Energy Efficiency Objectives Research Articles

Sustainable Business Practices through IoT: Energy Efficiency and Cost Reductions

This is the age of industrial transformation that is being driven by the Internet of Things (IoT). It offers very unique chance to maximize productivity excellence and foster economic expansion while having the opportunity to meet environmental concerns. This comprehensive study looks into not only the IoT contribution to energy efficiency and cost reduction but also potential use in areas of administrative operations with the same objective of energy efficiency and cost reduction. With the aid of rigorous mixed methods research that brings together qualitative data from multiple industries, against the backdrop of carefully selected case studies showing the actual benefits of IoT installations, the paper seeks to give a clear picture of the practical advantages of IoT-powered organizations. The research presents information regarding how IoT technologies function to optimize energy applications, enhance processes, and cut down waste through their ability to collect data in real-time and manage systems. Besides, it will analyze the problems companies may run into while adding IoT solutions, such as technological complexity, initial expenditure, and security matters. On the other hand, the research also designs the tactical moves that the companies can opt for as an alternative to completely eradicate these barriers and successfully deploy the IoT technology. Through the systematic study of the role of the Internet of Things on green business practices policymakers, business leaders, and technologists will gain valuable insights which can be applied in whatever levels they are working from. This policy is based on a systematic approach to IoT adoption, which, inter alia, suggests the organization of the project, statistical analysis, and improvement of the present situation to get the best economic and environmental state of affairs. This will further the comprehension of the power of IoT technology in both developing sustainability and increasing profitability, all indications that it is not just a technological advantage but rather a basic instrument for the future sustainability of business practices.

The Role of State Aid in the Achievement of the Energy Efficiency Objective in the Food Industry—The Example of Poland

The Role of State Aid in the Achievement of the Energy Efficiency Objective in the Food Industry—The Example of Poland

Eco-driving control for CAVs at signalized intersections: Adapting to traffic uncertainties

Eco-driving control for connected and automated vehicles (CAVs) presents significant energy-saving potential by enabling smooth, non-stop travel through signalized intersections. This study addresses the challenge of traffic uncertainties under congestion conditions, which often lead to trajectory planning errors and increased energy consumption. We propose a hierarchical eco-driving approach for CAVs that incorporates both upper-layer trajectory planning and lower-layer velocity control. The upper layer employs a dynamic programming-based model for time-based optimal eco-driving reference trajectory planning, focusing on objectives of energy efficiency and high mobility. In the lower layer, a model predictive control (MPC)-based two-mode velocity controller is formulated to adeptly handle infeasible or unreasonable reference trajectories. Evaluation results demonstrate that the proposed approach outperforms existing strategies, achieving average improvements of 3.99% in CO2 emissions, 3.65% in fuel consumption, and 2.61% in vehicle mobility. We suggest that a comprehensive evaluation framework for eco-driving performance of CAVs should be established with respect to diverse traffic conditions, regulating CAV market entry from an environmental perspective.

Energy-Efficient Object Detection: Impact of Weight Clustering for Different Arithmetic Representations

Energy-Efficient Object Detection: Impact of Weight Clustering for Different Arithmetic Representations

Sustainable energy skills in the construction sector 3.0: expertise, lessons learned, and developed methodologies on energy efficiency skills competencies, and qualifications

The European construction sector is one of the areas with the highest potential when it comes to reducing annual energy consumption. Facing many challenges to achieve ambitious energy efficiency objectives, this sector aims to proceed by applying successful training initiatives and supporting policy instruments. For this reason, the creation of BUILD UP Skills, an EU Initiative to improve the qualification and skills of Europe’s building workers, acted as a springboard to stimulate the demand for energy efficiency skills. Furthermore, focusing on the continuing education of craftsmen and other on-site building workers, this initiative is home to most of the projects presented during the Sustainable Energy Skills in the Construction sector workshops 1.0 and 2.0 and 3.0, presented at SP2020, SP2021 and SP 2022, respectively. Consisting of nine Horizon 2020 projects—PRO-Heritage, TRAIN4SUSTAIN, INSTRUCT, BUSLeague, the nZEB Roadshow, SEEtheSkills, ARISE, BUS-GoCircular and nZEB Ready—the objective of the third edition of the workshop was to share expertise, lessons learned, and developed methodologies on energy efficiency skills competencies, and qualifications across the building design, operation, and maintenance value chain.

Energy Efficiency Measures in Public Sector: An Empirical Analysis of the Determinants of Adoption of Low-Cost Energy Efficiency

This research delves into the intricate interplay between leadership styles, eco-innovation, and energy efficiency in the Indonesian public sector, examining the roles of green transformational leadership and ethical leadership. Acknowledging the pivotal role of public sector institutions in fostering sustainable practices, the study addresses the ongoing debate surrounding the efficacy of these leadership styles in the context of eco-innovation and energy efficiency. Using a quantitative research approach, data was collected from 227 faculty members across various Indonesian public sector institutions through a questionnaire. SmartPLS 4 software was employed for data analysis. The findings reveal that green transformational leadership positively influences eco-innovation, yet it does not directly impact energy efficiency. Conversely, ethical leadership does not directly drive eco-innovation but demonstrates a positive association with energy efficiency. Eco-innovation emerges as a direct contributor to energy efficiency. Significantly, the research identifies eco-innovation as a mediator between green transformational leadership and energy efficiency. However, this mediating effect is not observed in the relationship between ethical leadership and energy efficiency. The study underscores the need for leadership emphasizing environmental consciousness and ethics, coupled with the promotion of eco-innovation, to effectively attain sustainability and energy efficiency objectives within the Indonesian public sector.

An Analysis of the Development of the Cogeneration Sector in Spain: A Comprehensive Review of the Period 1980–2020 from a Regulatory Perspective

Combined Heat and Power (CHP) has been identified by the EU as a powerful resource capable of making substantial contributions to energy savings and reducing GHG emissions. Spain’s effort to promote CHP has been prolific since the 1980s. In this regard, there have been various Laws, Royal Decrees (RDs) and European Union (EU) Directives addressed to reach the national objectives set for the CHP sector. Despite these attempts, the evolution and growth of installed CHP capacity has been irregular, compared to other technologies. Likewise, the academic treatment of the Spanish CHP evolution has not deserved the same attention as other technologies such as wind, photovoltaic and thermal solar systems. As a result, this article is aimed at providing a comprehensive overview of the regulatory frameworks applied to the Spanish CHP sector and analysing the reasons behind the variable evolution of the installed CHP capacity. The study covers the legislative context from 1980 to 2020, describing the evolution during both the pre-liberalization and liberalization periods, highlighting the modifications in economic policies that affected self-producers and the so-called Special Regime (SR) for CHP, and examining the challenges faced during the cost containment measures that followed. The manuscript finds and explains the connection between the regulatory framework and the evolution of installed CHP capacity in Spain. Likewise, the connection between the industrial situation and the promotion of CHP, as well as the influence of the Spanish Electricity Sector (SES)’s liberalization on the CHP sector are also pointed out. The paper intends to provide valuable insights for CHP experts and policymakers by showcasing the importance of aligning regulatory measures with the objectives of energy efficiency. It also serves as a reference for countries in various stages of promoting CHP, and provides evidence for the importance of stable energy-policy control mechanisms.

A bi-objective optimization model of metro trains considering energy conservation and passenger waiting time

Continuous growth of energy consumption for metro systems has raised the public's concern. Meanwhile, both operators and passengers pursue for efficient metro services, especially during peak hours. This paper aims at designing a bi-objective optimization model and a new solution approach so that the energy conservation of metro trains and less passenger waiting time can be achieved. Firstly, considering complex routes, a multi-particle train operation model with the objectives of punctuality, parking accuracy, energy efficiency and safety provided is established for the minimization of traction energy consumption through investigating the optimal force coefficients and coast positions, while suitable dwell times and headway times of metro trains are found trough building a timetable model, which balances the regenerative energy, transfer and non-transfer passenger waiting time. Secondly, a deep reinforcement learning (DRL) and non-dominated sorting genetic algorithm II (NSGA-II) based two-layer solution approach is introduced for model calculation. Finally, comparison shows that the proposed model can achieve the energy conservation of metro trains by 9.16% with passenger waiting time decreased by 15.88%. More experiments further demonstrate the superiority of the developed model and solution approach.

Research on the operation optimization of Integrated Energy System based on dual model refined simulation of thermal network

Accurately assessing energy flow is of paramount importance for achieving the dual objectives of economic efficiency and energy efficiency in the operation of electric-thermal integrated energy systems. The generic thermal network model is commonly used to describe operating characteristics under baseline conditions. However, due to the high-dimensional nonlinear nature of solving its parameters, traditional methods have limitations. In this study, to address this issue, we conduct dual-mode detailed simulation analysis for the hydraulic and thermal processes of the thermal network. The finite difference method is employed to calculate dynamic parameters. Based on this, a network loss model is constructed using the mean square deviation method. To achieve the dual objectives of integrated energy system operation optimization, the NSGA-II algorithm and VIKOR decision rules are applied to obtain the optimal solution. Using these methods and taking a specific electric-thermal integrated energy system in the northern region as an example, we simulate and analyze the data of the system operation under three thermal network modes. The results indicate that the refined thermal network model can reduce the total operating cost to $16,871 and achieve an energy efficiency of 83.219 %, outperforming the results of other operational modes.

Exploration–Exploitation Tradeoff in the Adaptive Information Sampling of Unknown Spatial Fields with Mobile Robots

Adaptive information-sampling approaches enable efficient selection of mobile robots’ waypoints through which the accurate sensing and mapping of a physical process, such as the radiation or field intensity, can be obtained. A key parameter in the informative sampling objective function could be optimized balance the need to explore new information where the uncertainty is very high and to exploit the data sampled so far, with which a great deal of the underlying spatial fields can be obtained, such as the source locations or modalities of the physical process. However, works in the literature have either assumed the robot’s energy is unconstrained or used a homogeneous availability of energy capacity among different robots. Therefore, this paper analyzes the impact of the adaptive information-sampling algorithm’s information function used in exploration and exploitation to achieve a tradeoff between balancing the mapping, localization, and energy efficiency objectives. We use Gaussian process regression (GPR) to predict and estimate confidence bounds, thereby determining each point’s informativeness. Through extensive experimental data, we provide a deeper and holistic perspective on the effect of information function parameters on the prediction map’s accuracy (RMSE), confidence bound (variance), energy consumption (distance), and time spent (sample count) in both single- and multi-robot scenarios. The results provide meaningful insights into choosing the appropriate energy-aware information function parameters based on sensing objectives (e.g., source localization or mapping). Based on our analysis, we can conclude that it would be detrimental to give importance only to the uncertainty of the information function (which would explode the energy needs) or to the predictive mean of the information (which would jeopardize the mapping accuracy). By assigning more importance to the information uncertainly with some non-zero importance to the information value (e.g., 75:25 ratio), it is possible to achieve an optimal tradeoff between exploration and exploitation objectives while keeping the energy requirements manageable.

Multi-Objective Optimization in Solar and Wireless Energy Harvesting the IoT Relay Communication System

In this article, a solar and wireless energy harvesting (SWEH) Internet of Things network is presented. To address energy efficiency problems, we consider an amplify-and-forward relay node that harvests solar and wireless energy using the power splitting protocol to accomplish SWIPT. For the communication model under consideration, we derive the mathematical expressions for throughput and energy efficiency. We investigate the impact of both power transmitted by the source and power-splitting factor on the system’s achievable energy efficiency and the energy harvested. To maximize the contradictory objectives of energy efficiency and energy harvested simultaneously, we propose to use the Pareto-based multi-objective ant lion optimization algorithm (MOALO) and jointly optimize the transmitted power and power-splitting factor. In-depth simulation is used to determine the Pareto optimal values for transmitted power and power-splitting factor and to assess how the proposed algorithm performs in comparison to leading-edge algorithms such as NSGA-II, MOPSO, MOWOA, and MOMVO. The results unambiguously show that the suggested MOALO offers the best Pareto front within a reasonable amount of time. Finally, the results corroborate that the proposed SWEH system outperforms in terms of harvested energy and attainable energy efficiency.

Potential Benefits of Thermal Insulation in Public Buildings: Case of a University Building

Global energy demand continues to rise due to advances in both developed and developing countries. Energy-efficient technologies and eco-friendly policies have been insufficient to counterbalance the increasing demand and, thus, the national strategies of many countries have been shaped by energy conservation considerations. Buildings are responsible for more than one third of the global final energy consumption and the energy use in buildings is expected to grow more than 40% in the next 20 years. Even though the energy-efficient retrofits and thermal insulation of the building envelope have been widely studied in academia, the case of existing public buildings has been largely neglected. To fill the gap, this study investigates the thermal insulation of existing public buildings and unveils its potential benefits. An administrative building of a public university has been the subject of financial analysis to observe the feasibility of insulation applications and to identify the most feasible insulation application. The results reveal that (i) the most feasible application depends considerably on the financial scenarios and (ii) the feasibility of insulation applications is greatly influenced by the building geometry. This study contributes to the literature by demonstrating the feasibility of energy retrofits in an administrative public building and proposing an alternative way to achieve national energy efficiency objectives.

When to switch from heat-up to start-up in the warming-up process of a solid oxide fuel cell: A numerical study and multi-objective planning

Solid oxide fuel cells (SOFCs) at rest need to undergo warming-up process to reach normal performance. This process begins with cell heating (so called heat-up process) and continues with the activation of electrochemical reactions (so called start-up process) until the cell performance reaches steady state. This paper develops a transient numerical model to investigate the influence of the switching temperature (ST), defined as the electrolyte average temperature at the beginning of the start-up process, on the SOFC warming-up process. Also, an optimal ST is proposed by considering the different combinations of the objectives of warming-up time duration, energy efficiency and thermal shock. The results indicate that a lower ST (or an earlier onset of start-up process) results in a shorter warming-up time duration and a greater energy efficiency (as two desirable impacts), but causes a higher thermal shock (as a negative point). Using a multi-objective decision making approach (namely, linear programming technique for multi-dimensional analysis of preference), a ST of 823 K is proposed as an optimal compromise among the three mentioned objectives. However, when the energy efficiency is disregarded, a ST of 998 K is found to be optimum.

Performance Evaluation of Heat Pump Systems for Cold Climate Regions

This research presents a comprehensive investigation into the performance of heat pump systems operating in cold climate regions. Through a mixed-methods approach involving quantitative analysis and qualitative user insights, the study aims to elucidate the intricate dynamics influencing system efficiency, user behavior, and environmental impact. Quantitative data analysis reveals a 20% average decline in the Coefficient of Performance (COP) during extreme cold temperatures, underscoring the challenges of maintaining high efficiency under demanding conditions. Load shifting potential is demonstrated by a 15% reduction in energy consumption during off-peak hours, showcasing the systems' contribution to grid stability and energy efficiency objectives. Qualitative interviews with users uncover preferences for defrost strategies and highlight the importance of system responsiveness. The findings collectively emphasize the need for an integrated approach that amalgamates technological advancements, user preferences, and sustainable practices to optimize heat pump system performance in cold climates. This research contributes valuable insights to the advancement of heating solutions tailored for challenging climatic conditions

Thermal Performance Assessment of Lightweight Aggregate Concrete by Different Test Methods

Structural lightweight aggregate concrete is currently an alternative to normal-weight concrete when thermal insulation properties are required to meet the objectives of energy efficiency and sustainability. The accurate evaluation of the thermal performance is thus essential for designing structural lightweight concrete elements. This paper aims to evaluate the thermal behavior of structural lightweight aggregate concrete, assessed through different tests methods. To this end, a vast experimental campaign was carried out involving specimens produced with several types of lightweight aggregate and different water/cement ratios. The thermal performance was established by thermal conductivity, which was determined according to a modified transient pulse method and a quasi-stationary method, and specific heat capacity, which was determined through a transient pulse method and a heat transfer method. Normal-weight concrete was also tested for comparison purposes. Experimental evidence showed that lightweight aggregate concretes with lower density are associated with up to about 50% lower thermal conductivity and higher specific heat capacity than normal-weight concrete. Moreover, the study demonstrated that the expeditious transient pulse method is suitable for assessing the thermal conductivity of this type of concrete, and that both the transient pulse method and the heat transfer method are adequate to determine the specific heat capacity.

Energy Efficiency Lookouts

Abstract This article aims to carry out a comparative analysis in the European Union and in Romania, in the period 1991-2021, on the achievement of the ambitious energy efficiency objectives, by assessing the achievement of the assumed targets for primary and final energy consumption at the level of the years 2020 and 2030. Since 2007, EU leaders have been concerned about the decarbonisation of the energy sector, a first essential measure being to reduce energy consumption by 20% by 2020. As part of the decarbonisation objectives for 2050, we start from this premise, according to which energy efficiency must be improved, thus accelerating the transition of all member states of the European Union to clean energy and sustainable growth of economies. So, by using energy more efficiently and therefore consuming less, we can contribute both to protecting the environment and to mitigating climate change. The hypothesis from which we start in this study is that the target for 2020 of the level of primary and final energy consumption has been met, due to the negative effects of the COVID-19 pandemic. In order to test this hypothesis, Eurostat data were analyzed using the quantitative research method for both the European Union and Romania. As a result of the analysis carried out, this hypothesis can be fully confirmed, since from 2021, which coincides with the beginning of the recovery of economies, an increase in primary and final energy consumption can be observed again, after a sharp decrease in 2020, even below the level of the targets set for 2020. With regard to the targets assumed by the European Union for 2030, the consumption of primary and final energy is well above the predetermined values. And the conclusions show us with certainty, the need to make an additional effort in order to achieve them.

Distributed Intelligence in Autonomous PEM Fuel Cell Control

A combination of perceptive and deliberative processes is necessary to ensure the efficient and autonomous control of proton exchange membrane fuel cells (PEMFCs) under optimal humidification conditions. These processes enable monitoring and control tasks across various application scenarios and operating conditions. Consequently, it becomes crucial to adjust parameter values corresponding to different states of the PEMFC during its operation. In this context, this work presents the design and development of an architecture for the control and management of a PEMFC with a maximum power output of 500 [W] based on intelligent agents operating under optimal conditions (membrane humidification). The proposed architecture integrates perception and action algorithms that leverage sensory and contextual information using heuristic algorithms. It adopts a hierarchical structure with distinct layers, each featuring varying time windows and levels of abstraction. Notably, this architecture demonstrates its effectiveness in achieving the desired energy efficiency objective, as evidenced by successful validation tests conducted with different electrical power values delivered by the fuel cell, encompassing three distinct operating states (dry, normal, and flooded). An exemplary application of this scheme is the dynamic control of the humidification of the polymeric membrane, which further highlights the capabilities of this architecture.

China’s Green Total Factor Energy Efficiency Assessment Based on Coordinated Reduction in Pollution and Carbon Emission: From the 11th to the 13th Five-Year Plan

As the world’s largest energy consumer, China is facing multiple pressures to conserve energy, mitigate pollution and reduce carbon emissions. An objective and scientific assessment of green total factor energy efficiency (GTFEE) is an important prerequisite for achieving energy conservation, emission reduction and low carbon development. In this research, a non-radial data envelopment analysis (DEA) is conducted to assess the GTFEE in China, while the issue of climate and air pollution from energy use is considered in the assessment system. We use different indicators to refer to air pollutants, which provides a reference for related research in indicator selection. The results show that different indicators have different inefficiency values, but changes in the indicators have a minimal effect on the inefficiency values of the other constant indicators. We also assess the GTFEE for the last three five-year plan periods to inform future policy development. The results show that China’s average annual GTFEE exhibits a unique trend in each five-year plan period, with an overall “v” shaped trend. The annual average GTFEE of different regions also varies. The other changes in the rankings of the provinces are mainly concentrated in the 11th to 12th Five-Year Plan period.

Energy-Efficient Parallel Computing: Challenges to Scaling

The energy consumption of Information and Communications Technology (ICT) presents a new grand technological challenge. The two main approaches to tackle the challenge include the development of energy-efficient hardware and software. The development of energy-efficient software employing application-level energy optimization techniques has become an important category owing to the paradigm shift in the composition of digital platforms from single-core processors to heterogeneous platforms integrating multicore CPUs and graphics processing units (GPUs). In this work, we present an overview of application-level bi-objective optimization methods for energy and performance that address two fundamental challenges, non-linearity and heterogeneity, inherent in modern high-performance computing (HPC) platforms. Applying the methods requires energy profiles of the application’s computational kernels executing on the different compute devices of the HPC platform. Therefore, we summarize the research innovations in the three mainstream component-level energy measurement methods and present their accuracy and performance tradeoffs. Finally, scaling the optimization methods for energy and performance is crucial to achieving energy efficiency objectives and meeting quality-of-service requirements in modern HPC platforms and cloud computing infrastructures. We introduce the building blocks needed to achieve this scaling and conclude with the challenges to scaling. Briefly, two significant challenges are described, namely fast optimization methods and accurate component-level energy runtime measurements, especially for components running on accelerators.

An Improvement Strategy for Indoor Air Quality Monitoring Systems.

Air quality has a huge impact on the comfort and healthiness of various environments. According to the World Health Organization, people who are exposed to chemical, biological and/or physical agents in buildings with low air quality and poor ventilation are more prone to be affected by psycho-physical discomfort, respiratory tract and central nervous system diseases. Moreover, in recent years, the time spent indoors has increased by around 90%. If we consider that respiratory diseases are mainly transmitted from human to human through close contact, airborne respiratory droplets and contaminated surfaces, and that there is a strict relationship between air pollution and the spread of the diseases, it becomes even more necessary to monitor and control these environmental conditions. This situation has inevitably led us to consider renovating buildings with the aim of improving both the well-being of the occupants (safety, ventilation, heating) and the energy efficiency, including monitoring the internal comfort using sensors and the IoT. These two objectives often require opposite approaches and strategies. This paper aims to investigate indoor monitoring systems to increase the quality of life of occupants, proposing an innovative approach consisting of the definition of new indices that consider both the concentration of the pollutants and the exposure time. Furthermore, the reliability of the proposed method was enforced using proper decision-making algorithms, which allows one to consider measurement uncertainty during decisions. Such an approach allows for greater control over the potentially harmful conditions and to find a good trade-off between well-being and the energy efficiency objectives.