Optimization Of Energy Consumption Research Articles

Investigating the Impact of AI/ML for Monitoring and Optimizing Energy Usage in Smart Home

Integrating artificial intelligence (AI) and machine learning (ML) into smart home systems has significantly advanced and improved residential energy efficiency, addressing growing concerns around energy conservation and sustainability. Choosing appropriate AI/ML techniques to optimize energy consumption in the dynamic and contemporary smart home environment remains a complex challenge. This study investigates a range of AI/ML algorithms such as regression models, deep learning, clustering, and decision trees to enhance energy management in smart homes. The study highlights the core processes of smart home energy optimization, including data acquisition, feature extraction, and model evaluation, as well as the specific roles of each AI/ML technique in optimizing energy usage. The study also discusses the strengths and weaknesses of the AI/ML techniques used for smart homes. It further explores the application areas and emerging challenges such as data security risks, high implementation costs, and gaps in existing technology that impact the scalability of AI/ML solutions in smart home contexts. The findings reveal that AI/ML techniques can effectively transform energy management in smart homes, enabling real-time optimization and adaptive decision-making to minimize energy consumption and reduce costs. Additionally, the study highlights future research directions.

Multi-Objective Optimization of Injection Parameters and Energy Consumption Based on ANN-Differential Evolution

Multi-Objective Optimization of Injection Parameters and Energy Consumption Based on ANN-Differential Evolution

A Review of Worldwide Strategies for Promoting High-Temperature Heat Pumps

This paper provides a comprehensive overview of policies and incentives aimed at promoting high-temperature heat pumps (HTHPs) globally. It examines the various strategies employed by different countries to encourage the adoption of HTHPs, highlighting both the opportunities and the barriers encountered in these efforts. The analysis includes a review of financial incentives, regulatory measures, and technological initiatives designed to facilitate the integration of HTHPs into industrial applications. By describing different approaches across regions, this paper identifies best practices and potential pitfalls, offering a nuanced understanding of how different policy frameworks impact the deployment of HTHPs. Additionally, this paper explores the technological challenges that influence the effectiveness of these policies. The findings underscore the critical role of supportive policies in overcoming technical and economic barriers, ultimately fostering the widespread use of HTHPs as a viable solution for reducing industrial emissions and advancing global decarbonization goals. Furthermore, utilizing electricity from renewable energy sources (RESs), HTHPs can also contribute to grid stability by reducing electricity peaks. This aspect enhances the integration of RESs into the energy mix, creating a more resilient grid and optimizing energy consumption patterns in industrial applications.

Advancing adaptive facades for a sustainable future in building design

The essential need to mitigate carbon emissions has brought the energy performance of the building sector into focus. Conventional static façades, which are unable to adapt to dynamic external conditions, often compromise energy efficiency and occupant comfort. In contrast, adaptive façades present a groundbreaking opportunity to transform building design by responding dynamically to environmental stimuli, optimizing energy consumption, and enhancing indoor conditions. This paper explores the recent advancements in adaptive façade systems, emphasizing their potential to improve thermal comfort, daylight performance, and most essential the energy efficiency. In addition, this paper highlights the transformative role of quantum energy and smart materials in advancing façade performance and sustainable design. Overall, this paper underscores the potential of using adaptive façades to transform sustainable building practices, offering architects, researchers, and designers a roadmap for integrating innovative and energy-efficient solutions into the built environment.

GTD‐CER: Game‐Theoretic Dynamic Clustering for Enhanced Efficiency and Reliability in Wireless Sensor Networks

ABSTRACTEfficient cluster formation is a significant issue in wireless sensor networks (WSNs) for optimizing energy consumption, enhancing data reliability, and prolonging network lifetime. This paper proposes a dynamic cluster formation scheme modeled as a noncooperative game, where each sensor node acts as a player making strategic decisions on whether to become a cluster head (CH) or remain a member node (MN). In this role selection decision‐making phase, nodes evaluate their potential utility function, which incorporates energy consumption, data reliability, and data forwarding efficiency. Nodes dynamically switch roles based on their utility to achieve an optimal network configuration. Simulations conducted using the NS‐3 network simulator demonstrate that the proposed game theory‐based clustering scheme significantly improves network performance. We analyze key measures like energy consumption, packet delivery ratio (PDR), and data forwarding, and the results shows that the proposed scheme reduces energy consumption by 25%, improves PDR by 15%, and extends the network lifetime by 20%, compared to traditional methods. The dynamic role‐switching mechanism prolongs network lifetime by preventing the rapid depletion of node energy, ensuring stable and efficient network operations. It significantly enhances network performance, stability, and longevity.

Controlling the Intensity of a Bulb Based on Surrounding Light Using Arduino and LDR

This research focuses on the design and implementation of an intelligent lighting system that automatically adjusts the intensity of a bulb based on the surrounding ambient light using an Arduino microcontroller and a Light Dependent Resistor (LDR). The proposed system aims to optimize energy consumption, enhance user comfort, and contribute to sustainable energy usage. The LDR serves as a sensor to measure the intensity of ambient light, producing a voltage signal proportional to the surrounding illumination. This signal is processed by the Arduino, which controls the bulb's brightness using Pulse Width Modulation (PWM) via a driver circuit. The system ensures that the bulb emits minimal light in bright environments and increases brightness in darker conditions, thus maintaining a consistent level of illumination. Experimental results demonstrate the system's responsiveness, accuracy, and ability to significantly reduce unnecessary energy consumption. The paper concludes with an analysis of potential applications, such as smart homes, offices, and streetlights, and suggests future enhancements, including integration with IoT platforms for remote monitoring and control.

Intelligent Data-Driven Task Offloading Framework for Internet of Vehicles Using Edge Computing and Reinforcement Learning

Introduction: The Internet of Vehicles (IoV) was enabled through innovative developments featuring advanced automotive networking and communication to fulfill the need for real-time applications that are latency-sensitive, such as autonomous driving and emergency management. Given that the servers were much farther away from the actual site of operation, traditional cloud computing faced huge delays in processing. Mobile Edge Computing (MEC) resolved this challenge by enabling localized data processing, reducing latency and enhancing resource utilization.Methods: This study proposed an Efficient Mobile Edge Computing-based Internet of Vehicles Task Offloading Framework (EMEC-IoVTOF). The framework integrated deep reinforcement learning (DRL) to optimize task offloading decisions, focusing on minimizing latency and energy consumption while accounting for bandwidth and computational constraints. Offloading costs were calculated using mathematical modeling and further optimized through Particle Swarm Optimization (PSO). An adaptive inertia weight mechanism was implemented to avoid local optimization and enhance task allocation decisions.Result: The proposed framework was thus proved effective for any latency reduction and energy consumption optimization in efficiently improving the overall system performance. DRL and MEC together facilitate scalability in task distribution by ensuring robust performance in dynamic vehicular environments. Integration with PSO further enhances the decision-making process and makes the system highly adaptable to dynamic task demands and network conditions.Discussion:The findings highlighted the potential of EMEC-IoVTOF to address key challenges in IoV systems, including latency, energy efficiency, and bandwidth utilization. Future research could explore real-world deployment and adaptability to complex vehicular scenarios, further validating its scalability and reliability.

RESEARCH AND EVALUATION OF CNC CUTTING PROCESS BASED ON ENERGY CONSUMPTION OPTIMISATION, 23-31.

RESEARCH AND EVALUATION OF CNC CUTTING PROCESS BASED ON ENERGY CONSUMPTION OPTIMISATION, 23-31.

Improving energy efficiency and fault tolerance of mission-critical cloud task scheduling: A mixed-integer linear programming approach

Cloud services have become indispensable in critical sectors such as healthcare, drones, digital twins, and autonomous vehicles, providing essential infrastructure for data processing and real-time analytics. These systems operate across multiple layers, including edge, fog, and cloud, requiring efficient resource management to ensure reliability and energy efficiency. However, increasing computational demands have led to rising energy consumption and frequent faults in cloud data centers. Inefficient task scheduling exacerbates these issues, causing resource overutilization, execution delays, and redundant processing. Current approaches struggle to optimize energy consumption, execution time, and fault tolerance simultaneously. While some methods offer partial solutions, they suffer from high computational complexity and fail to effectively balance the workloads or manage redundancy. Therefore, a comprehensive task scheduling solution is needed for mission-critical applications. In this article, we introduce a novel scheduling algorithm based on Mixed Integer Linear Programming (MILP) that optimizes task allocation across edge, fog, and cloud environments. Our solution reduces energy consumption, execution time, and failure rates while ensuring balanced distribution of computational loads across virtual machines. Additionally, it incorporates a fault tolerance mechanism that reduces the overlap between primary and backup tasks by distributing them across multiple availability zones. The scheduler’s efficiency is further enhanced by a custom-designed heuristic, ensuring scalability and practical applicability. The proposed MILP-based scheduler demonstrates significant average improvements over the best state-of-the-art algorithms evaluated. It achieves a 9.63% increase in task throughput, reduces energy consumption by 18.20%, shortens execution times by 9.35%, and lowers failure probabilities by 11.50% across all layers of the distributed cloud system. These results highlight the scheduler’s effectiveness in addressing key challenges in energy-efficient and reliable cloud computing for mission-critical applications.

Building energy management model integrating rule-based control algorithm and genetic algorithm

Energy is a crucial material foundation for the development of human society. Building energy consumption accounts for a significant proportion of global energy consumption. Optimizing building energy management is of great significance for achieving sustainable development. A building energy management model that integrates rule-based control algorithm and genetic algorithm is proposed, aiming to optimize building energy utilization and reduce operating costs. Mathematical models for different devices in the building energy system are established, and the rule-based control algorithm is used to provide system decision support. Then, the genetic algorithm is integrated to address the complexity and uncertainty of energy optimization problems. The comparative test results showed that the proposed fusion algorithm had higher fitness values and faster convergence speed. The root mean square errors of the algorithm in the training and testing sets were 43.6544 and 43.6844, with the lowest error and highest accuracy among the four algorithms. The simulation experiment results showed that the building energy management model integrating rule-based control algorithm and genetic algorithm had energy expenditures of 788.3 yuan and 967.6 yuan for two types of buildings, respectively. Taking Building 1 as an example, compared with Supervisory Control and Data Acquisition (SCADA), Beetle Antennae Search and Particle Swarm Optimization (BAS-PSO) algorithm, and Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN) algorithm, the proposed model reduced the cost of energy consumption optimization by 39.30%, 28.32%, and 20.20%, respectively. Overall, the proposed building energy management model effectively reduces operating costs, utilizes building energy, and contributes to daily building energy management and decision support.

Improving Energy Efficiency with Energy Recovery for Propylene Production

Propylene production through the metathesis of 2-butene is a well-established method. However, energy efficiency in this process remains an area of improvement. This study focuses on optimizing energy consumption by integrating heat recovery and reducing the reliance on external energy sources. Simulations were conducted using Aspen HYSYS V11 to compare the basic and modified processes. Modifications included utilizing heat from condensers and coolers to power compressors and heaters, eliminating redundant heating units. Thermodynamic analyses confirmed the endothermic nature of the reactions. Results indicated a significant reduction in total heat flow, from 2.983×108 kJ/h to 1.564×108 kJ/h, leading to improved specific energy consumption. With a production capacity of 15,400 tons/year, the optimized process demonstrated enhanced energy efficiency and sustainability. This study highlights the potential of process modifications to achieve energy savings, lower production costs, and minimize environmental impact in the chemical industry. Copyright © 2024 by Authors, Published by Universitas Diponegoro and BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Performance Analysis of NOMA Systems over Rayleigh Fading Channels in 5G Communication

This paper aims to analyse the performance of Non-vertical Multiple Access (NOMA) systems operating in radio communication systems, especially under Rayleigh fading channels. While NOMA technology improves spectrum efficiency by allowing users to share the same time and frequency resource, Rayleigh fading channels represent multipath propagation effects commonly encountered in real radio environments. In this research, the effects of different user power levels and channel conditions on performance metrics such as system capacity, error rate (BER) and energy efficiency are investigated. Using mathematical modelling and simulation methods, the performance advantages and limitations of NOMA under Rayleigh fading conditions are evaluated. The findings show that NOMA systems offer significant advantages in terms of spectrum efficiency and user fair sharing in Rayleigh fading channels, but require careful design for error rate and energy consumption optimisation. This study provides an important reference for more effective implementation of NOMA technology in radio communication systems.

Inverter technology and the role of the user behavior: Towards a more efficient use of energy

The article analyzes the energy efficiency of air conditioning (AC) systems with inverter technology compared to On/Off systems, highlighting the importance of user behavior in energy consumption. AC is essential for comfort and health, but it also contributes significantly to CO2 emissions due to high energy consumption. Inverter technology can reduce energy consumption by maintaining a more stable internal temperature and reducing operating costs and emissions in comparison with On/Off systems. However, incorrect use by users, such as turning the AC to maximum capacity and then reducing it, lead to energy inefficiencies. Thus, users play a crucial role in the control to improve energy efficiency. It has been studied that the participation of users in temperature management and the advanced technologies use optimize energy consumption and reduce environmental impact. i.e., the adoption of inverter systems and user education are essential to achieve a more sustainable and efficient use of energy in hot climate areas.

An Optimized Beluga Whale Approach for Migration to Reduce Power and Service Level Agreement in Real-Time System

Managing power consumption in cloud data centers has become a critical challenge. Live container migration is a technology supporting energy efficiency in this context. To address the hurdles of power consumption management, thereby mitigating carbon emissions and minimizing service level agreement (SLA) violations, we propose an approach utilizing a real-time server with the Beluga Whale Optimization Algorithm (BWOA) for container migration. The proposed approach aims to optimize energy consumption while ensuring SLA compliance. The BWOA, a machine learning-based method, is employed to predict the resource requirements of containers and migrate them to hosts with sufficient resources. We implemented the proposed approach in a real-time cloud server and compared its performance with other algorithms in terms of response time. The results demonstrate a remarkable 30% improvement in response time, leading to reduced SLA violations and optimized power consumption in containerized data centers.

Intelligent adjustment and energy consumption optimization of the fresh air system in hospital buildings based on Fuzzy Logic and Genetic Algorithms

Intelligent adjustment and energy consumption optimization of the fresh air system in hospital buildings based on Fuzzy Logic and Genetic Algorithms

Experimental investigation on the carob extraction using arrhenius approach and response surface

Environmental problems have increased the need for sustainable agricultural practices that conserve water and energy. Carob, an eco-friendly crop with multiple health benefits, holds the potential for economic evaluation. This study investigates the carob molasses extraction process, focusing on the influence of temperature and water quantity on the diffusion coefficient. The rheological behavior of carob molasses was analyzed experimentally, and a model was developed to optimize energy consumption during the extraction process. The impact of temperature on the mass transfer coefficient was examined using the Arrhenius approach, with the extraction conducted at a maximum of 50 °C to prevent caramelization. The activation energy for carob extraction was determined to be 5.475 kJ/mol, and a new equation is proposed for estimating the mass transfer coefficient.

Intelligent Sensor Software for Robust and Energy-Sustainable Decision-Making in Welding of Steel Reinforcement for Concrete.

In today's industrial landscape, optimizing energy consumption, reducing production times, and maintaining quality standards are critical challenges, particularly in energy-intensive processes like resistance spot welding (RSW). This study introduces an intelligent decision support system designed to optimize the RSW process for steel reinforcement bars. By creating robust machine learning models trained on limited datasets, the system generates interactive heat maps that provide real-time guidance to production engineers or intelligent systems, enabling dynamic adaptation to changing conditions and external factors such as fluctuating energy costs. These heat maps serve as a flexible and intuitive tool for identifying robust operational points that balance quality, energy efficiency, and productivity. The proposed methodology advances decision-making in welding processes by combining robust predictive modeling with innovative visualization techniques, offering a versatile solution for multiobjective optimization in real-world industrial applications.

Artificial Intelligence and Smart Technologies in Safety Management: A Comprehensive Analysis Across Multiple Industries

The integration of Artificial Intelligence (AI) and smart technologies into safety management is a pivotal aspect of the Fourth Industrial Revolution or Industry 4.0. This study conducts a systematic literature review to identify and analyze how AI and smart technologies enhance safety management across various sectors within the Safety 4.0 paradigm. Focusing on peer-reviewed journal articles that explicitly mention “Smart”, “AI”, or “Artificial Intelligence” in their titles, the research examines key safety management factors, such as accident prevention, risk management, real-time monitoring, and ethical implementation, across sectors, including construction, industrial safety, disaster and public safety, transport and logistics, energy and power, health, smart home and living, and other diverse industries. AI-driven solutions, such as predictive analytics, machine learning algorithms, IoT sensor integration, and digital twin models, are shown to proactively identify and mitigate potential hazards, optimize energy consumption, and enhance operational efficiency. For instance, in the energy and power sector, intelligent gas meters and automated fire suppression systems manage gas-related risks effectively, while in the health sector, AI-powered health monitoring devices and mental health support applications improve patient and worker safety. The analysis reveals a significant trend towards shifting from reactive to proactive safety management, facilitated by the convergence of AI with IoT and Big Data analytics. Additionally, ethical considerations and data privacy emerge as critical challenges in the adoption of AI technologies. The study highlights the transformative role of AI in enhancing safety protocols, reducing accident rates, and improving overall safety outcomes across industries. It underscores the need for standardized protocols, robust AI governance frameworks, and interdisciplinary research to address existing challenges and maximize the benefits of AI in safety management. Future research directions include developing explainable AI models, enhancing human–AI collaboration, and fostering global standardization to ensure the responsible and effective implementation of AI-driven safety solutions.

Adaptive Software Component for Predicting Energy Consumption of an Electric Vehicle on a Planned Route

This paper presents the development of an adaptive software component for forecasting the energy consumption of electric vehicles along planned routes. It includes a conceptual framework, architectural design, and modular software composition, introducing mathematical models for managing energy consumption with a model-based design approach for precise predictions and optimization. The importance of data sources such as route information, vehicle condition, and driver behavior is emphasized to create a comprehensive state vector for energy optimization. Following ISO 26262 and A-SPICE 3.1 standards, the implementation uses a model-based approach with Simulink and aligns with the V-Model for rigorous validation. The methodology details segmenting routes and optimizing energy consumption for each segment, considering driving style and environmental conditions. The gradient search method adjusts energy consumption to minimize usage while maximizing comfort and ensuring route completion. This research lays the groundwork for future advancements in predictive energy management systems for electric vehicles, with potential real-world applications. Future work will focus on refining predictive models, exploring machine learning for improved accuracy, and integrating real-time data from connected vehicle technologies for dynamic optimization.

An energy-efficient adaptive two stage pipeline RISC-V based soft-processor for edge-computing applications

This paper introduces a highly adaptable RV32I soft-processor architecture tailored for energy-restricted edge computing. Notably, it features a adptive two-stage pipeline with dynamic control to optimise energy consumption, activating the second stage selectively during memory operations. Implemented in Verilog HDL and validated on an FPGA platform, the architecture achieves impressive energy efficiency at 0.54 mW/M Hz. Comparative experiments demonstrate a substantial 28.2% reduction in average power usage compared to state-of-the-art single-cycle RV32I processors. These advancements position the design as highly suitable for energy-constrained edge computing applications, addressing critical efficiency challenges in resource-limited environments.