Optimization Of Energy Consumption Research Articles

Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings

In the current context of energy transition and increasing climate change, optimizing building performance has become a critical objective. Efficient energy use and occupant comfort are paramount considerations in building design and operation. To address these challenges, this study introduces a predictive model leveraging Machine Learning (ML) algorithms. The model aims to predict thermal comfort levels and optimize energy consumption in Heating, Ventilation, and Air Conditioning (HVAC) systems. Four distinct ML algorithms Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and EXtreme Gradient Boosting (XGBOOST) are employed for this purpose. Data for the model is collected using a network of Raspberry Pi boards equipped with multiple sensors. Performance evaluation of the ML algorithms is conducted using statistical error metrics, including, Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2). Results reveal that the RF and XGBOOST algorithms exhibit superior performance, achieving accuracies of 96.7% and 9.64% respectively. In contrast, the SVM algorithm demonstrates inferior performance with a R2 of 81.1%. These findings underscore the predictive capability of the RF and XGBOOST model in forecasting Predicted Mean Vote (PMV) values. The proposed model holds promise for enhancing occupant thermal comfort in buildings while simultaneously optimizing energy consumption in HVAC systems. Further research could explore the practical applications of these findings in building design and operation.

A device to device driven approach towards optimizing energy efficiency for 6G networks

Our study aims to develop more energy-efficient mobile communication systems through the exploration of the 6th generation (6G) technology that is expected to be implemented in 2033. We focus on the impact of device-to-device (D2D) communication on power efficiency, which is a crucial need in this domain. To achieve this, we conducted a pioneering experiment using an in-house testbed and K-means clustering to classify locations as D2D enabled or disabled. Our findings show that there is a dynamic clustering mechanism that enables certain nodes to sustain D2D functionality around temporary base stations, resulting in a remarkable 5% improvement in network lifetime per second. This research not only enhances our understanding of 6G networks but also provides a practical methodology for optimizing energy consumption, which holds significant implications for society in advancing sustainable and efficient communication.

Optimization of energy use efficiency and environmental assessment in soybean and peanut farming using the imperialist competitive algorithm

This study proposes using input-output analysis to improve energy use efficiency and assess environmental indicators in soybean and peanut farming. The research findings show that soybean production consumes 43170.20 MJ ha−1 of total energy and yields 40417.28 MJ ha−1 of output energy, while peanut production requires 28677.36 MJ ha−1 of total energy and produces 28677.36 MJ ha−1 of output energy. The LCA results indicate that on-farm emissions are associated with production inputs for both soybean and peanut systems, with diesel fuel and chemical fertilizers being the main contributors. Soybean farming shows higher levels of diesel fuel pollutants due to reduced usage, while peanuts have a higher prevalence of contaminants associated with diesel fuel. Using ICA to calculate optimized energy consumption results in 36672.77 MJ ha−1 for soybean production and 23485.45 MJ ha−1 for peanut production, leading to energy savings of 6075.33 MJ ha−1 and 5191.91 MJ ha−1, respectively. Additionally, the environmental impact category quantities for ICA analysis of soybean and peanut production reveal the resources per ton for soybean production to be 111.25 USD2013 and for peanut production to be 45.43 USD2013 in terms of energy use. The use of ICA demonstrates potential in maximizing energy efficiency and evaluating environmental indicators in soybean and peanut farming. By optimizing resource allocation and decision-making processes, ICA can help farmers reduce energy consumption and minimize environmental impact, ultimately leading to more sustainable and eco-friendly farming practices that benefit both the environment and the agricultural industry. Further research and implementation of ICA in farming practices have the potential to significantly improve energy efficiency and environmental sustainability in the soybean and peanut farming industry.

Finding the optimal reliable energy consumption path for electric vehicles under rainfall conditions

ABSTRACT This paper presents a new path-finding problem to ensure reliable energy consumption for electric vehicles (EVs) under rainfall conditions. The objective function of the proposed model aims to find a reliable path that minimises energy consumption while ensuring a certain probability of completing the trip without exhausting a given battery energy budget. By considering the influence of adverse weather conditions at different periods, the existing model is expanded. To address the non-additivity and non-linearity characteristics of the optimisation model, an enhanced heuristic algorithm is proposed, incorporating inequality techniques, the K-shortest algorithm, and path-updating strategies. Lastly, the proposed algorithm is validated using Hong Kong’s grid-based road network as a case study, which demonstrates the correctness and effectiveness of the algorithm. The results indicate that by considering adverse weather conditions, the estimation of energy consumption can be significantly improved in terms of accuracy, achieving more efficient and reliable optimal path recommendations.

Enhancing Underwater Wireless Sensor Networks With Flexible Communication and Positioning

An innovative approach centred around underwater wireless communication and positioning in Underwater Wireless Sensor Networks, addressing limited coverage by proposing a dual-hop system combining optical fiber and wireless links. Time frame design and Code Division Multiplexing Access enhance multi-user signal transmission efficiency. Proof-of-concept experiments validate feasibility, with the introduction of the hybrid fish eye routing protocol further enhancing performance by intelligently combining fish-eye routing and traditional protocols for improved coverage and reliability. Moreover, the hybrid fish eye protocol incorporates adaptive routing algorithms that dynamically adjust to changes in underwater conditions, ensuring robustness and adaptability in challenging environments. Additionally, the hybrid fish eye protocol optimizes energy consumption by minimizing unnecessary transmissions, thereby prolonging the lifespan of underwater sensor networks. With full-duplex communication, precise positioning, and an extended transmission range, convergent system demonstrates significant potential for supporting high-capacity transmission among mobile nodes in forthcoming underwater scenarios

Q-learning based scheduling method for continuous pickling process of titanium strips

This article addresses the energy consumption optimization problems of the pickling process for titanium strip manufacturing. The hybrid flow shop scheduling schemes for the pickling process of titanium strips are designed, and a novel shop scheduling method based on reinforcement learning is proposed for the pickling process of titanium strips. In the scheduling scheme, the pickling chemical treatment process of titanium strips are described as an asymmetric hybrid flow shop scheduling problem (AHFSP), and a mathematical model containing a temperature structure is established with the optimization objectives of minimizing pickling time and energy consumption. Based on the proposed scheduling scheme, a novel shop scheduling method based on reinforcement learning for the titanium strip pickling process is proposed. First, a mixed integer linear programing model for the mixed flow shop scheduling problem is established. Second, the flow shop scheduling problem with sequential energy consumption decisions is approximated as an asymmetric traveling sales-man problem (ATSP). Finally, the ATSP is described as a Markov decision processes (MDP), and a Q-learning based scheduling method for titanium strip pickling shops is proposed. Finally, the effectiveness of the proposed method is verified by examples, and the scheduling scheme can reduce the energy consumption by 16.61% on average while maintaining the schedule, which improves the productivity and economic efficiency.

Next‐generation energy‐efficient optical networking: DQ‐RGK algorithm for dynamic quality of service and adaptive resource allocation

SummaryIn green optical networking, designing an adaptive energy‐saving scheme plays a vital role, in optimizing energy consumption by dynamically adjusting resources based on network traffic and environmental conditions, to a more sustainable and efficient optical communication infrastructure. Traditional methods in optical networking face challenges such as static resource allocation, limited adaptability, inefficient power usage, environmental insensitivity, and scalability issues. Therefore this article proposed a novel method named Dynamic Quality of Service based Random update Genghis Khan (DQ‐RGK) algorithm, the proposed model can tackle the abovementioned complexities. In this study, cluster head dynamic placement is utilized to optimize the network's performance by adapting the placement of cluster heads to the current topology, load distribution, and energy levels in the network nodes. Additionally, Dynamic Quality of Service (QoS) is employed to respond dynamically to changes in network conditions, adapting to varying traffic patterns and resource availability. In this work, the Genghis Khan Shark optimization with a random update strategy is implemented for hyperparameter optimization to enhance the performance of the DQ‐RGK method. The DQ‐RGK adjusts the parameters of QoS in real‐time, and this ensures that network resources based on the requirements changed and priorities of applications, which ultimately optimizes performance and enhances user experience. By dynamically assigning and reallocating resources based on the current demand the algorithm enhances overall network efficiency and reduces energy consumption. Then, this work analyzes the experimental results, where some evaluation measures estimate the DQ‐RGK method's performance. Routing efficiency, latency, scalability, spectral efficiency, Packet Delivery Ratio, throughput, network lifetime, energy consumption, jitter, and energy consumption are the measures employed by the DQ‐RGK model. In The results, other routing models that do not provide efficiency are utilized, a comparison of these other routing models is represented in results. The overall DQ‐RGK model's effectiveness is represented in the experimental results and its effectiveness is greater among other methods.

Energy consumption optimization model and system for the pellet drying process of straight grate

The drying process is a crucial stage in the thermal treatment of iron ore pellets in a straight grate, as it has a direct impact on pellet quality and energy consumption. Consequently, this paper develops a simulation model for the pellet bed drying process, which is validated through industrial data and enables the visualization of temperature and moisture distribution within the pellet bed. Based on the simulation model, an optimization model for the drying process is developed to minimize the specific energy consumption per tonne of green pellets. The constraints of the drying process are the moisture content requirements and burst temperature. A genetic algorithm is employed to optimize the parameters such as trolley speed, pellet bed thickness, air temperature, and air pressure. The results show that, with an absolute error criterion of 10 °C, the temperature calculation accuracy of the simulation model exceeds 86%, effectively describing the drying process in a straight grate sintering machine. Under yield limitations, the optimal cumulative energy consumption per tonne of green pellet is reduced by 1.50% during the drying process. Finally, a simulation and optimization system for the drying process in the straight grate is implemented in C++. This system not only generates essential data for designing the drying section in a straight grate but also lays a theoretical foundation for system optimization and optimal control.

Estimation of Centrifugal Pump Efficiency at Variable Frequency for Irrigation Systems

The sustainability of the food production achieved with the help of irrigation systems and the sustainability of their energy consumption are major challenges of the current century. Pumping systems currently account for approximately 30% of global electrical energy consumption. As electricity prices rise, there is a growing need for technological advancements to enhance energy efficiency and reduce consumption costs effectively. This study focuses on operating centrifugal pumps at variable frequency as an effective means of achieving this goal. Most centrifugal pump manufacturers/providers traditionally assume that pump efficiency remains constant across various operating frequencies, often equating the efficiency at various frequencies to that at the standard frequency (50/60 Hz). In contrast, this paper introduces a new formula for estimating pump efficiency, crucial to precise power consumption determination, particularly in variable-frequency scenarios. The formula parameters are identified by using experimental data acquired from an existing pumping system. The tests and results presented in this paper demonstrate that the proposed formula outperforms the formulas of the current industry standards in accuracy. Practically, the new relation assures enhanced accuracy in estimating pump efficiency and absorbed power, allowing for the design of a more precise model used for control systems synthesis required for operating centrifugal pumps at variable frequency. This research offers a new way of calculating pump efficiency, which could be very useful for industry practitioners seeking to optimize energy consumption in pumping systems.

Fuzzy based trusted malicious unmanned aerial vehicle detection using in flying ad-hoc network

The communication security of unmanned aerial vehicles (UAVs) or drones based on flying ad hoc networks (FANETs) can be enhanced and maintained by assessing vulnerabilities, threats, and attacks. Thus, trust, high mobility, and complex communication interfaces are crucial for effective coordination and segregation of malicious drones from the genuine. A new efficient honesty-based detection scheme for malicious drones has been proposed to distinguish between intentional and unintentional misbehavior of the UAVs. The proposed scheme configures a drone system for packet transmission within the FANET, specifying buffer size and packet size variables to control data flow and prevent congestion. It then computes energy used for packet reception and transmission, optimizes energy consumption, and evaluates the drone’s mobility through the link stability index (LSI) and honesty UAV. The honesty UAV parameter categorizes UAVs into unintentional malicious and intentional malicious categories. Fuzzy logic helps identify intentional and unintentional misbehaving drones and improves performance in the network. It continuously generates the malicious UAV detection mechanism by assessing the parameters i.e., energy, pattern of movements, and the honesty score about the UAVs as a whole. Thereby a dynamic rating system to adeptly identify and differentiate the cooperative and non-cooperative nature of drones from the network is assimilated in the paper. Simulation results show the proposed scheme approximately aligns with the actual cases for varying numbers of malicious drones. Finally, the observation of the simulation result reflects substantial enhancement in performance metrics, with a significant cutback in end to end delay and packet delivery ratio by 10–30% and 20–50% respectively, contrasting with the existing techniques.

Energy optimization of the smart residential electrical grid considering demand management approaches

-Many researchers have investigated the optimal energy scheduling to meet the demand regarding sustainable development issues and the economic and technical indices. This study presents the day-ahead operation of a smart residential distribution electrical grid as a bi-stage-multiple-criteria decision-making modeling. The proposed energy optimization is implemented based on optimal demand management in upper-stage and multiple-criteria decision-making in lower-stage. The multiple-criteria problem is modeled from the viewpoint of the grid's operator to optimize energy consumption costs, power losses and demand side comfort. The optimal demand management in upper-stage is coordinated considering price traffic in the upstream grid. The load shifting approach and load interruption approach are presented as optimal demand management for residential consumers. The operation of demand management by using load shifting and load interruption approaches is done via deferrable loads and clippable loads in smart residential homes, respectively. The proposed energy optimization in both stages by improved sunflower optimization (IFSO) is handled, and the TOPSIS method is proposed for the best trade-off of the multiple-criteria decision-making. The day-ahead energy operation is applied on the 33-bus distribution network to investigate the effectiveness of the residential distribution electrical grid considering obtained results from mathematical modeling and demand management approaches.

Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization

As the demand for artificial intelligence (AI) models and applications continues to grow, data centers that handle AI workloads are experiencing a rise in energy consumption and associated carbon footprint. This work proposes a variational quantum computing-based robust optimization (VQC-RO) framework for control and energy management in large-scale data centers to address the computational challenges and overcome limitations of conventional model-based and model-free strategies. The VQC-RO framework integrates variational quantum circuits (VQCs) with classical optimization to enable efficient and uncertainty-aware control of energy systems in AI data centers. Quantum algorithms executed on noisy intermediate-scale quantum (NISQ) devices are used for value function estimation trained with Q-learning, leading to the formulation of a robust optimization problem with uncertain coefficients. The quantum computing-based robust control strategy is designed to address uncertainties associated with weather conditions and renewable energy generation while optimizing energy consumption in AI data centers. This work also outlines the computational experiments conducted at various AI data center locations in the United States to analyze the reduction in power consumption and carbon emission levels associated with the proposed quantum computing-based robust control framework. This work contributes a novel approach to energy-efficient and sustainable data center operation, promising to reduce carbon emissions and energy consumption in large-scale data centers handling AI workloads by 9.8 % and 12.5 %, respectively.

Sustainable Energy Consumption Analysis through Data Driven Insights

Energy is the backbone of our society, supporting our daily activities and driving progress. It plays a crucial role in shaping our modern way of life. The future of global energy consumption is influenced by many factors, including demographics, economic dynamics, technological developments, political actions, environmental demands and geopolitical considerations. As the world's population continues to grow and urbanize, the demand for energy is increasing. At the same time, rapid technological innovations are shaping the energy landscape and changing production, distribution and consumption patterns. In the midst of this development, it is very important to optimize energy consumption, accurately anticipate needs, curb climate change, limit emissions of greenhouse gasses, fight against pollution and promote sustainability. This study includes an in-depth analysis of historical consumption trends, assessing the multiple benefits of renewable energy integration, estimating carbon emissions, formulating practical policy recommendations and providing empirically informed insights. The work is based on various data obtained from platforms such as Kaggle and using advanced visualization techniques such as Power BI dashboards. The study provides invaluable perspectives on future energy needs, the penetration of renewable sources into the energy mix, and the strategic needs to achieve sustainable energy use.

Medium office energy consumption optimization using EnergyPlus

This research paper studies the impacts of HVAC system scheduling and building envelope material properties on the building energy efficiency in the downtown New York City, metropolitan area, that is highly associated with Urban Heat Island. By utilizing EnergyPlus for whole energy building simulation, the research compares two HVAC operational cases: a baseline case with constant temperature setpoints, and an occupancy-based temperature setpoint schedule. The study also investigated the influence of thermal conductivity variations in stucco exterior wall materials, with three cases, the default baseline thermal conductivity and a 15% variance, respectively. Results indicate that occupancy-driven scheduling significantly reduces energy consumption by approximately 16% annually compared to baseline. Conversely, the influence of thermal conductivity on energy consumption is minimal, with only a 0.35% deviation noted in response to a 15% variation in thermal conductivity. This suggests that both optimizing HVAC scheduling and the thermal conductivity of building envelope materials can save building energy consumption while optimizing HVAC scheduling has a relatively stronger impact than optimizing the thermal conductivity with a 15% variance in downtown New York City.

SMART REFRIGERATOR USING IoT

With the rapid advancement of technology, smart devices like refrigerators have become integral parts of modern living. In this context, our proposed model aims to enhance refrigerator functionality by incorporating features for food quantity detection, temperature monitoring, and spoilage reminders. This system utilizes a mobile application interface to provide users with real-time information regarding the quantity of food items in the refrigerator and the current temperature. By leveraging this data, users can efficiently plan their meals, reduce food wastage, and optimize energy consumption through automatic control mechanisms. Additionally, the system incorporates a spoilage reminder feature, alerting users to food items in air-tight containers that may spoil without emitting detectable odours. This comprehensive approach to refrigerator management not only enhances convenience but also promotes sustainability by minimizing food waste and energy usage. Keywords— - Embedded system, Internet of things, DHT11, IR detector, Arduino Uno

Negawatt-Driven Sustainable Energy Management Based-on Nanomaterial-Enhanced Batteries

With the dramatic increase of energy demand around the world, it has become a necessity to monitor and optimize energy consumption to conserve its use and decrease carbon emissions. This paper presents a comprehensive analysis of MicroGrids (MGs) that encompass a diverse array of energy sources, including renewable energy, Diesel Generators (DGs), and Battery Storage Systems (BSS). In this study, critical constraints are addressed such as distributed generator limits and grid power exchange. Moreover, the potential integration of nanomaterials within BSS to enhance energy efficiency and achieve Negawatt energy are explored. The main objectives are to evaluate the performance of various energy system configurations, with a specific focus on nanomaterial-enhanced batteries, and identify the optimal strategy to minimize operating costs while maximizing Negawatt energy production. By considering technical, economic, and environmental factors, this research aims to provide valuable insights into sustainable and efficient Microgrid solutions for future energy management.

Development and Validation of the HPLC Method for Simultaneous Quantification of Glucose and Xylose in Sugar Cane Bagasse Extract.

A validated rapid high-performance liquid chromatography (HPLC)-refractive index (RI) method was developed for the identification and quantification of glucose and xylose in hydrolyzed bagasse extract. The separation of compounds was achieved on Eurokat® H column (300 × 8mm, 10μm) at 75°C, using 0.01N sulfuric acid solution as mobile phase and 0.6mL/min as flow rate. The method was validated based on accuracy, precision, linearity, robustness, uncertainty, limit of detection (LOD) and limit of quantification (LOQ). Total chromatographic analysis time per sample was ~11min. Calibration plots were linear over the concentration ranges 11-100μg/100μL for glucose and xylose. The LOD was 0.8ppm and LOQ was 2.5ppm. The high recovery and low relative standard deviation confirm the suitability of the method for determination of glucose and xylose in bagasse extract. The proposed HPLC-RI method was accurate, fast and robust and required less run time due to less analytes retention times and allowed optimal energy consumption owing to lower column oven temperature.

Application of Renewable Energy in Green Buildings and Energy Consumption Optimization

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.

Energy management strategies based on soft actor critic reinforcement learning with a proper reward function design based on battery state of charge constraints

The environmental concerns have pushed our society to find solutions to reduce the emission of transportation systems. One promising solution is the fuel cell hybrid electric vehicle (FCHEV), with a proton exchange membrane fuel cell (PEMFC) and a lithium-ion battery. The presence of two different energy sources requires a smart energy management strategy (EMS) which must not only optimize energy consumption (conventional approaches), but also mitigate component’s degradation (New approaches : Health-conscious strategies). For that, a wide range of techniques has been proposed in the literature, and the most recent techniques are based on machine learning. Reinforcement learning (RL) is usually the most chosen strategy, but as in the optimization techniques, a strong design of the objective function (called reward function in RL) is required. The main challenge in the reward function is to encourage a proper battery state of charge (SOC) management, while reducing the energy consumption and avoiding the factors that accelerate the FC aging. The current advancement in the literature shows satisfactory results, but the reward function design have great improvement potential in the chosen approach. Indeed, most approaches in RL for SOC management with continuous actions are based on the reference SOC principle, which reduces the EMS ability to optimize the other part of the reward function. This paper reveals the challenges associated with the introduction of SOC limits in the reward function and proposes an approach, based on SOC boundaries in the reward function. The contribution allows to better consider the parts to optimize, as it gives more freedom than previous reference SOC techniques all reducing the consumption by 12.9%.

A decentralized federated learning based interoperable and heterogeneity aware predictive optimization method for energy and comfort in smart homes environment

It is a known fact that a comfortable residential space or workspace is crucial to productivity and well-being. In smart home environments, sensors can be used to sense environment and occupant data and actuators can be controlled accordingly for maximizing thermal comfort. However, keeping in view the growing demand of energy and scarcity of energy generation sources, it is mandatory to optimize the energy usage complementary to optimize thermal comfort. In order for the developed method to be interoperable and heterogeneity aware across different edge device vendors and for ease of hardware connectivity in smart homes, a REST API based framework is developed. Sensors mounted with IoT devices collect data from its respective smart home environment and store the data in its local database. An edge server is used to control the connectivity among these IoT devices and to initiate the decentralized federated learning mechanism. Each IoT device train local deep learning models for predicting energy usage, indoor temperature, indoor humidity for maintaining a comfortable environment state using minimal energy. Models from each IoT devices are aggregated at each IoT device iteratively to make federated learning independent of central server. Global deep learning model is iteratively trained for each prediction requirement. Later, these models are used to predict indoor temperature, humidity and energy consumption for time stamp (t+1), keeping in view environmental and occupant related sensed data at time stamp (t). Optimization of energy consumption and thermal comfort is mathematically modeled and based on the predictions of DL models, environment is controlled by handling actuators using a particle swarm optimization (PSO) mechanism. Relevant constraints are incorporated in PSO for limiting the positional variations in particles for efficient learning. The method continually learns and adapts to changes in the surroundings, providing real-time monitoring and control of thermal comfort to the end users. To validate our approach, data is collected and experiments are conducted in a real-time smart home’s environment. The results are compared with those obtained without using the proposed predictive optimization mechanism. The study demonstrates the effectiveness of proposed predictive optimization mechanism in providing a comfortable and productive smart home or workspace environment utilizing 38% less energy as compared to competitor.