Energy Consumption Management Research Articles

Energy Consumption Analysis and Management Strategies for the Dairy Farm in India Towards Sustainable Development

ABSTRACT Environmental pollution and the rapid decline in the availability of fossil fuels are the major concerns of the electric power industry in recent years. Effective utilization of electrical energy is the need of the hour in building the nation towards sustainable development. This study presents the energy consumption analysis and management performed in a diary firm located in Tamil Nadu, India. Initially, the energy usage of the firm is estimated based on its electricity bills, meter readings, and survey followed by detailed energy consumption analysis and evaluation. The study employs guidelines provided by the Indian Bureau of Energy Efficiency for energy management. The detailed analysis performed finds scopes for improvement in efficient energy usage. Numerical analysis shows that implementation of proposed action plans that include installation of automatic power factor correction, replacement of overheating equipment, and replacement of lighting systems with energy-efficient lighting would save annual energy of 123.96 kWh which in turn results in the reduction of annual CO2 emission by 117.76 tonnes. Furthermore, this paper also proposes a novel optimal process scheduling which optimally schedules different processes in the firm without affecting productivity. This makes an additional savings of INR 4,23,360 per annum in the energy cost by minimizing the maximum demand of the firm with zero investment. A comparison of the proposed approach with existing methodologies available in the literature shows the effectiveness of the proposed technique.

Bio-inspired bidirectional deep machine learning for real-time energy consumption forecasting and management

Accurately predicting electrical power demand is crucial to making related forecasts and to effective sustainable energy management. Most relevant state-of-the-art studies deploy models that do not use optimizing parameters and do not incorporate strategies for using forecast results. This study was designed to develop a novel electricity consumption forecasting model, the Symbiotic Bidirectional Gated Recurrent Unit, which integrates Gated Recurrent Unit, Bidirectional Technique, and Symbiotic Organisms Search algorithms. The results of tests on a series of evaluation criteria showed the proposed model performed significantly better than six comparison models when parameter optimization was used. For all three sector datasets, the proposed model generated the most-accurate predictions of all models. In practical terms, when supply is expected to exceed demand, the prediction results may be used to adjust power plant output to reduce wastage. Conversely, when demand is expected to exceed supply, Time-of-Use tariffs may be implemented based on time-of-day and seasonal fluctuations in demand to facilitate reductions in peak usage and level out overall demand.

Client selection for federated learning using combinatorial multi-armed bandit under long-term energy constraint

In a federated learning system, it is often the case that the more clients it involves, the less increment of the outcome it achieves. It is thus essential to design a client selection strategy to choose an appropriate subset of the clients to participate in federated learning. However, client selection is not easy due to the heterogeneity of clients and the long-term energy budget of each client. Moreover, long-term energy budgets intertwined with the short-term client selection often make the problem NP-hard. In this paper, we propose an online strategy Energy-Aware Client Selection for Federated Learning (EACS-FL) to address this problem. The problem is formulated with a joint energy and delay optimization objective, and the Combinatorial Multi-Armed Bandit (CMAB) is introduced to solve the problem in an online manner. We take advantage of Lyapunov optimization to manage energy consumption of clients, which enables us to deal with independent energy budgets through minimizing virtual energy deficit queues. Theoretical analysis shows that EACS-FL achieves sublinear regret and keeps all queues stable. Experiment results exhibit that the proposed approach outperforms the existing works and achieves close-to-optimal delay and energy consumption performance.

METHODS AND TOOLS FOR IN-DEPTH ANALYSIS OF ENERGY CHARACTERISTICS AND MANAGEMENT OF ENERGY CONSUMPTION IN EDUCATIONAL INSTITUTIONS

A factor of the state's national security is reducing energy dependence and taking measures to increase energy efficiency, especially in the budgetary sphere, because about 90% of such buildings do not meet modern energy efficiency requirements. Creating a reliable energy consumption management system, choosing measures to reduce consumption and diversifying energy sources is a difficult task that requires consideration of building energy consumption processes as a complex problem with the use of modern tools of the energy management system, modeling, and the use of modern approaches to automation and measurements. The object of research is energy consumption processes in educational institutions. The subject of the research is energy characteristics and methods of energy consumption management in educational institutions. Research methods. analytical methods, system analysis, synthesis, modeling, systematization, method of comparative and structural analysis. One of the tasks of this study is to review the capabilities of software products that can be used by energy managers of educational institutions to monitor, analyze and forecast energy consumption, as well as to create reports. Also in the work, an analysis of the state of affairs regarding energy consumption management was carried out on the example of KPI named after Igor Sikorskyi; proposals for the development of the automated energy monitoring system were provided, in particular, the architecture of the web application was developed taking into account the support of existing databases to improve the energy monitoring process.

Quantile Effect of Economic Factors and Energy Consumption on Environmental Degradation in Vietnam

This study aims to highlight the asymmetric impact of economic growth (GDP), trade openness (TO), energy consumption (EC), foreign direct investment (FDI), and financial development (FD) on carbon dioxide (CO2) emissions in Vietnam over the period 1990–2022, using quantile-on-quantile regression and Granger causality in quantiles techniques. Our mainstream findings indicate that the selected macroeconomic indicators have a strong positive effect on CO2 emissions in this country, and this effect is more pronounced in the lowest and highest quantiles of the respective variables. In addition, the results of Granger causality suggest a bidirectional causal relationship between the examined indicators. These findings suggest that fostering economic growth, promoting trade openness, managing energy consumption, encouraging foreign direct investment, and enhancing financial development can contribute to a sustainable environment in Vietnam.

Modern architectural landscape planning and design based on environmental sensors and 3D point cloud processing

With the continuous development of modern technology, the application of sensor technology in architectural landscape planning and design is becoming increasingly widespread. The combination of environmental sensors and 3D point cloud processing technology provides new possibilities for modern architectural landscape planning and design. Environmental sensors can provide accurate environmental data support for the design process by monitoring the climate conditions of the building site in real-time, better understanding the natural environmental characteristics of the building site, and making targeted planning and design. Environmental sensors can accurately measure the intensity and direction of light, helping designers arrange the layout and window design of buildings to maximize the use of natural lighting. By monitoring and analyzing temperature data, the design team can effectively optimize the energy consumption management of buildings and improve their energy efficiency performance. The combination of 3D point cloud processing technology and environmental sensor data accurately restores the real environment of the building site to a digital model, allowing designers to conduct comprehensive spatial analysis and design. This digital and intelligent design process improves the precision and efficiency of design, allowing the design team to better understand the environmental characteristics of the building site and achieve intelligent and personalized landscape design, Injecting new vitality and creativity into future architectural planning and design.

Resilient data-driven non-intrusive load monitoring for efficient energy management using machine learning techniques

The integration of smart homes into smart grids presents numerous challenges, particularly in managing energy consumption efficiently. Non-intrusive load management (NILM) has emerged as a viable solution for optimizing energy usage. However, as smart grids incorporate more distributed energy resources, the complexity of demand-side management and energy optimization escalates. Various techniques have been proposed to address these challenges, but the evolving grid necessitates intelligent optimization strategies. This article explores the potential of data-driven NILM (DNILM) by leveraging multiple machine learning algorithms and neural network architectures for appliance state monitoring and predicting future energy consumption. It underscores the significance of intelligent optimization techniques in enhancing prediction accuracy. The article compares several data-driven mechanisms, including decision trees, sequence-to-point models, denoising autoencoders, recurrent neural networks, long short-term memory, and gated recurrent unit models. Furthermore, the article categorizes different forms of NILM and discusses the impact of calibration and load division. A detailed comparative analysis is conducted using evaluation metrics such as root-mean-square error, mean absolute error, and accuracy for each method. The proposed DNILM approach is implemented using Python 3.10.5 on the REDD dataset, demonstrating its effectiveness in addressing the complexities of energy optimization in smart grid environments.

Research on design strategy of public space service of commercial complex based on internet of things

The purpose of this study is to explore the design strategy of public space service of commercial complex based on the Internet of Things. By constructing a three-tier model of environmental energy consumption monitoring and management system for commercial complex, the application of network sensor technology is analyzed, and a data access interface based on Windows Communication Foundation (WCF) service is designed to realize batch hosting of WCF services and reduce the complexity of large-scale publishing services. In addition, this paper proposes a clustering method based on probabilistic topics (PTCM), which divides the service search space into probabilistic topics and effectively narrows the service search scope. The simulation results show that the algorithm shortens the shortest distance between mobile nodes and the average network distance, and improves the perceived service quality of the Internet of Things. This study not only provides new ideas and methods for the design of public space services in commercial complexes, but also provides useful reference for the application and promotion of Internet of Things technology in the field of public services.

A Study Article of DLMS to IOT Protocol

Abstract: The DLMS (Device Language Message Specification) protocol plays a crucial role in energy metering and management. It provides a standardized communication framework for exchanging data between utility meters and data collection devices. The protocol enables interoperability, remote meter reading, and supports efficient management of energy consumption, billing, and grid operations. The DLMS protocol is essential for Internet of Things (IoT) applications as it provides a standardized method for communication between Internet of Things devices and energy meters. This facilitates the seamless integration of energy data into IoT platforms, enabling efficient monitoring, analysis, and management of energy consumption in smart grid and smart city deployments. To this end, we have developed a prototype DLMS to IoT gateway that is based on either STM32 or any other IoT module. However, we encountered a challenge in testing the prototype since we couldn't get any digital meter to conduct hands-on testing..

Design of proactive management system for residential buildings by using smart equipment

This study's object is an energy efficiency of residential sector. The work is aimed at solving the task to improve the energy efficiency of the housing sector by devising technical solutions for monitoring and managing energy consumption and microclimate parameters of buildings. The proposed proactive management system for residential buildings consists of multi-sensors measuring CO2, temperature and humidity, smart meters of heat and electricity consumption, and smart plugs. The equipment is combined into single system through an integration controller with remote user access through an interactive web interface. A feature of the technical solution is the ability to collect, process, visualize, and archive data on the consumption of energy, as well as on the key parameters of the microclimate of residential premises. The advantages of the system are its flexibility due to the possibility of integrating additional devices during operation, as well as the use of standard communication protocols, which enables the interchangeability of component elements. The implementation and testing were carried out under the conditions of a real pilot site. The use of the system in practice confirmed the efficiency and stability of the operation, making it possible to obtain data on the parameters of energy consumption and microclimate and devising recommendations for reducing energy consumption at the pilot site. It was established that the microclimate meets the requirements of the standards (air temperature is about 22 °С while relative humidity does not exceed 60 %). Decrease in energy consumption can be achieved by reducing the temperature of the heat carrier in the absence of residents, as well as by considering the influence of weather conditions. During periods of residents activity, an excess of the permissible level of CO2 was recorded, therefore, automatic ventilation systems should be provided in the apartments

Climate mitigation technology for holistic resource management in sub-Saharan Africa: Impact on greenhouse gas emissions

This study investigates the impact of climate change mitigation technologies, specifically agricultural land management and renewable energy consumption and production, on greenhouse gas emissions (GHG) in sub-Saharan Africa (SSA) over the period of 1991 to 2015. Our analysis was conducted using an ARDL panel data model with data from 26 countries representing four sub-regions. The results demonstrated that an increase in renewable energy consumption is significantly associated with a decrease in GHG emissions, with a long-term coefficient of -0.422 and a short-term coefficient of -0.757. Additionally, natural resource rents, agricultural land use and population density have a positive impact on greenhouse gas emissions, with coefficients of 0.0605, 0.392 and 0.690, respectively. However, renewable energy production does not have a significant effect on greenhouse gas emissions. This suggests that promoting renewable energy consumption can be an effective way to combat greenhouse gas emissions in the region, and policymakers should implement policies and programs that encourage and facilitate the adoption of renewable energy whilst taking into consideration the impact of agricultural land use. Overall, this study emphasizes the importance of promoting renewable energy consumption and managing agricultural land use as a viable approach to combating greenhouse gas emissions in sub-Saharan Africa, and highlights the potential of climate mitigation technology as a tool for regulators to optimize policy development and counter climate change.

Managing energy consumption by adapted energy performance contracting modes in rural China

Energy performance contracting (EPC) as a market instrument has been effective in promoting energy efficiency worldwide, but it has encountered many insurmountable obstacles in rural energy management. In this study, based on the characteristics of energy management in rural areas, three EPC modes are designed and tested in 24,000 rural households. The test results show that two adapted EPC modes of local government involvement and energy payment directly from the national grid can effectively overcome the barriers encountered in the traditional EPC modes and work well under the economic and social environmental conditions in rural areas. The key to the adaptation of the traditional EPC modes is the introduction of the local government as the third party. Participation of the third party can effectively reduce and remove the barriers and risks and increase the mutual trust between the clients (households) and the energy service companies (ESCOs). Based on the testing results, this study suggests that governmental departments should formulate relevant EPC policies and technical guidelines within the rural context. This research recommends that farmers should not manage their energy services by themselves and it is suggested to out-contracting ESCOs by applying the modes developed and tested by this paper.

Analysis of patterns of electricity consumption in mining and processing enterprises

Mining and processing enterprises are large consumers of electricity, with a large share of energy costs in the cost of production. Thus, the price of electricity in the cost of production takes higher values for Hydrometallurgical Plant 1. In this regard, the development of energy consumption models that can be used as a basis for managing the energy consumption process is an important component of learning. It is important to analyze the energy consumption to estimate the electricity consumption under different operating conditions of the mining industry. In order to study and plan electricity consumption and develop methods for regulating electricity consumption, it is necessary to determine the place of each technological stage in the energy consumption balance [1]. This article examines the laws of electricity consumption in mining and processing enterprises. The study of such laws helps to determine the level of energy consumption management for the mining and processing plant.

ANALYSIS REDUCTION OF ENERGY LOSSES IN DISTRIBUTION NETWORKS

The article analyzes the reduction of energy losses in distribution networks, which is a serious problem requiring an integrated approach, including technological, regulatory, and behavioral interventions. Some measures that can be developed and implemented to solve this problem are given, such as infrastructure modernization, taking into account investments in the modernization of distribution infrastructure, affecting a significant reduction in energy loss, and the introduction of load management strategies that optimize energy distribution and reduce losses. This includes measures such as load balancing, voltage regulation, and demand management programs to shift peak loads to off-peak hours. The integration of smart grid technologies allows monitoring and management of the distribution network in real-time. This includes the deployment of advanced measurement infrastructure, sensors, and automation systems to more effectively detect and reduce energy losses. Technological losses are associated with the technology of the process of transmission of electricity through networks. Commercial losses are measured by the difference between actual estimated losses and technological losses, taking into account commercial losses, electric energy entering the electric grid, electric energy supplied to consumers, and electricity costs for own needs of substations. Circuit engineering methods for accounting for energy losses in electrical networks are used in various combinations. At the same time, it should be borne in mind that the sequence of reporting operations using the results of specified specific parameters should give an accurate result. To determine the loss of electricity, data on the minimum and maximum load of the network and the amount of electricity consumed over the same period are required. The calculation is given by the method of average load. Information about the minimum and maximum network load is a key element for effective design, management and maintenance of electrical networks. This information allows you to optimize the use of resources and ensure reliable network operation in various conditions. Minimum network load - the minimum load is the minimum amount of energy that is consumed by the network during a certain period of time, for example, during night hours or periods of low activity. Information about the minimum load allows you to optimize network operation, for example, by turning off part of the etquipment or managing energy consumption during off-peak periods. Maximum network load - is the maximum amount of energy consumed by the network at a given time. This may be during periods of peak demand, for example, during a heat wave when a lot of air conditioners are running, or during an increase in production in industrial areas. The maximum load information allows you to determine the required power and capacity of the network equipment to ensure reliable operation under maximum load conditions. Keywords: distribution networks, network load, average load, power, period, power factor, electrical energy, duty cycle, consumers, practical research, voltage.

Importance of natural energy, and blue resources to address environmental challenges in view of the Saudi Vision 2030

AbstractThe purpose of this study is to scrutinize the impact of natural resources, energy, and blue economic indicators on the ecological footprint of Saudi Arabia over the period from 1993 to 2022 by employing a nonlinear autoregressive distributed lag model. We have simulated the missing data using the Markov Chain Monte Carlo algorithm. The study encompasses the effects of post‐Vision 2030 and post‐COVID‐19 policies and their assistance in minimizing the environmental footprint. The observed results determine that non‐renewable energy consumption increases the ecological footprint, whereas natural resources and biocapacity drop the ecological footprints in the case of pre‐ and post‐Vision 2030. For variable fisheries production, the post‐ and pre‐Vision 2030 results demonstrate a boosted ecological footprint in Saudi Arabia, with the highest coefficient among all results. This research offers valuable insights into how Saudi Arabia's energy consumption and natural resource management impact its ecological footprint, highlighting the effectiveness of Vision 2030 and post‐COVID policies in promoting environmental sustainability. The study's findings provide crucial guidance for policy making to reduce environmental impact while considering the role of fisheries and biocapacity in ecological balance. Based on empirical findings, this study commends some policy understandings that assist in being effectively implemented towards a sustainable environment.

A peer-to-peer trading model to enhance resilience: A blockchain-based smart grids with machine learning analysis towards sustainable development goals

Blockchain technology, with its peer-to-peer trading feature, influences the management of energy consumption by offering the potential to transform transparency, efficiency, and sustainability within the energy sector. Nonetheless, there is a need to develop analytical decision-making models tailored for managing peer-to-peer energy transactions to improve energy resilience. Therefore, the purpose of this paper is to address the research question: How can energy distribution systems be protected via blockchain technology to enhance energy resilience and mitigate vulnerabilities to disruptions? This paper employs a conceptual research model design and a mathematical decision-making model to address the research question by capturing the peer-to-peer trading capability of blockchain technology. The theory of planned behavior provides theoretical explanations for the proposed model. The sample includes longitudinal energy consumption data from 2015 to 2023 in Texas. The findings indicate a significant improvement in energy efficiency along with a considerable decrease in total electricity consumption. Post hoc analysis results reveal that the seasonal autoregressive integrated moving average algorithm is effective as a reliable input for the proposed mathematical model. The significant implications are to implement blockchain-based smart grids in which energy systems become more resilient to disruptions, as the peer-to-peer capability enables users to trade energy. The proposed model suggests that energy will be used more efficiently and effectively. This paper contributes to prior works by introducing a mathematical model that captures the trading behavior of energy consumers. Moreover, this paper proposes the SARIMA algorithm to predict energy demand.

LEC-MiCs: Low-Energy Checkpointing in Mixed-Criticality Multi-Core Systems

With the advent of multicore platforms in designing Mixed-Criticality Systems (MCSs), simultaneous management of reliability and energy while guaranteeing an acceptable service level for low-criticality tasks is a crucial challenge. To ensure the reliability of the MCSs against transient faults, fault-tolerant techniques are employed which will increase energy consumption. To mitigate the energy overhead, the Dynamic Voltage and Frequency Scaling (DVFS) technique will be exploited. However, this technique might lead to violating the timing constraints of high-criticality tasks. Therefore, this paper presents, for the first time, the low-energy checkpointing technique to guarantee the reliability of multiple preemptive periodic mixed-criticality tasks in a multicore platform. In contrast to the previous works in checkpointing technique which consider a specific number of faults that all the tasks in the system should tolerate, in this paper, the number of tolerable faults for each execution section of a task, and in each voltage and frequency level is determined through proposed formulas to meet the reliability target based on safety standards. Then, our proposed method determines the number of checkpoints and their non-uniform intervals for the normal and overrun sections of each task to reduce energy consumption, respectively. Moreover, the unified demand bound function (DBF) analysis is proposed for analyzing the schedulability of the task set, where each high-criticality task meets its timing and reliability constraints, and low-criticality tasks execute based on their derived guaranteed periods in each operational mode of the system. Experimental results show that our proposed scheme meets the timing and reliability constraints while at the same time, improving the QoS of low-criticality tasks, and managing energy consumption with an average of 29.49%, and 32.78%, respectively.

An enhanced coati optimization algorithm for optimizing energy management in smart grids for home appliances

This research presents an innovative approach to energy management in smart homes, aiming to efficiently regulate energy demands while ensuring customer loyalty. The focus is on addressing the limitations of existing demand-side management (DSM) programs, which predominantly target the residential sector. The proposed solution introduces the Adaptive Coati Optimization algorithm, which optimizes device organization based on Critical-Peak-Price and Real-Time-Price power payment systems. By strategically managing energy consumption, the algorithm reduces electrical expenses and peaks without compromising user convenience. The study evaluates the effectiveness of the approach across three operational periods (60 minutes, 12 minutes, and 24 minutes) to align with varying customer needs. Overall, the Adaptive Coati Optimization algorithm offers a promising solution for cost-efficient energy management in smart homes, combining both financial benefits and enhanced user satisfaction. The results indicate a significant decrease in tariffs rates, up to 30%, leading to a 20% increase in user satisfaction and a 25% improvement in cost utilization.

Short-Term Load Forecasting Method for Industrial Buildings Based on Signal Decomposition and Composite Prediction Model

Accurately predicting the cold load of industrial buildings is a crucial step in establishing an energy consumption management system for industrial constructions, which plays a significant role in advancing sustainable development. However, due to diverse influencing factors and the complex nonlinear patterns exhibited by cold load data in industrial buildings, predicting these loads poses significant challenges. This study proposes a hybrid prediction approach combining the Improved Snake Optimization Algorithm (ISOA), Variational Mode Decomposition (VMD), random forest (RF), and BiLSTM-attention. Initially, the ISOA optimizes the parameters of the VMD method, obtaining the best decomposition results for cold load data. Subsequently, RF is employed to predict components with higher frequencies, while BiLSTM-attention is utilized for components with lower frequencies. The final cold load prediction results are obtained by combining these predictions. The proposed method is validated using actual cold load data from an industrial building, and experimental results demonstrate its excellent predictive performance, making it more suitable for cold load prediction in industrial constructions compared to traditional methods. By enhancing the accuracy of cold load predictions. This approach not only improves the energy efficiency of industrial buildings but also promotes the reduction in energy consumption and carbon emissions, thus contributing to the sustainable development of the industrial sector.

Theoretical Framework and Research Proposal for Energy Utilization, Conservation, Production, and Intelligent Systems in Tropical Island Zero-Carbon Building

This study aims to theoretically explore the technological systems of tropical island zero-carbon building (TIZCB) to scientifically understand the characteristics of these buildings in terms of energy utilization, energy conservation, energy production, and intelligent system mechanisms. The purpose is to address the inefficiencies and resource wastage caused by the traditional segmented approach to building energy consumption management. Thus, it seeks to achieve a comprehensive understanding and application of the zero-carbon building (ZCB) technology system. This article focuses on the demands for energy-efficient comfort and innovative industrialization in construction. Through an analysis of the characteristics of TIZCB and an explanation of their concepts, it establishes a theoretical framework for examining the system mechanisms of these buildings. Additionally, it delves into the energy utilization, energy conservation, energy production, and intelligent system from macro, meso, and micro perspectives. This approach results in the development of an implementation strategy for studying the mechanisms of energy usage, conservation, and intelligent production systems in TIZCB. The results show that: (1) this study delves into the theoretical underpinnings of TIZCB, emphasizing their evolution from a foundation of low-carbon and near-zero energy consumption. The primary goal is to achieve zero carbon emissions during building operation, with reliance on renewable energy sources. Design considerations prioritize adaptation to high-temperature and high-humidity conditions, integrating regional culture along with the utilization of new materials and technologies. (2) A comprehensive technical framework for TIZCB is proposed, encompassing energy utilization, conservation, production capacity, and intelligent systems. Drawing from systems theory, control theory, and synergy theory, the research employs a macro–meso–micro analytical framework, offering extensive theoretical support for the practical aspects of design and optimization. (3) The research implementation plan establishes parameterized models, unveiling the intricate relationships with building performance. It provides optimized intelligent system design parameters for economically viable zero-carbon operations. This study contributes theoretical and practical support for the sustainable development of TIZCB and aligns with the dual carbon strategy in China and the clean energy free trade zone construction in Hainan.