K-Means Clustering Algorithm–Based Energy Profiling of Milling Machines: Status Based Optimization of Energy

Integrated and multi-hour optimization of office building energy consumption and expenditure

This paper addresses the estimation problem of the minimum required energy of fuel cell vehicle (FCV) driving through a road link conditioned on the current and future traffic states. The challenge is to estimate driving profile of individual vehicles and obtain the corresponding optimal energy consumption of powertrain. Combining the powertrain model and macroscopic traffic dynamics, the FCV tank-to-traffic energy dissipation model is established. Based on the optimal energy consumption within each road segment solved by dynamic programming, the optimal demand power field is obtained. According to the estimated driving profile, optimal segmental energy consumption (OSEC) is calculated by interpolation of optimal demand power field, whose summation is utilized as the estimator for global optimal accumulative energy consumption (GOAEC). The validation result indicates that the highest Pearson correlation coefficient between the sequence of OSEC and GOAEC reaches 0.86, while in the case of the poorest estimation performance, the sequences share a similar shape. The effective estimator for GOAEC provides the quantified evidence supporting decisions on route planning and velocity planning.

Energy consumption patterns in the accommodation sector—the New Zealand case

In order to reduce the energy consumption of deep-sea self-sustaining profile buoy (DSPB) and extend its running time, a stage quantitative oil draining control mode has been proposed in this paper. System parameters have been investigated including oil discharge resolution (ODR), judgment threshold of the floating speed and frequency of oil draining on the energy consumption of the system. The single-objective optimization model with the total energy consumption of DSPB’s ascent stage as the objective function has been established by combining the DSPB’s floating kinematic model. At the same time, as the static working current of the DSPB can be further optimized, a multi-objective energy consumption optimization model with the floating time and the energy consumption of the oil pump motor as objective functions has been established. The non-dominated sorted genetic algorithm-II (NSGA-II) has been employed to optimized the energy consumption model in the ascent stage of the DSPB. The results showed that the NSGA-II method has a good performance in the energy consumption optimization of the DSPB, and can reduce the dynamic energy consumption in the floating process by 28.9% within 2 h considering the increase in static energy consumption.

An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors

Energy use is an indicator of economic growth. However, high energy intensity has two main disadvantages. First, low energy efficiency increases a country’s dependence on other countries, especially when the country lacks energy sources. Second, if the country’s energy needs are met using traditional fossil fuels, this increases its CO2 emissions and reduces its air quality. Improving energy efficiency and reducing energy intensity are essential to reach the sustainability targets. This paper investigates the determinants of energy use in Turkiye for the period 1991–2019 by taking a dual approach. First, utilizing the Tapio decoupling factor, the decoupling factor analysis is not only being done for total energy use and real GDP, but also for industrial energy use and industrial income. Second, the factors determining the country’s total energy use are also examined, followed by an investigation of the indicators of energy use in the industry sector, which is highly energy intensive. For the industrial sector, two different decomposition analyses are performed and results are compared. The refined Laspeyres index method is adopted, and for each analysis, three main factors are considered. The empirical findings demonstrate that the income effect and population effect increased Turkiye’s total energy use, whereas the energy intensity effect decreased it. The first decomposition analysis for the industrial energy use reveals partly contrasting results with the previously published articles. For the industry sector, the second analysis show that productivity and employment increased Turkiye’s sectoral energy use; however, the sector’s energy intensity reduced it. Turkiye achieved some success in terms of reducing energy intensity at the sectoral and aggregate levels; however, as the findings of the present study demonstrate, further efforts are needed to lessen the country’s energy dependence and also to achieve future environmental sustainability targets. Trends relating to the determining factors in total and sectoral energy use are also compared in this paper, and some policy implications are presented.

Fruit and vegetable processing comes 6th in terms of energy consumption in the agri-food industry. At the same time, 88.4% of the industry’s final energy consumption structure is thermal energy, which depends heavily on electricity consumption. In addition, fruit and vegetable processing has a significant impact on the environment due to consumption of significant amounts of water. Reducing these three indicators simultaneously would increase the efficiency of the process while improving environmental protection. This paper proposes neural models of thermal energy, electricity and water consumption for selected major fruit- and vegetable-processing plants in Poland. These models were the basis for formulating a multi-criteria optimization task. Optimization of thermal energy, electricity and water consumption was carried out using genetic algorithms. The optimization results in the sense of Pareto can be the basis for the use of sustainable technology in selected fruit- and vegetable-processing plants.

Recent improvements in the energy efficiency of agriculture: Case studies from Ontario, Canada

Energy analysis and optimization of a food defrosting system

<p>Concomitant with the expeditious growth of the construction industry, the challenge of building energy consumption has become increasingly pronounced. A multitude of factors influence the energy consumption of building operations, thereby underscoring the paramount importance of monitoring and predicting such consumption. The advent of big data has engendered a diversification in the methodologies employed to predict building energy consumption. Against the backdrop of factors influencing building operation energy consumption, we reviewed the advancements in research pertaining to the supervision and prediction of building energy consumption, deliberated on more energy-efficient and low-carbon strategies for buildings within the dual-carbon context, and synthesized the relevant research progress across four dimensions: The contemporary state of building energy consumption supervision, the determinants of building operation energy consumption, and the prediction and optimization of building energy consumption. Building upon the investigation of supervision and determinants of building energy consumption, three predictive methodologies were examined: (ⅰ) Physical methods, (ⅱ) data-driven methods, and (ⅲ) mixed methods. An analysis of the accuracy of these three predictive methodologies revealed that the mixed methods exhibited superior precision in the actual prediction of building energy consumption. Furthermore, predicated on this foundation and the identified determinants, we also explored research on the optimization of energy consumption prediction. Through an in-depth examination of building energy consumption prediction, we distilled the methodologies pertinent to the accurate forecasting of building energy consumption, thereby offering insights and guidance for the pursuit of building energy conservation and emission reduction.</p>

Modelling and optimization of energy consumption in essential oil extraction processes

Multi-objective optimization of residential building energy consumption, daylighting, and thermal comfort based on BO-XGBoost-NSGA-II

With the increasing energy consumption in the mining industry, the effective prediction and optimization of energy consumption in mining equipment have become pressing challenges. Traditional energy consumption prediction methods suffer from data processing delays and the fixed nature of monitoring devices, making them inadequate for meeting the real-time and flexible demands of modern mining operations. The advent of mobile computing platforms has introduced new possibilities for the dynamic prediction and optimization of energy consumption in mining equipment. In recent years, energy consumption prediction techniques based on mobile computing platforms have gained significant attention, enabling realtime data acquisition and analysis for a more precise understanding of energy consumption patterns and the implementation of efficient optimization strategies. However, existing studies predominantly focus on conventional models and methodologies, lacking effective mechanisms to capture spatiotemporal dynamics and optimize energy consumption accordingly. In this study, a spatiotemporal gated graph convolutional prediction model was proposed for the dynamic prediction of energy consumption in mining equipment based on a mobile computing platform. Additionally, an energy consumption optimization strategy was explored using the prediction results. This study provides a novel approach to energy consumption optimization in mining equipment, offering both theoretical significance and practical value.

This paper presents life cycle analysis of the container-based single-family housing and combines energy analysis and optimization, life cycle assessment and life cycle costing. The proposed models include the container code (CC), designed to Canadian national building code as reference model, and an improved container (IC) incorporating passive solar technologies. Utilizing passive design strategies results in approximately 79% reduction in annual operational energy consumption for the improved case. The life cycle assessment considers the total energy use and global warming potential over 60-year lifespan. Three life cycle phases are considered: pre-use, use and operation, and end of life. As a result of envelope upgrade, the IC offers 77% reduction in life cycle energy use and global warming potential (GWP) than the CC. However, the envelope upgrade requiring greater energy intensity through industrial processing of additional building materials led to higher environmental impact, approximately 65,370 kWh of embodied energy and 20 t of CO2 eq of GWP embodied carbon emissions for the improved container at pre-use phase. In terms of life cycle cost, IC shows less than 10% total cost savings as compared to CC. This study, however, proves that integrating passive solar design techniques in building not only results in reduced energy consumption but rather translates to reduction in life cycle environmental impacts and life cycle cost building systems, which is, in this case, a modular container housing.

With the progress of the eras and the development of science and technology, the requirements of device-to-device (D2D) connectivity increased rapidly. As one important service in future systems, ultra-reliable low-latency communication (URLLC) has attracted attention in many applications, especially in the Internet of Things (IoT), smart cities, and other scenarios due to its characteristics of ultra-low latency and ultra-high reliability. However, in order to achieve the requirement of ultra-low latency, energy consumption often increases significantly. The optimization of energy consumption and the latency of the system in the communication field are often in conflict with each other. In this paper, in order to optimize the energy consumption and the latency jointly under different scenarios, and since the detailed requirements for latency and reliability are diverse in different services, we propose an adaptive UE aggregation (AUA)-based transmission scheme that explores the diversity gain of multiple simultaneous paths to reduce the overall latency of data transmission, wherein multiple paths correspond to multiple coordination nodes. Furthermore, it could provide the feasibility of link adaptation by adjusting the path number according to the real transmission environment. Then, unnecessary energy waste could be avoided. To evaluate the performance, the energy-delay product (EDP) is proposed for the latency and energy comparison. The provided simulation results align with the numerical data. Through the analysis, it can be proven that the proposed scheme can achieve a joint optimization of latency and energy consumption to meet different types of URLLC services.