An innovative energy predictive process planning tool for assembly automation systems

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.

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.

Excessive energy consumption in households represents a significant challenge to global sustainability, contributing to climate change. The United Nations (UN) estimates that the energy sector accounts for 60% of global greenhouse gas emissions, while the International Energy Agency (IEA) projects accelerated growth in global electricity demand. With this in mind, the mobile application for smart home energy management and consumption optimization was developed using the Cascada methodology. It integrates key functions, including device registration with associated energy consumption, real-time monitoring and control with alerts for surpluses, and detailed energy-efficiency analysis at monthly, quarterly, or annual intervals. For their evaluation, criteria such as innovation, functionality, technology, satisfaction, and usability were applied. The results obtained were high, with means of 4.03 and 4.04 on a Likert scale, respectively. Experts particularly highlighted the system's cross-platform compatibility (mean 4.50) and its ability to manage devices efficiently (mean 4.40), while users emphasized the intuitive interface (mean 4.00) and ease of navigation (mean 4.20). In addition, 48.3% of users rated satisfaction as "Very good", and 43.3% rated usability with the same score, reinforcing the effectiveness and acceptance of the application consolidating its practical value in real contexts and indicate a high degree of trust and commitment from users, showing the potential of the application to promote sustainability and optimize energy consumption in households, contributing to both cost reduction and environmental care.

INTRODUCTION: With the increasing global awareness of sustainable development and environmental protection, green building has become one of the important development directions in the construction industry. The application of sustainable type energy in the construction industry is of great significance in reducing building energy consumption and environmental pollution. This study aims to explore the application of sustainable types of energy and conduct research on energy consumption optimization. OBJECTIVE: The aim of this study is to analyze the current situation of the application of sustainable types of energy in the construction industry, to explore its impact on the energy consumption of buildings, and to propose corresponding optimization strategies in order to achieve the goal of sustainable development of green buildings in China. METHODS: This study adopts a combination of literature review and case study; firstly, a literature review on the application of sustainable types of energy, sorting out its technical characteristics and application effects; then, several typical cases are selected to analyze its energy application and energy consumption in buildings; finally, relevant strategies and suggestions for optimizing the energy consumption are put forward by combining the results of the literature review and the case study. RESULTS: Through the literature review and case analysis, it is found that sustainable types of energy, such as solar energy and wind energy, have been widely used in buildings and achieved certain energy-saving effects. However, there are also some problems, such as inefficient energy utilization and high cost. To address these problems, this study proposes a series of optimization strategies, including suggestions for optimizing energy system design, improving energy utilization efficiency, and reducing energy costs. CONCLUSION: This study concludes that the application of sustainable types of energy in green buildings is an important way to optimize building energy consumption and sustainable development. Through measures such as optimizing energy system design and improving energy utilization efficiency, building energy consumption can be further reduced, environmental pollution can be reduced, and the development of the construction industry can be promoted. However, further research and practice are still needed to continuously improve relevant technologies and policies to promote the application and development of sustainable types of energy in buildings.

PurposeAssembly lines are one of the places where energy consumption is intensive in manufacturing enterprises. The use of robots in assembly lines not only increases productivity but also increases energy consumption and carbon emissions. The purpose of this paper is to minimize the cycle time and total energy consumption simultaneously in parallel robotic assembly lines (PRAL).Design/methodology/approachDue to the NP-hardness of the problem, A Pareto hybrid discrete firefly algorithm based on probability attraction (PHDFA-PA) is developed. The algorithm parameters are optimized using the Taguchi method. To evaluate the results of the algorithm, a multi-objective programming model and a restarted simulated annealing (RSA) algorithm are used.FindingsAccording to the comparative study, the PHDFA-PA has a competitive performance with the RSA. Thus, it is possible to achieve a sustainable PRAL through the proposed method by addressing the cycle time and total energy consumption simultaneously.Originality/valueTo the best knowledge of the authors, this is the first study addressing energy consumption in PRAL. The proposed method for PRAL is efficient in solving the multi-objective balancing problem.

Reduction of energy consumption is important for reaching a sustainable future. This paper presents a novel method for optimizing the energy consumption of robotic manufacturing systems. The method embeds detailed evaluations of robots' energy consumptions into a scheduling model of the overall system. The energy consumption for each operation is modeled and parameterized as function of the operation execution time, and the energy-optimal schedule is derived by solving a mixed-integer nonlinear programming problem. The objective function for the optimization problem is then the total energy consumption for the overall system. A case study of a sample robotic manufacturing system and an experiment on an industrial robot are presented. They show that there exists a real possibility for a significant reduction of the energy consumption in comparison to state-of-the-art scheduling approaches. Note to Practitioners-The motivation for this work is the great interest of companies on energy consumption optimization, looking for cost reduction and sustainability in manufacturing. Existing optimization methods focus on different levels of details. A high-level model would be able to optimize the overall system. Unfortunately, due to the high computational cost, it can hardly consider the deep level of mechanical and electrical parameters, which determine the real energy consumption. This paper presents a novel method to embed detailed energy consumption models into a scheduling optimization problem. An effective parameterization of the time variables reduces the model complexity, allowing the optimizer to reschedule the complete sequence of operations for minimizing the total energy consumption, while keeping a fixed cycle time. The method has been integrated into a commercial tool for robot programming. The optimization is applicable both on new and existing robotic systems, since the required modifications are limited to the operations rescheduling, and no investments on new hardware are expected.

Manufacturing industries these days are more concerned about the energy consumption due to the increasing energy cost. It is observed that industries can achieve significant savings in the energy cost even when a small percentage of energy consumption is reduced. In a manufacturing industry, assembly lines are equipped with robots to perform the assembly tasks and there is a need for efficiently balancing the assembly line by reducing the energy consumption during the production phase. The authors could not find any research on optimizing energy consumption in U-shaped robotic assembly line systems to date. The objective of this paper is to propose an optimization models with an objective of minimizing the energy consumption in a U-shaped robotic assembly line when the number of workstations are fixed. Since the assembly line balancing problems falls under the category of NP-hard, an efficient bio inspired algorithm is implemented to solve the problem. The model proposed is very well applicable for automobile assembly line where robots are employed. The energy consumption is evaluated on the benchmark data and energy consumption obtained by the U-shaped robotic assembly line is compared with the energy consumption obtained with straight robotic assembly line. The cycle time of the layouts are also compared. The results obtained using this proposed model can be used for the decision making process in the industry. The paper also proposes an optimization model for minimizing the cycle time. The energy consumption of the assembly line when the cycle time is minimized is evaluated and presented.

Indoor comfort parameters and outdoor environmental parameters are adjusted by controlling the compressor speed to adjust the refrigerant circulation and flow rate entering the indoor heat exchanger, timely meeting the indoor cooling and heating load requirements, and achieving summer cooling and winter heating. The problem of energy waste caused by the operation of CAC (Central Air Conditioning) systems also comes with it. In this regard, this article integrates DL (Deep Learning) to design energy consumption prediction and optimization algorithms for CAC systems. The results indicate that the energy consumption varies with the equipment speed at different optimal settings, and the overall energy consumption optimization effect is significant. Without optimization control, the average hourly power consumption was 264.7kw, and after optimization, the average hourly power consumption was 252.4kw. However, in the testing of system efficiency, it can be found that the data comparison before and after improving system efficiency optimization shows that the system efficiency improved by about 5% after using the algorithm in this paper. This article aims to help managers control energy consumption, save costs, and ultimately achieve the goal of reducing operational management costs through the study of optimizing energy consumption control in CAC systems using DL. Indoor comfort parameters and outdoor environmental conditions are regulated by adjusting the compressor speed to control the refrigerant circulation and flow rate entering the indoor heat exchanger. This ensures the timely fulfillment of indoor cooling and heating load requirements, enabling effective summer cooling and winter heating. Additionally, it addresses the issue of energy wastage resulting from the operation of CAC systems. In response to this challenge, this paper integrates DL techniques to develop energy consumption prediction and optimization algorithms tailored for CAC systems. The findings reveal that energy consumption fluctuates with equipment speed under various optimal settings, demonstrating a substantial overall improvement in energy consumption optimization. In the absence of optimization control, the average hourly power consumption stood at 264.7 kW, which decreased to 252.4 kW after implementing optimization measures. However, during the system efficiency testing, it was observed that the comparison of data before and after implementing system efficiency optimization revealed an approximate 5% enhancement in system efficiency through the utilization of the algorithm presented in this paper. This article's objective is to assist managers in effectively managing energy consumption, reducing costs, and ultimately achieving the goal of minimizing operational management expenses by investigating energy consumption optimization in CAC systems through the application of DL.

Worldwide energy supply is mostly reliant on fossil fuels. Carbon dioxide emissions have caused many negative environmental issues like climate change, air pollution, and energy security. An important alternative to this hazard is substituting the fossil fuel-based carbon energy sources with renewable energy sources. Passive strategies, which are devised to provide thermal comfort in buildings are examples of how to use renewable energies. For this study, a dairy product warehouse in the city of Yazd in Iran was thoroughly investigated. The main goal of this study is to introduce different scenarios, then identifying them based upon optimization of energy consumption. Another main purpose of the present study is to maximize the use of passive energy to meet the cooling needs of a dairy products warehouse in the studied area. Underground temperature is lower than the surface in summer, also it is higher in winter. Therefore, this property of soil is investigated by using nine different scenarios at different heights for constructing underground warehouse for storing dairy products. Clearly, different renewable tools like wind turbine, wind catcher, solar chiller, and different roof designs by Savanah grass, roof pond are also investigated. At first, the cooling load of the warehouse is calculated separately for each season. Then, according to the energy load values obtained, the nominated scenarios are investigated. The results of the comparisons show that the construction of a warehouse at a depth of 3 m from the ground with a green roof covered with Savannah grass helps achieve the best degree of reduction in the cooling power.

Building information modeling can help in predicting the energy efficiency in future based on dynamic patterns obtained by visualization of data. The aim of this study was to investigate the effective parameters of energy consumption using BIM technology which can evaluate the buildings energy performance. First, three forms of general states in the building were modeled to evaluate the proposed designs in Autodesk Revit Software. Then, the main building form for energy modeling and analysis was selected. Autodesk Revit 2020 software was also used to obtain the results of climate data analysis and building energy consumption index. Finally, the most optimal mode was selected by examining different energy consumption modes. The results showed that the use of building information modeling technology in adjusting the parameters affecting energy consumption can save energy cost up to 58.23% in block D. Energy cost savings for block C and the western lobby were obtained as 51.03% and 43.05%, respectively. Based on energy use intensity, energy cost savings for blocks C, D, and the western lobby were estimated as 16.67%, 16.30%, and 11%, respectively. The results of parametric studies on alternative schemes of energy use intensity optimization showed that 16.30% savings could be achieved by the base building model in a 30-year time horizon. Therefore, it was concluded that optimization of energy consumption would reduce the environmental pollutants emission and contribute to preservation and sustainability of the environment.

Energy consumption and energy modeling are important issues in designing and implementing of Wireless Sensor Networks (WSNs), which help the designers to optimize the energy consumption in WSN nodes. Good knowledge of the sources of energy consumption in WSNs is the first step to reduce energy consumption. Therefore, an accurate energy model is required for the evaluation of communication protocols. In this paper, an energy model for WSNs is provided considering the physical layer and MAC layer parameters by determining the energy consumed per payload bit transferred without error over AWGN channel. This model has been tested with real data and and NS-2 simulator. Results show good agreement between proposed model, experimental measurements and NS-2 simulator with mean absolute percentage error less than 5.18%. Furthermore, the proposed model is exploited to optimize transmitted power to achieve minimum energy consumption. Finally, a closed-form expression for optimum transmitted power is derived for M-QAM modulation scheme.

Energy consumption is the result of interactions between hardware, software, users, and the application environment. Optimisation of energy consumption has become crucial, and energy is considered a critical resource, so it is important to know and understand both how energy is measured and consumed on mobile devices. An accurate knowledge will allow us to develop efficient solutions to reduce energy consumption in order to improve the user experience. In this paper, we propose an experimental methodology to build a model of the energy consumption of mobile applications. Based on precise measurements, we elaborate predictive models of energy consumption for both unconnected and connected applications.

Technico-economic study of an industrial reverse osmosis desalination unit

According to the energy crisis in the world, optimization of energy consumption is indispensable. Technological advances in constructions and application of renewable energy resources demand saving energy. Atrium, as one of the construction elements, catches sufficient daylight and acts as a passive solar element and results in a decline of energy consumption. This research seeks to evaluate the impact of different positions of atrium on its energy performance; this embraces modelling three groups of spatial configuration of atriums in a given four-story building. The total volume and area of atriums in all three groups are equal. DesignBuilder, the energy simulation software, is the platform for simulation and comparison. The results show that the number of atriums does not have a noticeable impact on energy consumption; however, the spatial configuration of atriums, specially the distance of atriums to external walls have a little impact on the annual energy consumption. Based on the purpose of a specific design, one can decide on whether to take into account the discrepancy.

Integrated IDA–ANN–DEA for assessment and optimization of energy consumption in industrial sectors