of the Faculty of Electrical Engineering and Informatics (FEI) Versus the Faculty of Chemical Technology (FChT) in the City of Pardubice

Abstract The study’s comparative aspect between the Faculty of Electrical Engineering and Informatics (FEI) and the Faculty of Chemical Technology (FChT) buildings is pivotal for elucidating nuanced differences in energy consumption practices within academic institutions, facilitating targeted energy efficiency measures. The research methodology adopts a comparative case study approach to examine two focal research buildings. Analysis entails computing energy consumption intensity per unit area for both buildings and comparing energy consumption data to identify disparities. The need for understanding energy consumption patterns and improving energy efficiency in academic infrastructure is directly addressed through variables studied in the comparative analysis, including energy consumption comparison, annual and monthly variations, ratio, and intensity. The data indicates that the average heating intensity in the FChT building surpasses that of the FEI building by a factor of 2.3, signifying significant disparities in heating energy consumption trends between the two structures and suggesting potential areas for targeted energy efficiency interventions. Moreover, the average electrical intensity in the FChT building is over 1.7 times higher than in the FEI building. Interestingly, despite the FChT building being 2.6 times larger than the FEI, its intensity does not always correspond with the increase in building area, indicating additional factors influencing energy consumption.

Information flow dynamics between cryptocurrency returns and electricity consumption: A comparative analysis of Bitcoin and Ethereum

Understanding energy consumption associated with cryptocurrency mining gained increasing attention, with the literature focusing mainly on Bitcoin. This study uses data from the two energy consumption indices, to estimate static and dynamic transfer entropies. The results provide a nuanced understanding of the bidirectional relationships and their implications. The dominant direction of information flow for Bitcoin is from electricity consumption to returns, while for Ethereum, it is from returns to electricity consumption, suggesting that Ethereum's returns significantly impact electricity consumption patterns. Results highlight the need for policies that integrate energy forecasting and environmental sustainability considerations and has significant implications for policymaking.

Impact of Energy Use on Air Pollution: Evidence from OCED Countries

Air pollution poses significant environmental and health risks, with numerous studies suggesting its dire consequences ranging from respiratory diseases to global climate change implications. In this context, understanding energy consumption patterns and their impact on air pollution is critical, especially in developed nations with high energy consumption rates. This paper empirically analyzes the impact of energy use on air pollution in OECD countries – a group of nations that play a pivotal role in global energy consumption and policy-making. Despite their economic advancements, the implications of their energy choices on air quality have not been extensively studied, presenting a gap in the literature. Spanning 12 years from 2010 to 2021, our research encompasses a total of 456 observations, employing panel data. We aim to unveil the relationship between energy use, both traditional and renewable and air pollution levels. The variables studied include Energy use per capita, Alternative and nuclear energy, Greenhouse gas emissions, Energy imports, Access to electricity, and the production of renewable energy (air, water, solar, geothermal). Leveraging various econometric models such as OLS, OLS Robust, fixed, and random effects models, our findings reveal that electricity use exacerbates air pollution levels. Particularly, the increase in carbon dioxide, coupled with the rising access to electricity, deteriorates air quality. In contrast, electricity sourced from renewables like water, wind, nuclear, and geothermal energy aids in mitigating air pollution, emphasizing the importance of sustainable energy choices for future policy considerations in OECD nations.

Spatial Model for Energy Consumption of LEED-Certified Buildings

In this research endeavor, we undertook a comprehensive examination of the factors influencing the energy consumption of LEED-certified buildings, employing both a general linear regression model and a spatial delayed regression model. Gaining a profound understanding of energy utilization patterns within LEED-certified structures can significantly contribute to advancing eco-friendly construction practices. Our investigation draws upon data from a 2010 study conducted at the University of Wisconsin—Madison (UW), encompassing various independent variables, such as temperature, that exhibit some degree of correlation with energy consumption in LEED-certified buildings. The principal objective of this study is twofold: firstly, to ascertain the significance of specific exogenous variables, notably temperature, and secondly, to explore the impact of spatial factors, such as function and location, on energy usage. Our research framework encompasses meticulous data collection and rigorous analysis, culminating in the presentation and discussion of our findings. Notably, our study unveils intriguing insights. Contrary to conventional assumptions, we discovered that the energy consumption of LEED-certified buildings does not exhibit a robust linear association with average annual temperature, the count of power plants within a 50 mile radius, or the LEED rating itself. However, our spatial regression models unveil a compelling narrative: the geographic distribution and functional diversity of distinct LEED buildings wield discernible influence over energy consumption patterns. The implications of our research resonate profoundly in the realm of LEED-certified building design and construction. Architects, builders, and stakeholders should consider the nuanced interplay of spatial variables and geographical positioning in the pursuit of optimal energy efficiency. Moreover, our findings stimulate further inquiry in this field, paving the way for future investigations aimed at refining sustainable building practices and enhancing our understanding of energy consumption within LEED-certified structures.

Applying Fuzzy Time Series for Developing Forecasting Electricity Demand Models

Managing the energy produced to support industries and various human activities is highly relevant nowadays. Companies in the electricity markets of each country analyze the generation, transmission, and distribution of energy to meet the energy needs of various sectors and industries. Electrical markets emerge to economically analyze everything related to energy generation, transmission, and distribution. The demand for electric energy is crucial in determining the amount of energy needed to meet the requirements of an individual or a group of consumers. But energy consumption often exhibits random behavior, making it challenging to develop accurate prediction models. The analysis and understanding of energy consumption are essential for energy generation. Developing models to forecast energy demand is necessary for improving generation and consumption management. Given the energy variable’s stochastic nature, this work’s main objective is to explore different configurations and parameters using specialized libraries in Python and Google Collaboratory. The aim is to develop a model for forecasting electric power demand using fuzzy logic. This study compares the proposed solution with previously developed machine learning systems to create a highly accurate forecast model for demand values. The data used in this work was collected by the European Network of Transmission System Operators of Electricity (ENTSO-E) from 2015 to 2019. As a significant outcome, this research presents a model surpassing previous solutions’ predictive performance. Using Mean Absolute Percentage Error (MAPE), the results demonstrate the significance of set weighting for achieving excellent performance in fuzzy models. This is because having more relevant fuzzy sets allows for inference rules and, subsequently, more accurate demand forecasts. The results also allow applying the solution model to other forecast scenarios with similar contexts.

Prosumers integration in aggregated demand response systems

In recent years, the increasing popularity of renewable energy and energy-efficient technologies is creating a new movement towards more sustainable communities. Understanding energy consumption is important for the optimisation of resources and the implementation of ecological trends. This paper integrates electricity consumers into a cooperative framework for planning sustainable smart communities through aggregators, which reallocates consumers’ demand according to available renewable energy supply collected from consumers and service providers. The aggregated demand response also includes consumers involved in energy production activities through microgeneration capabilities. A characterisation study of the different types of demand preferences is performed by defining scenarios of communities and consumers’ behaviours, which are validated through a reputation factor. The results show that the system adequately manages demand reallocation following the preferences and contribution of consumers and/or prosumers. Besides, this research analyses the current energy policy concerning demand flexibility, demand aggregation and microgeneration capacity, and their regulations in Spain. Finally, microgeneration acceptance, the role of aggregators and prosumers in the scheduling process is also investigated through a series of surveys.

Increasing Energy Efficiency Based on the Kaizen Approach

The energy crisis, an increasing concern of the industrial sector, is characterized by increased energy costs and low energy availability. As part of an effort to increase energy efficiency, the present study assessed the impact of applying lean tools on the energy consumption of a manufacturing organization. Using the Kaizen approach, the organization’s processes were analyzed to identify the main vulnerabilities and make the required changes to increase consumption performance. Several technical improvements were performed to reach increased energy efficiency. Results showed an energy efficiency increase; consumption was reduced by up to 7.5% in the production line, 3.5% in the extruder stage, and up to 20% for the injection stage of the manufacturing process. The results suggest that the organization’s energy reduction targets have been achieved. Standardization of procedures was useful in the development of Kaizen projects which could be applied with certain limitations. The implications of these results for understanding energy consumption reduction are discussed.

Energy Efficient Real-Time E-Healthcare System Based on Fog Computing

The rapid development of Internet of Things (IoT) enabled systems in public and private spaces offers consumers numerous conveniences. Among different internet-connected systems, the use of e-health systems is growing rapidly. The utilization of IoT devices and cloud-fog network technologies have made e-healthcare provision more convenient. While providing valuable services to the healthcare sector, like any other IoT-enable systems it is putting pressure on energy, an essential element of life. Therefore, it is imperative to know the energy consumption model of e-health systems. Considering the importance of energy consumption in IoT-based systems, this article develops a cloud-fog-based e-health system and makes it energy efficient by understanding energy consumption at different layers of communication. Moreover, how fog integration with the cloud reduces energy consumption and delays at different stages of communication is discussed.

Data Driven Disaggregation Method for Electricity Based Energy Consumption for Smart Homes

Sustainable energy systems must be capable of ensuring sustainable development by providing affordable and reliable energy to consumers. Hence, knowledge and understanding of energy consumption in the residential sector are indispensable for energy preservation and energy efficiency which can only be possible with the help of consumer participation. New energy efficiency methods are developed due to the global adoption of smart meters that monitor and communicate residential energy consumption. Moreover, energy monitoring of each appliance is not feasible, as it is a costly solution. Therefore, energy consumption disaggregation is an answer for cost-cutting and energy saving. Contrary to the non-intrusive load monitoring (NILM) approaches, which are based on high-frequency power signals, we propose a data-driven algorithm that requires only a time-series energy meter dataset, a few appliances’ data, and energy consumption data from a consumer-based online questionnaire. Afterward, the proposed algorithm disaggregates whole house energy consumption into nine different energy consumption sectors such as lighting, kitchen, cooling, heating, etc. The energy consumption disaggregation algorithm is applied to datasets of 10 homes under experimentation. One of the homes provides us with the knowledge of 96.8% energy consumption, where only 28% knowledge is reported by monitoring plugs and 68% knowledge obtained by unmonitored means. Finally, the energy consumption obtained by the algorithm is compared with actual energy consumption, which shows the excellent functioning of the developed method.

Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach

Understanding energy consumption patterns is crucial for energy demand-side management. Unlike traditional data mining or machine learning-based methods, this paper presents visual analysis methods for exploring energy consumption data from spatial, temporal, and spatiotemporal dimensions, including variability, segmentation, and energy demand shifts. To support the proposed methods, we develop a visual analysis tool that allows users to explore consumption data and validate their hypotheses based on visual results through human–client–server interactions. In particular, we propose a novel potential flow-based method to model energy demand shift patterns and have integrated it into the proposed analysis tool. We comprehensively evaluate the proposed methods and the tool using real-world electricity consumption data from the Shanghai Pudong district, and compare with traditional data mining methods. The results demonstrated the effectiveness and superiority of the proposed visual analysis methods, including its ability to discover the spatiotemporal variability of energy demand, customer groups, and demand shift patterns across different geographical areas and time horizons. All results can be well explained by knowledge of the energy consumption in the study region.

Drone flight data reveal energy and greenhouse gas emissions savings for very small package delivery

SummaryUncrewed aerial vehicles (UAVs) for last-mile deliveries will affect the energy productivity of delivery and require new methods to understand energy consumption and greenhouse gas (GHG) emissions. We combine empirical testing of 188 quadcopter flights across a range of speeds with a first-principles analysis to develop a usable energy model and a machine-learning algorithm to assess energy across takeoff, cruise, and landing. Our model shows that an electric quadcopter drone with a very small package (0.5 kg) would consume approximately 0.08 MJ/km and result in 70 g of CO2e per package in the United States. We compare drone delivery with other vehicles and show that energy per package delivered by drones (0.33 MJ/package) can be up to 94% lower than conventional transportation modes, with only electric cargo bicycles providing lower GHGs/package. Our open model and coefficients can assist stakeholders in understanding and improving the sustainability of small package delivery.

Energy Consumption Connection of Industrial Sector Based on Industrial Link Theory: A Case Study of China

China’s energy consumption and its growth trend determine the domestic energy production and supply pattern. The understanding of energy consumption and its changes will help to enhance urban resilience. Based on the improved input-output model and hypothesis extraction model, this paper aims at constructing the energy-industry connection model, analyzing the energy transfer implied in the economic activities of various industrial sectors, and examining the energy transfer effect between the supply-side (SS) and demand-side industry sectors of the Shaanxi Province. The results showed that, in 2017, the energy industrial sector was the most energy consumption industry in Shaanxi Province. The industrial energy sector belonged to the net energy output industrial sector, and the energy products were transferred to other industrial sectors. This paper can provide a scientific basis for the energy SS reform, adjusting the industrial layout of regions in the Yellow River Basin.

Causality Relationship between Electric Power Consumption and Economic Growth in Malaysia and Thailand: ARDL Bound Testing Approach

This paper examined the causal relationships between per capita electric power consumption and economic growth per capita in Malaysia and Thailand. The data used in this study was the yearly data from 1971 to 2014. The ARDL and Granger causality approaches were employed. Overall, the empirical results showed that it had established a long-run relationship between electric power consumption and economic growth. Moreover, the Granger causality approach recognized a one-way causal direction flowing from economic growth to electric power consumption in Malaysia. However, for Thailand, empirical results had no long-run relationship between electric power consumption and economic growth. Therefore, the Granger causality approach had recognized no way of causal direction flowing from electric power consumption to economic growth. Finally, the empirical results of this study provided policymakers a better understanding of energy consumption and economic growth nexus to formulate energy policy in Malaysia and Thailand. In addition, the government of Malaysia should consider the economic situation when implementing the relevant energy policies.

THE BEHAVIOUR OF HOUSEHOLDS TOWARDS ELECTRICITY CONSUMPTION: A CASE STUDY AT SEREMBAN

Understanding energy consumption behaviours among households is an effective way to encourage energy conservation and improve energy efficiency. This article examines households’ knowledge, awareness, commitment, attitude, and behaviour towards electricity consumption. The sample of the study consists of 360 urban households in an intermediate city, Seremban. The findings reveal that cost-saving via government initiatives is a crucial factor influencing the households’ energy consumption knowledge. The correlation analysis also revealed that knowledge, awareness, commitment, and attitude correlated significantly with the households’ electricity consumption behaviour. The evidence suggests that the government and power companies implement strict regulations and technological advances to promote energy conservation and improve energy efficiency among households. The findings can better understand where attention should be directed and the measures for long-term energy conservation, climate change mitigation, and sustainable development.

A dynamic energy benchmarking methodology on room level for energy performance evaluation

Building energy benchmarking is the basis of energy performance assessment. Most of existing energy benchmarking studies are aimed at building-level or subsystem-level energy performance evaluation, which cannot provide detailed energy performance information inside the building. This study aims to develop a dynamic room-level energy benchmarking method to evaluate energy performance of all rooms in office building. Taking a government office building as a case study, this paper analyses the influence of room types and energy consumption patterns on energy consumption, and proposes a K-shape clustering method to mine five typical energy consumption patterns in this building to describe occupancy behaviors of nearly 1000 rooms. The quartile method is used to establish energy benchmarks for different room types and energy consumption patterns. Finally, this study takes staff room as a case to evaluate the energy performance of rooms. Through the establishment of room-level energy benchmark, it is found that the total energy saving potential accounts for 24.69% of the total energy consumption of all the rooms. This method provides detailed information to help building managers and engineers understand energy consumption status on room level better and help them develop targeted energy-saving measures. Moreover, it should be noted that this energy benchmarking could be periodically updated with energy performance due to the increasing awareness of occupants for energy conservation.

Understanding energy consumption of hydraulic press during drawing process

Prediction of manufacturing equipment’s energy consumption plays an important role in selecting appropriate process parameters for energy saving. However, it is difficult to model the energy consumption of metal forming equipment during the drawing process characterized by variable process parameters and dynamic loads. In this paper, a model was developed to quantify the energy consumption of a typical hydraulic press during the drawing process under a range of operating conditions. The hydraulic press studied consists of two different circuits and two controllable parameters, i.e., punch velocity and blank holder force can be set for drawing processes performed. To start, the energy flow during the drawing process was analyzed by using the energy conversion mechanism and components’ specifications to understand the detailed energy characteristics of each circuit. Then, orthogonal experiments including these two parameters at three levels were designed and carried out to find significant parameters. Finally, the contribution of each parameter, which was obtained from the analysis of variance (ANOVA) of the experimental results, was used to simplify energy flow modeling efforts. Consequently, a model of the press was established and used to predict the energy consumption of the drawing processes with different parameters. Good agreement with the experimental results was observed. The model can be used to identify parameters for minimal energy consumption, while the approach could be adapted to develop an energy consumption model for different hydraulic equipment.