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Journal articles on the topic 'Algorithm-driven consumption'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Yuan, Shijiao. "Analysis of Consumer Behavior Data Based on Deep Neural Network Model." Journal of Function Spaces 2022 (September 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/4938278.

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This paper divides the research modes of consumer purchase behavior characteristics into three categories: experience-driven mode, theory-driven mode, and data-driven mode. An analysis algorithm based on customer consumption behavior is proposed, and the idea of combining customer consumption behavior factors such as satisfaction and loyalty is proposed. Through comparison, it is pointed out that the data-driven model is most suitable for analyzing the characteristics of online consumers’ purchasing behavior. Using the decision support of knowledge base, different service schemes for customers

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Zhao, Xinxin, Ming Zhang, and Guangyu Xue. "Data-Driven Algorithm Based on Energy Consumption Estimation for Electric Bus." World Electric Vehicle Journal 14, no. 12 (2023): 329. http://dx.doi.org/10.3390/wevj14120329.

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The accurate estimation of battery state of charge (SOC) for modern electric vehicles is crucial for the range and performance of electric vehicles. This paper focuses on the historical driving data of electric buses and focuses on the extraction of driving condition feature parameters and data preprocessing. By selecting relevant parameters, a set of characteristic parameters for specific driving conditions is established, a process of constructing a battery SOC prediction model based on a Long short-term memory (LSTM) network is proposed, and different hyperparameters of the model are identi

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Yang, Ran, Zhaonan Li, Junhao Qian, and Zhihua Li. "Task-Driven Virtual Machine Optimization Placement Model and Algorithm." Future Internet 17, no. 2 (2025): 73. https://doi.org/10.3390/fi17020073.

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In cloud data centers, determining how to balance the interests of the user and the cloud service provider is a challenging issue. In this study, a task-loading-oriented virtual machine (VM) optimization placement model and algorithm is proposed integrating consideration of both VM placement and the user’s computing requirements. First, the VM placement is modeled as a multi-objective optimization problem to minimize the makespan of the loading tasks, user rental costs, and energy consumption of cloud data centers; then, an improved chaos-elite NSGA-III (CE-NSGAIII) algorithm is presented by c

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Chentouf, Mohamed, and Zine El Abidine Alaoui Ismaili. "A Novel Net Weighting Algorithm for Power and Timing-Driven Placement." VLSI Design 2018 (October 18, 2018): 1–9. http://dx.doi.org/10.1155/2018/3905967.

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Nowadays, many new low power ASICs applications have emerged. This new market trend made the designer’s task of meeting the timing and routability requirements within the power budget more challenging. One of the major sources of power consumption in modern integrated circuits (ICs) is the Interconnect. In this paper, we present a novel Power and Timing-Driven global Placement (PTDP) algorithm. Its principle is to wrap a commercial timing-driven placer with a nets weighting mechanism to calculate the nets weights based on their timing and power consumption. The new calculated weight is used to

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Noguera-Vivo, Jose Manuel, and María del Mar Grandío-Pérez. "Enhancing Algorithmic Literacy: Experimental Study on Communication Students’ Awareness of Algorithm-Driven News." Anàlisi 71 (January 29, 2025): 37–53. https://doi.org/10.5565/rev/analisi.3718.

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This article addresses the need for algorithmic literacy in the field of journalism and media education. Amid the escalating complexity of disinformation in the media landscape, the aim is to enhance users’ awareness and understanding of algorithm-driven content. Through focused research on communication students, the study investigates attitudes, beliefs and knowledge relating to the influence of algorithmic systems on news consumption. Existing scholarship is surveyed to establish the evolving nature of algorithmic literacy, ranging from optimizing search engines to countering misconceptions

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Sambu, Patach Arrojula. "Backend-Driven Efficient Upload Algorithm: Balancing Data Freshness and Device Energy Consumption." European Journal of Advances in Engineering and Technology 9, no. 2 (2022): 56–62. https://doi.org/10.5281/zenodo.13919560.

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In mobile applications, maintaining a balance between data freshness and device energy consumption is a critical yet challenging task. Frequent uploads of event data ensure real-time insights but can significantly drain battery life and consume network data, especially over cellular connections. Conversely, infrequent uploads conserve energy but lead to delayed data availability, impairing the ability of app owners to make timely and informed decisions. This paper proposes a backend-driven efficient upload algorithm that dynamically balances these conflicting requirements. The algorithm levera

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Zhang, Peng, Xin Ma, and Kun She. "A Novel Power-Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China." Complexity 2019 (November 6, 2019): 1–22. http://dx.doi.org/10.1155/2019/1510257.

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Along with the improvement of Chinese people’s living standard, the proportion of residential energy consumption in total energy consumption is rapidly increasing in China year by year. Accurately forecasting the residential energy consumption is conducive to making energy programming and supply plan for the administrative departments or energy companies. By improving the grey action quantity of traditional grey model with an exponential time term, a novel power-driven grey model is proposed to forecast energy consumption as reference data for decision makers. The nonlinear parameter of power-

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Hussain, Asad, Jacopo Cimaglia, Sabrina Romano, Francesco Mancini, and Valerio Re. "Data Driven Disaggregation Method for Electricity Based Energy Consumption for Smart Homes." Journal of Physics: Conference Series 2385, no. 1 (2022): 012006. http://dx.doi.org/10.1088/1742-6596/2385/1/012006.

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Abstract Sustainable energy systems must be capable of ensuring sustainable development by providing affordable and reliable energy to consumers. Hence, knowledge and understanding of energy consumption in the residential sector are indispensable for energy preservation and energy efficiency which can only be possible with the help of consumer participation. New energy efficiency methods are developed due to the global adoption of smart meters that monitor and communicate residential energy consumption. Moreover, energy monitoring of each appliance is not feasible, as it is a costly solution.

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Kassem, Sameh A., Abdulla H. A. EBRAHIM, Abdulla M. Khasan, and Alla G. Logacheva. "FORECASTING ELECTRIC CONSUMPTION OF THE ENTERPRISE USING ARTIFICIAL NEURAL NETWORKS." Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy 7, no. 1 (2021): 177–93. http://dx.doi.org/10.21684/2411-7978-2021-7-1-177-193.

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Energy consumption has increased dramatically over the past century due to many factors, including both technological, social and economic factors. Therefore, predicting energy consumption is of great importance for many parameters, including planning, management, optimization and conservation. Data-driven models for predicting energy consumption have grown significantly over the past several decades due to their improved performance, reliability, and ease of deployment. Artificial neural networks are among the most popular data-driven approaches among the many different types of models today.

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Huang, Kang, Jianjun Wu, Xin Yang, Ziyou Gao, Feng Liu, and Yuting Zhu. "Discrete Train Speed Profile Optimization for Urban Rail Transit: A Data-Driven Model and Integrated Algorithms Based on Machine Learning." Journal of Advanced Transportation 2019 (May 2, 2019): 1–17. http://dx.doi.org/10.1155/2019/7258986.

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Energy-efficient train speed profile optimization problem in urban rail transit systems has attracted much attention in recent years because of the requirement of reducing operation cost and protecting the environment. Traditional methods on this problem mainly focused on formulating kinematical equations to derive the speed profile and calculate the energy consumption, which caused the possible errors due to some assumptions used in the empirical equations. To fill this gap, according to the actual speed and energy data collected from the real-world urban rail system, this paper proposes a da

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Chen, Hao, Peng Du, Yuan Wang, Dafeng Jin, and Xiaomin Lian. "Dynamic energy-efficient torque allocation algorithm for in-wheel motor-driven vehicle." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 7 (2020): 1815–25. http://dx.doi.org/10.1177/0954407019899205.

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In-wheel motor-driven vehicle improves the overall performance with its torque vectoring system, which distributes the torque command of each motor. This paper proposes a novel torque allocation algorithm to dynamically optimize energy consumption of the vehicle. It splits the optimization problem into two sub-problems and obtains the executive torque of each side. The method also simplifies the solution by modification and discretization of feasible torque space, thus ensuring that there must be solvable and reducing online computational load. Two representative simulation cases—New European

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De Guzman, C. J. P., A. Y. Chua, T. S. Chu, and E. L. Secco. "Evolutionary Algorithm-Based Energy-Aware Path Planning with a Quadrotor for Warehouse Inventory Management." HighTech and Innovation Journal 4, no. 4 (2023): 829–37. http://dx.doi.org/10.28991/hij-2023-04-04-012.

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Quadrotors have been vital for automating warehouse processes. However, a significant gap in recent studies is that they use a single quadrotor with limited battery life, considering that their objective involves navigation in a large-scale environment such as a warehouse. Using an energy consumption model to enable more efficient navigation can be explored. Conventional data-driven energy models and path planning algorithms are insufficient for describing the various motions that a quadrotor can perform in warehouse operations, such as changes in yaw. This study aims to design a novel exhaust

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Sodhro, Ali Hassan, Li Chen, Aicha Sekhari, Yacine Ouzrout, and Wanqing Wu. "Energy efficiency comparison between data rate control and transmission power control algorithms for wireless body sensor networks." International Journal of Distributed Sensor Networks 14, no. 1 (2018): 155014771775003. http://dx.doi.org/10.1177/1550147717750030.

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This article presents comparison between data rate or rate control, that is, video transmission rate control algorithm and transmission power control algorithms for two different cases. First, energy consumption due to high peak variable data rates in video transmission. Second, energy depletion due to high transmission power consumption and dynamic nature of wireless on-body channel. The former one focuses on constant (fixed) transmission power level and variable data rate (“severe” conditions), for example, medical monitoring of the emergency patients. The latter considers variable transmiss

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Rocha, José Antonio Castán, Alejandro Santiago, Alejandro H. García-Ruiz, Jesús David Terán-Villanueva , Salvador Ibarra Martínez, and Mayra Guadalupe Treviño Berrones. "Pareto Approximation Empirical Results of Energy-Aware Optimization for Precedence-Constrained Task Scheduling Considering Switching off Completely Idle Machines." Mathematics 12, no. 23 (2024): 3733. http://dx.doi.org/10.3390/math12233733.

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Recent advances in cloud computing, large language models, and deep learning have started a race to create massive High-Performance Computing (HPC) centers worldwide. These centers increase in energy consumption proportionally to their computing capabilities; for example, according to the top 500 organization, the HPC centers Frontier, Aurora, and Super Computer Fugaku report energy consumptions of 22,786 kW, 38,698 kW, and 29,899 kW, respectively. Currently, energy-aware scheduling is a topic of interest to many researchers. However, as far as we know, this work is the first approach consider

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Lin, Xiaoyu, Hang Yu, Meng Wang, Chaoen Li, Zi Wang, and Yin Tang. "Electricity Consumption Forecast of High-Rise Office Buildings Based on the Long Short-Term Memory Method." Energies 14, no. 16 (2021): 4785. http://dx.doi.org/10.3390/en14164785.

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Various algorithms predominantly use data-driven methods for forecasting building electricity consumption. Among them, algorithms that use deep learning methods and, long and short-term memory (LSTM) have shown strong prediction accuracy in numerous fields. However, the LSTM algorithm still has certain limitations, e.g., the accuracy of forecasting the building air conditioning power consumption was not very high. To explore ways of improving the prediction accuracy, this study selects a high-rise office building in Shanghai to predict the air conditioning power consumption and lighting power

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Yang, Hua, Jungang Yang, Wendong Zhao, and Cuntao Liu. "On-Demanding Information Acquisition in Multi-UAV-Assisted Sensor Network: A Satisfaction-Driven Perspective." Mathematical Problems in Engineering 2021 (October 13, 2021): 1–14. http://dx.doi.org/10.1155/2021/2717733.

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When multiple heterogeneous unmanned aerial vehicles (UAVs) provide service for multiple users in sensor networks, users’ diverse priorities and corresponding priority-related satisfaction are rarely concerned in traditional task assignment algorithms. A priority-driven user satisfaction model is proposed, in which a piecewise function considering soft time window and users’ different priority levels is designed to describe the relationship between user priority and user satisfaction. On this basis, the multi-UAV task assignment problem is formulated as a combinatorial optimization problem wit

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Yang, Hua, Jungang Yang, Wendong Zhao, and Cuntao Liu. "On-Demanding Information Acquisition in Multi-UAV-Assisted Sensor Network: A Satisfaction-Driven Perspective." Mathematical Problems in Engineering 2021 (October 13, 2021): 1–14. http://dx.doi.org/10.1155/2021/2717733.

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When multiple heterogeneous unmanned aerial vehicles (UAVs) provide service for multiple users in sensor networks, users’ diverse priorities and corresponding priority-related satisfaction are rarely concerned in traditional task assignment algorithms. A priority-driven user satisfaction model is proposed, in which a piecewise function considering soft time window and users’ different priority levels is designed to describe the relationship between user priority and user satisfaction. On this basis, the multi-UAV task assignment problem is formulated as a combinatorial optimization problem wit

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Saroja, S., T. Revathi, and Nitin Auluck. "Multi-Criteria Decision-Making for Heterogeneous Multiprocessor Scheduling." International Journal of Information Technology & Decision Making 17, no. 05 (2018): 1399–427. http://dx.doi.org/10.1142/s0219622018500311.

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This paper proposes a new tri-objective scheduling algorithm called Heterogeneous Reliability-Driven Energy-Efficient Duplication-based (HRDEED) algorithm for heterogeneous multiprocessors. The goal of the algorithm is to minimize the makespan (schedule length) and energy consumption, while maximizing the reliability of the generated schedule. Duplication has been employed in order to minimize the makespan. There is a strong interest among researchers to obtain high-performance schedules that consume less energy. To address this issue, the proposed algorithm incorporates energy consumption as

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Kanoma, Saminu Isah, Bashar Bin Usman, Danlami Gabi, Abubakar Sidiq Nurudeen, and Hassan Umar Suru. "Towards Enhancing Energy Consumption and Time Complexity of Combinatorial Algorithms for Solving the Knapsack Problem." International Journal of Science for Global Sustainability 10, no. 4 (2024): 20–27. https://doi.org/10.57233/ijsgs.v10i4.730.

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The increasing demand for energy-efficient and time-optimized computational systems has driven research into combinatorial algorithms, particularly those used to solve the knapsack problem. The knapsack problem is one of the most significant in combinatorial optimization, which involves determining the optimal selection of items to include in a knapsack while adhering to specific constraints, such as weight or profit limits. This study compares the energy consumption and time complexity of the greedy and dynamic programming algorithms applied to this problem. Using power models to measure tota

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Luo, X. J., and Lukumon O. Oyedele. "Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm." Advanced Engineering Informatics 50 (October 2021): 101357. http://dx.doi.org/10.1016/j.aei.2021.101357.

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Piantkoski, Érico Schardong Rhor, Diógenes Antonio Marques José, and Armando da Silva Filho. "OT-DRIVEN OPTIMIZATION OF SELF-SUFFICIENT SOLAR ENERGY SYSTEMS THROUGH METEOROLOGICAL DATA INTEGRATION." Revista Multidisciplinar do Nordeste Mineiro 10, no. 1 (2025): 1–29. https://doi.org/10.61164/rmnm.v10i1.4031.

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Over two million Brazilians lack access to electricity, as reported by the Brazilian Institute of Geography and Statistics (IBGE). Meanwhile, a significant portion of the population reliant on hydroelectric power faces recurring energy price hikes driven by water scarcity and systemic mismanagement. Decentralized solar energy systems offer a promising alternative; however, their intermittent nature due to seasonal variability can compromise reliability for off-grid users. To address this challenge, this study proposes an IoT-enabled embedded system that integrates meteorological data with a so

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Zhou, Tao, Junjie He, Jintao Shi, et al. "P‐12.21: Automatic Driving Waveform Modification System with Data‐Driven Optimization Algorithm for Electrophoretic Display Device." SID Symposium Digest of Technical Papers 56, S1 (2025): 1577–79. https://doi.org/10.1002/sdtp.19152.

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Electrophoretic Displays (EPDs) have attracted great attention due to the low power consumption and eye‐friendly potential. In this work, we built an automatic driving waveform modification system and implanted a data‐driven optimization algorithm to this system. With this system, we optimized the response time of the EPDs by automatic iterations.

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Kim, Sun-Ho, Young-Ran Yoon, Jeong-Won Kim, and Hyeun-Jun Moon. "Novel Integrated and Optimal Control of Indoor Environmental Devices for Thermal Comfort Using Double Deep Q-Network." Atmosphere 12, no. 5 (2021): 629. http://dx.doi.org/10.3390/atmos12050629.

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Maintaining a pleasant indoor environment with low energy consumption is important for healthy and comfortable living in buildings. In previous studies, we proposed the integrated comfort control (ICC) algorithm, which integrates several indoor environmental control devices, including an air conditioner, a ventilation system, and a humidifier. The ICC algorithm is operated by simple on/off control to maintain indoor temperature and relative humidity within a defined comfort range. This simple control method can cause inefficient building operation because it does not reflect the changes in ind

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Borgato, Nicola, Enrico Prataviera, Sara Bordignon, Roberto Garay-Martinez, and Angelo Zarrella. "A data-driven model for the analysis of energy consumption in buildings." E3S Web of Conferences 523 (2024): 02002. http://dx.doi.org/10.1051/e3sconf/202452302002.

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Data-driven models are gaining traction in Building Energy Simulation, driven by the increasing role of smart metering and control in buildings. This paper aims to enhance the knowledge in this sector by introducing a practical method to analyse heating consumption. The methodology involves the analysis of hourly total heating demand and outdoor temperature measurements to create and calibrate Energy Signature Curves. Importantly, the building Energy Signature Curve is calibrated independently for each daily hour, resulting in a subset of 24 data-driven models. After calibration, a disaggregat

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Hua, Cheng Bing, Zhao Wei, and Chang Zi Nan. "Underwater Acoustic Sensor Networks Deployment Using Improved Self-Organize Map Algorithm." Cybernetics and Information Technologies 14, no. 5 (2014): 63–77. http://dx.doi.org/10.2478/cait-2014-0044.

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Abstract The traditional Self-Organize Map (SOM) method is used for the arrangement of seabed nodes in this paper. If the distance between the nodes and the events is long, these nodes cannot be victory nodes and they will be abandoned, because they cannot move to the direction of events, and as a result they are not being fully utilized and are destroying the balance of energy consumption in the network. Aiming at this problem, this paper proposes an improved self-organize map algorithm with the introduction of the probability-selection mechanism in Gibbs sampling to select victory nodes, thu

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Abedini, Mohamad, and Mohamad Mostafai. "Adaptive energy consumption scheduling of multi-microgrid using whale optimization algorithm." International Journal of Modeling, Simulation, and Scientific Computing 12, no. 05 (2021): 2150036. http://dx.doi.org/10.1142/s1793962321500367.

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In this paper, we investigated the day-ahead scheduling of network consisting of several distributed generation sources and connected loads. This scheduling is an optimization problem that has two objective functions, namely, operating costs and the cost caused by the emission of pollutants. In this scheduling, while introducing a new structure for micro-grid utilization, the possibility of exchanging power among networks and exchanging power between networks and the upstream distribution network is provided. The uncertainty of distributed wind generation sources and solar units has been inves

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Fan, Qinqin, Yaochu Jin, Weili Wang, and Xuefeng Yan. "A performance-driven multi-algorithm selection strategy for energy consumption optimization of sea-rail intermodal transportation." Swarm and Evolutionary Computation 44 (February 2019): 1–17. http://dx.doi.org/10.1016/j.swevo.2018.11.007.

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Meng, Weiping, Yang He, and Yongquan Zhou. "Q-Learning-Driven Butterfly Optimization Algorithm for Green Vehicle Routing Problem Considering Customer Preference." Biomimetics 10, no. 1 (2025): 57. https://doi.org/10.3390/biomimetics10010057.

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This paper proposes a Q-learning-driven butterfly optimization algorithm (QLBOA) by integrating the Q-learning mechanism of reinforcement learning into the butterfly optimization algorithm (BOA). In order to improve the overall optimization ability of the algorithm, enhance the optimization accuracy, and prevent the algorithm from falling into a local optimum, the Gaussian mutation mechanism with dynamic variance was introduced, and the migration mutation mechanism was also used to enhance the population diversity of the algorithm. Eighteen benchmark functions were used to compare the proposed

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Celani, Fabio, Mohsen Heydari, and Alireza Basohbat Novinzadeh. "Model-Free Adaptive Control for Attitude Stabilization of Earth-Pointing Spacecraft Using Magnetorquers." Aerospace 12, no. 3 (2025): 219. https://doi.org/10.3390/aerospace12030219.

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This paper presents an attitude stabilization algorithm for a Low Earth Orbit (LEO) Earth-pointing spacecraft using magnetorquers as the only torque actuators and employing Model-Free Adaptive Control (MFAC) as the control algorithm. MFAC is a data-driven control algorithm that relies solely on input–output data from the plant. This paper validates the effectiveness of the proposed approach through numerical simulations in a specific case study. The simulations show that the proposed algorithm drives the spacecraft’s attitude to three-axis stabilization in the orbital frame from arbitrary init

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Deng, Jiawen, Jie Yang, Xin’an Wang, and Xing Zhang. "A Novel Instruction Driven 1-D CNN Processor for ECG Classification." Sensors 24, no. 13 (2024): 4376. http://dx.doi.org/10.3390/s24134376.

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Electrocardiography (ECG) has emerged as a ubiquitous diagnostic tool for the identification and characterization of diverse cardiovascular pathologies. Wearable health monitoring devices, equipped with on-device biomedical artificial intelligence (AI) processors, have revolutionized the acquisition, analysis, and interpretation of ECG data. However, these systems necessitate AI processors that exhibit flexible configuration, facilitate portability, and demonstrate optimal performance in terms of power consumption and latency for the realization of various functionalities. To address these cha

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Yang, Yongjie, Yulong Li, Yan Cai, Hui Tang, and Peng Xu. "Data-Driven Golden Jackal Optimization–Long Short-Term Memory Short-Term Energy-Consumption Prediction and Optimization System." Energies 17, no. 15 (2024): 3738. http://dx.doi.org/10.3390/en17153738.

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In order to address the issues of significant energy and resource waste, low-energy management efficiency, and high building-maintenance costs in hot-summer and cold-winter regions of China, a research project was conducted on an office building located in Nantong. In this study, a data-driven golden jackal optimization (GJO)-based Long Short-Term Memory (LSTM) short-term energy-consumption prediction and optimization system is proposed. The system creates an equivalent model of the office building and employs the genetic algorithm tool Wallacei to automatically optimize and control the buildi

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Lakshmi, Mettu Jhansi, and Mahesh Babu Arrama. "1177Optimized Energy Utilization in Cognitive Radio Networkswith Congestion Control." International Journal of Intelligent Systems and Applications in Engineering 12, no. 22 (2024): 1177–85. http://dx.doi.org/10.36893/ijisae.2024.v12n22.6644.

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Various real-time applications can be handled through wireless sensor networks, which consist of a wide range of sensor nodes. A novel congestion control mechanism is proposed on optimized rates for energy-efficient transmissions. To reduce energy consumption across the network, a rate-based congestion control algorithm based on cluster routing is presented. By reducing the end-to-end delay, rate control improves the network life time over a large simulation period. Clustering is initially performed using novel routing algorithms. After that, rate control is implemented using an energy optimiz

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Yang, Hao, Maoyu Ran, and Haibo Feng. "Improved Data-Driven Building Daily Energy Consumption Prediction Models Based on Balance Point Temperature." Buildings 13, no. 6 (2023): 1423. http://dx.doi.org/10.3390/buildings13061423.

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The data-driven models have been widely used in building energy analysis due to their outstanding performance. The input variables of the data-driven models are crucial for their predictive performance. Therefore, it is meaningful to explore the input variables that can improve the predictive performance, especially in the context of the global energy crisis. In this study, an algorithm for calculating the balance point temperature was proposed for an apartment community in Xiamen, China. It was found that the balance point temperature label (BPT label) can significantly improve the daily ener

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Waseem, Muhammad, Zhenzhi Lin, and Li Yang. "Data-Driven Load Forecasting of Air Conditioners for Demand Response Using Levenberg–Marquardt Algorithm-Based ANN." Big Data and Cognitive Computing 3, no. 3 (2019): 36. http://dx.doi.org/10.3390/bdcc3030036.

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Air Conditioners (AC) impact in overall electricity consumption in buildings is very high. Therefore, controlling ACs power consumption is a significant factor for demand response. With the advancement in the area of demand side management techniques implementation and smart grid, precise AC load forecasting for electrical utilities and end-users is required. In this paper, big data analysis and its applications in power systems is introduced. After this, various load forecasting categories and various techniques applied for load forecasting in context of big data analysis in power systems hav

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Roman, Raul-Cristian, Radu-Emil Precup, Emil M. Petriu, and Florin Dragan. "Combination of Data-Driven Active Disturbance Rejection and Takagi-Sugeno Fuzzy Control with Experimental Validation on Tower Crane Systems." Energies 12, no. 8 (2019): 1548. http://dx.doi.org/10.3390/en12081548.

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In this paper a second-order data-driven Active Disturbance Rejection Control (ADRC) is merged with a proportional-derivative Takagi-Sugeno Fuzzy (PDTSF) logic controller, resulting in two new control structures referred to as second-order data-driven Active Disturbance Rejection Control combined with Proportional-Derivative Takagi-Sugeno Fuzzy Control (ADRC–PDTSFC). The data-driven ADRC–PDTSFC structure was compared with a data-driven ADRC structure and the control system structures were validated by real-time experiments on a nonlinear Multi Input-Multi Output tower crane system (TCS) labora

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Wang, Lei. "Multisignal Cooperative Processing Method for Internet of Vehicles Based on Data-Driven Edge Computing Method." Security and Communication Networks 2022 (November 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/8412750.

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With the rapid development of Internet of Vehicles applications, more and more data are generated. How to effectively distribute content in the Internet of Vehicles to meet the service quality requirements of users has become one of the industry pain points in the field of smart cars and autonomous driving. In order to solve the shortage of local computing resources of vehicles, a vehicle edge network is proposed, which uses data-driven edge computing to offload vehicle tasks to a mobile edge computing server to reduce overall network energy consumption and meet task latency requirements. In a

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Nazemi, Seyyed Danial, Esmat Zaidan, and Mohsen A. Jafari. "The Impact of Occupancy-Driven Models on Cooling Systems in Commercial Buildings." Energies 14, no. 6 (2021): 1722. http://dx.doi.org/10.3390/en14061722.

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Cooling systems play a key role in maintaining human comfort inside buildings. The key challenges that are facing conventional cooling systems are the rapid growth of total cooling energy and annual electricity consumption in commercial buildings. This is even more significant in countries with an arid climate, where the cooling systems are typically working 80% of the year. Thus, there has been growing interest in developing smart control models to assign optimal cooling setpoints in recent years. In the present work, we propose an occupancy-based control model that is based on a non-linear o

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Su, Moting, Zongyi Zhang, Ye Zhu, and Donglan Zha. "Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm." Energies 12, no. 6 (2019): 1094. http://dx.doi.org/10.3390/en12061094.

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Natural gas is often described as the cleanest fossil fuel. The consumption of natural gas is increasing rapidly. Accurate prediction of natural gas spot prices would significantly benefit energy management, economic development, and environmental conservation. In this study, the least squares regression boosting (LSBoost) algorithm was used for forecasting natural gas spot prices. LSBoost can fit regression ensembles well by minimizing the mean squared error. Henry Hub natural gas spot prices were investigated, and a wide range of time series from January 2001 to December 2017 was selected. T

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Yin, Tiannuo. "Research on Consumer Behavior Patterns and Their Precision Marketing Strategies Based on Decision Tree Algorithms: A Case Study of Superstore Customer Data." Advances in Economics, Management and Political Sciences 201, no. 1 (2025): 53–63. https://doi.org/10.54254/2754-1169/2025.ld25121.

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With the rapid development of data-driven marketing, personalized and precision strategies have become essential tools for enterprises to enhance competitiveness. However, the deep mining of multidimensional consumer data in offline superstore scenarios still faces significant challenges. This study applies a decision tree algorithm to analyze consumer behavior patterns in four product categorieswine, candy, meat, and goldbased on customer consumption data from a supermarket. The findings indicate that income level is the core factor influencing consumption. Family structure also plays a diffe

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Wu, Yanpeng, Ying Wu, Josep Guerrero, Juan Vasquez, Emilio Palacios-García, and Yajuan Guan. "IoT-enabled Microgrid for Intelligent Energy-aware Buildings: A Novel Hierarchical Self-consumption Scheme with Renewables." Electronics 9, no. 4 (2020): 550. http://dx.doi.org/10.3390/electronics9040550.

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This paper presents a novel hierarchical Internet of Things (IoT)-based scheme for Microgrid-Enabled Intelligent Buildings to achieve energy digitalization and automation with a renewable energy self-consumption strategy. Firstly, a hierarchical structure of Microgrid-Enabled Intelligent Buildings is designed to establish a two-dimensional fusion layered architecture for the microgrid to interact with the composite loads of buildings. The building blocks and functions of each layer are defined specifically. Secondly, to achieve transparent information fusion and interactive cooperation between

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Popa, Angela, Alfonso P. Ramallo González, Gaurav Jaglan, and Anna Fensel. "A Semantically Data-Driven Classification Framework for Energy Consumption in Buildings." Energies 15, no. 9 (2022): 3155. http://dx.doi.org/10.3390/en15093155.

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Encouraged by the European Union, all European countries need to enforce solutions to reduce non-renewable energy consumption in buildings. The reduction of energy (heating, domestic hot water, and appliances consumption) aims for the vision of near-zero energy consumption as a requirement goal for constructing buildings. In this paper, we review the available standards, tools and frameworks on the energy performance of buildings. Additionally, this work investigates if energy performance ratings can be obtained with energy consumption data from IoT devices and if the floor size and energy con

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Popa, Angela, Alfonso P. Ramallo González, Gaurav Jaglan, and Anna Fensel. "A Semantically Data-Driven Classification Framework for Energy Consumption in Buildings." Energies 15, no. 9 (2022): 3155. http://dx.doi.org/10.3390/en15093155.

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Encouraged by the European Union, all European countries need to enforce solutions to reduce non-renewable energy consumption in buildings. The reduction of energy (heating, domestic hot water, and appliances consumption) aims for the vision of near-zero energy consumption as a requirement goal for constructing buildings. In this paper, we review the available standards, tools and frameworks on the energy performance of buildings. Additionally, this work investigates if energy performance ratings can be obtained with energy consumption data from IoT devices and if the floor size and energy con

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Wang, Xiaoyu, Dongkun Luo, Jianye Liu, Wenhuan Wang, and Guixin Jie. "Prediction of Natural Gas Consumption in Different Regions of China Using a Hybrid MVO-NNGBM Model." Mathematical Problems in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/6045708.

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The accurate and reasonable prediction of natural gas consumption is significant for the government to formulate energy planning. To this end, we use the multiverse optimizer (MVO) algorithm to optimize the parameters of the Nash nonlinear grey Bernoulli model (NNGBM (1,1)) and propose a hybrid MVO-NNGBM model to predict the natural gas consumption in 30 regions of China. The results indicate that the prediction precision of the hybrid MVO-NNGBM model is better than that of other grey-based models. According to the forecast results, China’s natural gas consumption will grow rapidly over the ne

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Nitin Mishra. "AI-Driven Software Quality Prediction: A Hybrid Deep Learning and Evolutionary Algorithm Approach." Journal of Information Systems Engineering and Management 10, no. 34s (2025): 844–54. https://doi.org/10.52783/jisem.v10i34s.5874.

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Objectives: This study aims to enhance software quality prediction by addressing the limitations of traditional machine learning models—namely, feature redundancy and high computational costs—through a hybrid deep learning approach integrated with Genetic Algorithms (GA) for efficient and accurate defect prediction. Methods:The proposed method combines Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs) for deep feature learning, alongside GA for optimal feature selection. Model pruning and quantization techniques were employed to improve computational efficiency. The hybrid

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Al-Sammak, Kanar Alaa, Sama Hussein Al-Gburi, Ion Marghescu, et al. "Optimizing IoT Energy Efficiency: Real-Time Adaptive Algorithms for Smart Meters with LoRaWAN and NB-IoT." Energies 18, no. 4 (2025): 987. https://doi.org/10.3390/en18040987.

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Real-time monitoring, data-driven decisions, and energy consumption optimization have reached a new level with IoT advancement. However, a significant challenge faced by intelligent nodes and IoT applications resides in their energy requirements in the long term, especially in the case of gas or water smart meters. This article proposes an algorithm for smart meters’ energy consumption optimization based on IoT, LoRaWAN, and NB-IoT using microcontroller-based development boards, PZEM004T energy meters, Dragino LoRaWAN shield, or BG96 NB-IoT modules. The algorithm adjusts the transmission time

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Nassef, Omar, Toktam Mahmoodi, Foivos Michelinakis, Kashif Mahmood, and Ahmed Elmokashfi. "Optimising Performance for NB-IoT UE Devices through Data Driven Models." Journal of Sensor and Actuator Networks 10, no. 1 (2021): 21. http://dx.doi.org/10.3390/jsan10010021.

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This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Suggested configurations are delivered from a Configuration Advocate, to improve energy consumption, delay, throughput or a combination of those metrics, depending on the user-end device and the application. Reinforcement learning utilising gradient descent and genetic algorithm is adopted synchronously with machine and deep learning algorithms to pre

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Gavinda, Virza, Nurdin Nurdin, and Fajriana Fajriana. "Predicting Electricity Consumption in Aceh Province Using the Markov Chain Monte Carlo Method." International Journal of Engineering, Science and Information Technology 5, no. 1 (2024): 128–35. https://doi.org/10.52088/ijesty.v5i1.678.

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Electricity is essential to nearly every aspect of modern life, from industrial sectors to household needs. In Aceh Province, the demand for electricity has consistently increased along with economic growth, urbanization, and population expansion. Various studies indicate that rising electricity consumption is closely linked to economic growth and industrialization. This study uses the Markov Chain Monte Carlo (MCMC) method with the Metropolis-Hastings algorithm to predict electricity consumption in Aceh Province. The research addresses the significant increase in electricity consumption drive

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Balać, Nebojša, Zoran Mileusnić, Aleksandra Dragičević, et al. "Implementation of XGBoost Models for Predicting CO2 Emission and Specific Tractor Fuel Consumption." Agriculture 15, no. 11 (2025): 1209. https://doi.org/10.3390/agriculture15111209.

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Tillage is one of the most energy-intensive operations in crop production, leading to high fuel consumption and the emission of harmful gases such as CO2 and NOx. This study was conducted under real field conditions to explore how soil parameters influence variations in fuel use and exhaust emissions. A machine learning approach based on the XGBoost algorithm was applied to develop predictive models for CO2 concentrations in exhaust gases and specific fuel consumption. The CO2 prediction model achieved an accuracy exceeding 80%, while the model for fuel consumption reached over 65%. Although n

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Athanasiadis, Christos, Dimitrios Doukas, Theofilos Papadopoulos, and Antonios Chrysopoulos. "A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption." Energies 14, no. 3 (2021): 767. http://dx.doi.org/10.3390/en14030767.

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Smart-meter technology advancements have resulted in the generation of massive volumes of information introducing new opportunities for energy services and data-driven business models. One such service is non-intrusive load monitoring (NILM). NILM is a process to break down the electricity consumption on an appliance level by analyzing the total aggregated data measurements monitored from a single point. Most prominent existing solutions use deep learning techniques resulting in models with millions of parameters and a high computational burden. Some of these solutions use the turn-on transien

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Lee, Sangyoon, and Dae-Hyun Choi. "Reinforcement Learning-Based Energy Management of Smart Home with Rooftop Solar Photovoltaic System, Energy Storage System, and Home Appliances." Sensors 19, no. 18 (2019): 3937. http://dx.doi.org/10.3390/s19183937.

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This paper presents a data-driven approach that leverages reinforcement learning to manage the optimal energy consumption of a smart home with a rooftop solar photovoltaic system, energy storage system, and smart home appliances. Compared to existing model-based optimization methods for home energy management systems, the novelty of the proposed approach is as follows: (1) a model-free Q-learning method is applied to energy consumption scheduling for an individual controllable home appliance (air conditioner or washing machine), as well as the energy storage system charging and discharging, an

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