Energy Consumption Management Research Articles

Analysis of energy requirements and nutritional needs of rock climbing athletes

Measuring the nutritional needs of rock climbing athletes can help athletes, coaches, and managers determine the portion of food and portion of the athlete's training. The purpose of this study is to determine the energy requirements and nutritional needs of FPTI rock climbing athletes in Buleleng Regency as recommendations for feeding. This research is a cross-sectional study that seeks to reveal quantitative variables and qualitative comments involving 12 rock climbing athletes. Data collection procedures by measurement of the nutritional needs of rock climbing athletes are carried out by providing an explanation of nutritional needs and measurement procedures, measuring BMI, calculating BMR and SDA, interviews for non-sports and sports physical activities, calculating total energy needs, determining food portions, and discussing the needs and eating of athletes per day. The calculation results show that the athlete's BMI is in the ideal category. The average energy requirement of athletes per day is 4568.45 calories. The average carbohydrate requirement for rock climbing athletes is 2741.07 calories or 685.27 grams. Energy needs that can be met by fat are 1142.11 calories or 126.9 grams. The energy requirement that can be supplied by protein is 685.27 calories or 171.32 grams. Rock climbing athletes require a diet with a rather low intake of protein and carbohydrates to keep body weight low. Adolescent rock climbing athletes must be careful in managing energy consumption which has an impact on growth and achievement.

Joint Optimization Algorithm for Small Base Station States Control and User Association in Wireless Caching Networks

The energy consumption management of small base stations (SBSs) in wireless caching networks with dense deployment of SBSs is an urgent problem to be solved. This paper jointly optimizes the SBS state control and user association problems in caching networks to reduce network energy consumption while taking into account the average service latency of the network to ensure user experience. First, a new definition of three-state SBSs in caching networks is proposed based on their ability to keep content cache updated. Then, a relaxed threshold setting method is designed and the SBS traffic prediction is used to obtain the initial state information of SBSs in the next period. In order to eliminate the impact on the accessing users when the switched-off base station (BS) wakes up, a SBS state asynchronous switching mechanism is proposed to ensure that the users who switch to the waking SBS can carry out communication services normally, and a user association strategy is constructed with the SBS load as the optimization target. Finally, a joint optimization model of user association and SBS state control (SSC-UA) is constructed to admit and correct the initial state of SBSs to maximize the system gain and obtain the final state strategy for each SBS in the next period. The simulation results show that the proposed SSC-UA algorithm can effectively improve the energy efficiency and reduce the network service delay at the same time.

An integrated home energy management system by the load aggregator in a microgrid using the internet of things infrastructure

<span lang="EN-US">Smart technologies enable the significant participation of consumers in demand-side management programs. In this paper, the management of electrical energy consumption for a set of residential houses in a microgrid by a load aggregator for a 24-h planning horizon is studied. In this study, consumption management programs are implemented on controllable equipment by sending binary codes by the load aggregator via the internet of things (IoT) infrastructure to residential sockets. To increase the level of customer convenience and provide more flexibility for consumers to participate in demand response programs, a parameter called the value of lost load (VOLL) has been introduced. According to the results, in addition to no need to use the energy management system for each residential house, only by moving shiftable loads to off-peak hours, 18.34% of energy consumption costs are saved daily. Also, from the load aggregator’s viewpoint for every 10% change in status from normal to the scheduled priority, there is a reduction of about 3.4% in the consumer’s peak-load cost. If solar arrays and storage resources are used, more than 18% of the total consumption cost can be saved.</span>

An Overview of the Architecture of Home Energy Management System as Microgrids, Automation Systems, Communication Protocols, Security, and Cyber Challenges

Today, the role of smart machines in our lives is valuable. With the advancement of digital technologies, such as the internet of things (IoT), many embedded systems have been developed for various applications. In homes, all daily activities and even security depend on machines. Therefore, implementing IoT-based smart homes has become a prominent research field. Also, since we are in the era of endless growth of the IoT and its applications, the topic of home automation systems is becoming more popular due to its countless advantages. In general, most home automation systems focus on one function: the ability to control home appliances remotely. In the world of information technology, the creation of rules and standards should always be done in the early stages of development because, after the work is done, incompatibilities between devices are created, which is a severe challenge and limits the use of technology. Therefore, the research on IoT technology has led to the formation of various protocols; this article gives an overview of seven essential protocols. Also, in this review article, energy consumption management, and privacy and security issues are discussed, and smart homes are introduced as critical requirements for the evolution of smart cities.

A novel energy management model among interdependent sections in the smart grids

AbstractTechnically, residential energy management systems are fundamental sectors in the smart grids for implementing demand response programs in the layer of households for managing energy consumption and reducing energy bills. The paper proposes a novel energy management scheme that takes production and usage into account based on a heuristic searching operation. In addition to modelling the grid, renewable energy sources, batteries, and electric vehicles, various kinds of electrical and thermal devices have been examined, including air conditioners, water heaters, vacuum cleaners etc. A method is developed for solving the objective constraint issue in a smart home in order to reduce energy consumption and determine feasible operation states among the various loads. Moreover, this paper proposes a grey wolf optimization method for solving the issue over a longer simulation period. Various cases were examined to evaluate the effectiveness of this suggested robust optimization algorithm. The outcomes show that the suggested model could not only reduce energy costs significantly but has also shown good performance for energy management purposes.

Optimization of the home energy system in presence of price fluctuation and intermittent renewable energy sources in grid-connected and islanded modes

The home energy management (HEM) system is a critical tool for managing energy consumption and minimizing the related costs in a building. A smart renewable-based HEM system that participates in the demand response program is modeled in this work and its operations in both grid-connected and islanded modes are studied. This HEM receives its energy from wind turbines in islanded mode and upstream grid in grid-connected mode. This system also includes a diesel generator in the case of wind generation insufficiency. In the connected mode, electricity price is uncertain and in islanded mode, wind generation is uncertain, which are taken into account in modeling. The goal of this paper is the minimization of the electricity bill cost for the households. The studied system also contains a controllable appliance to simulate participation in the demand response program. Moreover, the electric vehicles (EVs) in this system operate in vehicle-to-home (V2H) mode which means the battery in the EV can supply some of the needed energy if needed. Also, the kernel search optimization algorithm is utilized to improve system operation in presence of some uncertainties. The impact of applying the optimization is assessed to validate this approach. The optimization is also done in the grid-connected because the price fluctuates throughout the day and proper management of power purchase from the grid can greatly reduce the cost of energy. The system behavior is studied separately in four seasons with both operation modes, and with and without the optimization. The results indicate that using the optimization has decreased the energy costs in connected mode by 13.18 % and islanded mode by 9.55 %, which proves the effectiveness of the proposed modeling and optimization algorithm.

A heuristic deep feature system for energy management in wireless sensor network

The Wireless-Sensor-Network (WSN) has been employed in all digital applications for several purposes like sensing, storing, and sharing information. However, managing energy consumption is more critical because of the movable environment. Several existing models have addressed these energy management issues. Still, those models lack in optimizing the energy usage of the WSN during the collision environment. This has motivated to find the best solution for energy optimization with an intelligent model. So, the present research article aims to develop the novel Buffalo-based Deep Belief Energy Management Framework (BDBEMF) for the WSN application. Initially, the required number of sensor nodes was created then the BDBEMF was designed to monitor the high consumption nodes. In addition, the Low-energy adaptive-clustering-hierarchy protocol has been considered for the communication process. Consequently, the Cluster Head has been selected based on less energy utilization and high-density hubs. The data rate of each node has been measured, and the high leaded data has been shared to work fewer nodes to balance the energy. Finally, the amount of alive and dead nodes was validated with few communication metrics. The presented model has gained maximum throughput and less energy consumption.

Low-carbon warehousing practices and challenges: insights from emerging country

ABSTRACT This study aims to identify the issues, challenges, practices, and research agenda of low-carbon warehousing that the manufacturing industry faces in the low-carbon era. The debate on managing low-carbon and sustainable warehousing has gained popularity. Managing energy consumption and carbon emission in the warehouse has become a critical success factor towards low-carbon practices. Semi-structured, in-person interviews and phone conversations were used to gather the information for this study. The convenience sampling technique was used. The results showed that the internal challenges, such as financial constraint, perceived knowledge in low-carbon warehouses, and the top management support were the frequently highlighted domains. The findings revealed that organisational support, international standards certification, local authorities, and customer pressure were key success factors in implementing low-carbon warehouse practices. The companies have taken proactive measures to address the best practice of low-carbon warehouses.

Energy and load balancing routing protocol for IoT

SummaryDue to the limitation of node energy resources, the management of energy consumption is one of the most important problems of the internet of things (IoT). Therefore, many studies have tried to optimize and manage energy consumption by focusing on different techniques. Although each of these studies has improved and optimized energy consumption, there are many important problems, including maintaining traffic balance and energy consumption of network nodes. Therefore, a new method is necessary to maintain the load and energy balancing of network nodes. Therefore, this paper introduces energy and load balancing routing protocol for IoT (ELBRP) based on the development of the RPL routing protocol and the efficiency of data distribution technique. The ELBRP performance has three steps. In the first step, along with the process of sending DODAG information object (DIO) messages, the status of network nodes is evaluated. In the second step, the DODAG communication graph is formed according to the ELBRP. In the third step, data transmission is done according to the distribution technique with the goal of balancing traffic and energy. The simulation results using cooja simulator showed the superiority of ELBRP in improving energy consumption and successful delivery ratio, reducing delay and increasing the network lifetime compared to the similar methods.

A low-power WNoC transceiver with a novel energy consumption management scheme for dependable IoT systems

Wireless network-on-chip architectures (WNoCs), by combining wired and wireless modules and links, provide fast and efficient communication infrastructures for complex on-chip systems such as various IoT and intelligent systems. The most challenging step of designing high-performance and energy-efficient WNoCs is the structure of transceivers. In fact, transceivers and wired routers are the most power-hungry elements in WNoCs; therefore, any cut-down of power consumption in transceivers and routers significantly reduce the power consumption of WNoCs. In this paper, a low-power transceiver for WNoC is designed and then, a high-performance and configurable energy consumption management scheme for the transceiver is proposed. In the designed transceiver, various strategies are employed at both circuit-level and also at architectural-level for reducing static and dynamic power consumption. The proposed energy management scheme controls the activity and tunes the voltage level of the power supply of each module, in a distributed and dynamic manner. The evaluation results demonstrate that a WNoC utilizing the proposed scheme provides lower energy consumption, less latency, and higher reliability and higher network throughput compared to both of relevant single-chip and multi-chip WNoC architectures, at the cost of negligible impact on silicon area.

A Novel Time-of-Use Pricing Based Energy Management System for Smart Home Appliances: Cost-Effective Method

Smart grids (SG) allow users to plan and control device usage patterns optimally, thereby minimizing power costs, peak-to-average ratios (PAR), and peak load demands. The present study develops a typical framework of a home energy management system (HEMS) for SG scenarios using newly limited and multi-limited planning approaches for domestic users. Time-of-use pricing (TOUP) is used to develop, handle, and manage the optimization problem properly. As a capable method for optimizing the proposed problem, this paper uses a robust meta-heuristic algorithm named wind-driven optimization algorithm (WDOA) and compares it to the other optimization algorithms in order to demonstrate its efficiency. In addition, it integrates a rooftop photovoltaic (PV) system with the system in order to show that all devices are cost-effective if managed properly. Eight diverse case studies are analyzed using a variety of time planning algorithms. The simulation results advocate for the quality and high performance of the proposed model by minimizing the total cost and managing energy consumption economically.

Hybrid DC–AC Microgrid Energy Management System Using an Artificial Gorilla Troops Optimizer Optimized Neural Network

In this research, we introduce an artificial gorilla troop optimizer for use in artificial neural networks that manage energy consumption in DC–AC hybrid distribution networks. It is being proposed to implement an energy management system that takes into account distributed generation, load demand, and battery-charge level. Using the profile data, an artificial neural network was trained on the charging and discharging characteristics of an energy storage system under a variety of distribution-network power situations. As an added bonus, the percentage of mistakes was maintained far below 10%. An artificial neural network is used in the proposed energy management system, and it has been taught to operate in the best possible manner by using an optimizer inspired by gorillas called artificial gorilla troops. The artificial gorilla troops optimizer optimize the weights and bias of the neural network based on the power of the distributed generator, the power of the grid, and the reference direct axis current to obtain most suitable energy management system. In order to simulate and evaluate the proposed energy management system, small-scale hybrid DC/AC microgrids have been created and tested. When compared to other systems in the literature, the artificial gorilla troops optimizer enhanced neural network energy management system has been shown to deliver 99.55% efficiency, making it the clear winner.

A novel mathematical multi-criteria decision-making model for optimizing life cycle energy and cost in construction projects planning

ABSTRACT Today, energy management systems are usually assessed as key to sustainable development goals. One of the best criteria for energy management systems in construction projects is Life Cycle Energy Analysis (LCEA). This research is a quantitative-qualitative study based on a Multi-Criteria Decision-Making (MCDM) model. This research involves four stages: library study, statistical survey, descriptive study, and case study. The review of previous studies showed that the LCEA and MCDM alone could not provide a comprehensive energy consumption management assessment model, due to inherent uncertainty and the existence of various analysis parameters; however, when are used in an integrated framework; the LCEA & MCDM hybrid approach serves as the best instruments to assess energy management systems. This research has proposed a newly developed hybrid multi-objective mathematical decision-making model to select appropriate and optimum options for all building shell components to optimize and manage the Cost and Energy of the Building Life Cycle. The life cycle energy analysis of an educational building in a case study showed that optimization using the proposed model saves about 16% of energy during the lifetime of the building. In addition, the investment payback period for this optimization is less than seven months in the first year of operation.

Long-term equilibrium relationship analysis and energy-saving measures of metro energy consumption and its influencing factors based on cointegration theory and an ARDL model

The energy consumption of metros has widely been concerned with respect to their economy and environmental protection. To analyze the complex dynamic relationship between metro energy consumption and its influencing factors and provide a reference for metro energy conservation control, this paper uses the monthly energy consumption, passenger flow and operating distance statistical data for Wuhan Metro Line 2 from 2018 to 2019. First, metro energy consumption and its influencing factors are qualitatively analyzed and identified. Then, based on cointegration theory and an autoregressive distributed lag (ARDL) model, a correlation hypothesis between metro energy consumption and its influencing factors is constructed, and a method for analyzing the influencing factors of metro energy consumption is proposed. The total energy consumption of a metro (TEC), train traction energy consumption (TTEC), environmental control energy consumption (ECEC), station lighting energy consumption (SLEC), station equipment energy consumption (SEEC) and the operating distance (OD) and passenger flow (PF) variables are analyzed. Using cointegration and an impulse response function, the dynamic relationships between the various energy consumption factors and operating distance and passenger flow are evaluated. The results show that there are substantial differences in the effects of OD and PF and their degree of influence on metro energy consumption. (1) OD affects mainly TTEC and TEC. The degree of influence of OD on TTEC reaches 97.8%, and the degree of influence of OD on TEC reaches 65.9%. (2) PF affects mainly ECEC and SEEC, and the degrees of influence of PF on ECEC and SEEC are 32.2% and 41.3%, respectively. (3) Considering that OD is the key factor affecting TTEC and TEC, train marshaling schemes, train running intervals and train stopping scheme optimization countermeasures are proposed, which can provide decision support for metro energy consumption management and control.

Energy efficient with prolonging lifetime in homogeneous wireless sensor networks

In this artical, we suggest a new clustering model called the Fuzzy Low Energy Adaptive Clustering Hierarchy Protocol (FLEACH). It has the ability to go around obstacles and fix the issue of uneven energy consumption in homogenous wireless sensor networks (WSNs). In order to determine the best routing path for the homogeneous WSNs, we also suggest the Ant Colony Routing Method (ACORM), which is an energy-efficient routing technique. ACORM aims to investigate issues relating to balancing energy consumption and maximizing network life. To illustrate FLEACH-ACORM's effectiveness in managing energy consumption and optimizing homogeneous network life, we compare our system with two approaches: Power-Efficient Sensor Information Systems (PEGASIS) and LEACH. Results of the simulation indicate that the network lifetime achieved by FLEACH-ACORM could be increased by almost 28.5 % and 19.8 % more than that obtained by LEACHPEGASIS, LEACH clustering protocols, respectively.

Trends characterizing technological innovations that increase environmental pressure: A typology to support action for sustainable consumption

Technological innovation is widely recognized as an endogenous element of capitalism driving economic growth and consumption. Although technological innovations have benefited human health, quality of life, and comfort, especially in high-income countries, uncontrolled industrialization of technological innovations and mass consumption exert strong environmental pressure on natural resources and contribute to the degradation of the environment. Apart from their endogenous role in economy and consumption, these innovations are characterized by specific trends that affect the sustainability of manufactured goods and consumption patterns, such as rate of market penetration, ownership of manufactured goods, product lifespan, reparability, and recyclability. This paper aims to contribute to a theorization of the relationship between technological innovation, consumption, and sustainability. To this end, we propose a typology of trends characterizing technological innovation to constitute a coherent framework. These trends are then documented to evaluate their magnitude, drivers, and related issues, following the broad principles of integrative literature reviews through a purposeful review sampling. The following trend framework emerged with regards to technological innovations: (a) accumulation; (b) diversification; (c) substitution; (d) complexification. The work contributes to identifying and formalizing: (1) the terminology regarding each trend, (2) related concepts that should be considered to theorize the relationship between technological innovation and (un)sustainable consumption patterns, (3) the main drivers that sustain these trends, (4) interactions between these trends, and (5) societal consequences on material and energy consumption and waste management.

Evaluating Sustainability Aspects of Housing in UNESCO world heritage city of Yazd Case Study: Lariha House

Nowadays, reducing energy consumption and coexistence with natural and climatic conditions in the region has become one of the most prominent architectural goals. As a result of the growing population and crisis created by rising energy demand, managing energy consumption patterns has become an essential component of sustainability. By recognizing Iran’s culture, customs, traditions, and climates, traditional Iranian architectures designed and built the constructions with the least use of non-renewable energy. Besides, simple and logical methods had provided the most use of renewable energy. As a result, this study explores the optimization of energy consumption in the housing sector through current native pattern designing in hot and dry regions, with a specific emphasis on the relevance of energy savings. The case study of this research is Lariha house. Its Building design style will examine such as wall thickness, placement, the height of internal structures, etc, and the elements used plants, porches, wind flow, etc., which directly influences optimizing energy usage.

RT-SEAT: A hybrid approach based real-time scheduler for energy and temperature efficient heterogeneous multicore platforms

The demand for heterogeneous multicore platforms is growing at a rapid pace in modern gadgets. Such platforms help cater to various types of applications and thus provide high resource utilization. As each task has a different execution time on different types of cores, it is very challenging to schedule tasks on such platforms. With the advancement in technology, it has become imperative to manage energy consumption and temperatures of cores in multicore platforms. Hence, in this work, we propose RT-SEAT, a hybrid real-time scheduler for energy and temperature-efficient heterogeneous multicore systems. Through extensive experimental analysis, we have found that RT-SEAT is able to schedule more tasks (up to 46.75%), save more energy (up to 6.89%), and reduce the average temperature of the cores by 20.45%, when the system workload varies from 50% to 100%, with respect to the state-of-the-art.

TADR-EAODV: A trust-aware dynamic routing algorithm based on extended AODV protocol for secure communications in wireless sensor networks

Compliance with security requirements in Wireless Sensor Networks (WSNs) is known as an important phenomenon in conserving energy consumption due to its dynamic topology. Energy consumption management in WSN can be achieved through secure routing protocols. Authentication, firewall and intrusion detection, and trust management are common ways to provide security on the WSNs. Meanwhile, trust management provides better performance due to ensuring distributed security through collaboration. In this paper, a Trust-Aware Dynamic Routing algorithm based on Extended AODV protocol for secure communications in the WSN (TADR-EAODV) is proposed. TADR-EAODV considers factors such as direct trust, recommended trust, connectivity strength, energy rate and worthiness score to measure the distributed safety level of nodes in routing. We use the AODV protocol for routing work, which is extended through a multi-route routing approach. In addition, a centralized ensemble clustering for node grouping is included in TADR-EAODV, which enables clustering-based routing to enhance WSN performance. Extensive experiments have been performed in the presence of denial-of-service attacks to verify our claims. The experimental results prove that high-performance TADR-EAODV has been able to identify attackers by detecting their anomalous behavior. Besides, TADR-EAODV has improved the average packet transfer rate by 6% to 17% compared to existing state-of-the-art algorithms such as LEACH-TM, TAAO-SDTIM and TAOSC-MHR.

Energy Forecasting in a Public Building: A Benchmarking Analysis on Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) Networks

A primary energy consumption and CO2 emission source stems from buildings and infrastructures due to rapid urbanisation and social development. An accurate method to forecast energy consumption in a building is thus critically needed to enable successful management of adaptive energy consumption and ease the level of CO2 emission. However, energy forecasting for buildings, especially residential buildings, has several challenges, such as significant variations in energy usage patterns due to unpredicted demands of the residences and some intricate factors, which can randomly affect the patterns. Traditional forecasting approaches require a tremendous number of inputs needed for building physic models and variations often exist between as-built and as-designed buildings in reality. Most recent studies have adopted only ambient weather conditions, building components, and the occupant’s behaviours. As a result, in order to take into account the complexity of factors that can affect the building energy model development and its computation, we develop advanced machine learning models driven by the inherent electricity consumption pattern associated with the day and time. In this study, we demonstrate benchmarking results derived from three different machine learning algorithms, namely SVR, XGBoost, and LSTM, trained by using 1-year datasets with sub-hourly (30 min) temporal granularity to determine the outperformed predictor. Ultimately, the machine learning model robustness and performance on a basis of the coefficient of variation (CV) obtained by the SVR is benchmarked across XGBoost and LSTM trained by the same datasets containing attributes related to the building type, data size, and temporal granularity. The insight stemming from this study indicates that the suitable choice of the machine learning models for building energy forecasts largely depends on the natural characteristics of building energy data. Hyperparameter tuning or mathematical modification within an algorithm may not be sufficient to attain the most accurate machine learning model for building energy forecast.