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Energy-saving optimization of air-conditioning water system based on machine learning and improved bat algorithm

ENERGY AND BUILDINGS 2025年 332卷

作者： Bai, Yan Sun, Di Liu, Liang Shen, Yifan Yao, Haitong Xian Univ Architecture & Technol Sch Sci 13 Yanta Rd Xian Shaanxi Peoples R China Xian Univ Architecture & Technol Sch Informat & Control Engn Xian 710055 Peoples R China Anhui Jianzhu Univ Anhui Prov Key Lab Intelligent Bldg Energy Saving Hefei 230022 Peoples R China

A significant portion of the total energy consumption in buildings comes from the air-conditioning water systems (ACWSs) used for cooling. The setpoints of ACWSs are usually determined based on experience or rules to meet cooling demands, but they may not be optimal under dynamic operating conditions. Therefore, to reduce energy consumption by dynamically adjusting the setpoints of ACWS, an energy-saving method of ACWS based on machine learning and improved bat algorithm (IBA) is proposed. Firstly, machine learning techniques are employed to develop models for component-level and system-level energy consumption, as well as chilled water and cooling water return temperature in the ACWS. Their hyperparameters are optimized by ten-fold crossvalidation and grid search. Secondly, the bat algorithm (BA) is improved by combining shared grouping strategy, nonlinear factor, and Levy flight strategy with adjustable balancing factor. Thirdly, the developed optimal energy consumption model is used as a fitness calculation function for the IBA, while the developed optimal return water temperature model is employed to predict return water temperature of the constrain conditions in the process of optimization. Finally, a case study was executed using a measured dataset of an ACWS. The results illustration that the energy consumption model based on the system-level has higher accuracy, robustness, and generalization ability than based on the component-level, and the total energy consumption of the system is reduced by 7.84% after optimization. Meanwhile, compared with the other optimization algorithms, the IBA has better optimization results with significant advantages in robustness, iteration times, global search capability, and computation time.

关键词： Air-conditioning water system Energy-saving optimization Machine learning improved bat algorithm

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Optimal Scheduling Method of Combined Wind-Photovoltaic-Pumped Storage System Based on improved bat algorithm

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PROCESSES 2025年第1期13卷

作者： Fan, Hui Wu, Hongbo Li, Shilin Han, Shengfeng Ren, Jingtao Huang, Shuo Zou, Hongbo State Grid Hebei Elect Power Co Ltd Shijiazhuang 050081 Peoples R China State Grid Xingtai Power Supply Co Ltd Xingtai 054001 Peoples R China Wuhan Univ Hubei Engn & Technol Res Ctr AC DC Intelligent Dis Sch Elect Engn & Automat Wuhan 430072 Peoples R China China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China

Pumped storage power stations not only serve as a special power load but also store excess electricity from the power system, significantly reducing the curtailment of wind and solar power. This dual function ensures the stable operation of the power grid and enhances its economic benefits. The scheduling optimization problem of a combined wind-solar-pumped storage system is addressed in this study, and an optimization scheduling model is proposed with the objective of maximizing total system revenue. The model is designed to comprehensively account for the generation revenues from wind power, photovoltaic power, thermal power, and pumped storage, as well as the penalty costs associated with pollutant emissions. To address the limitations of traditional algorithms, which are prone to being trapped in local optima and exhibit slow convergence, an improved bat algorithm was developed. The algorithm is enhanced through the use of chaotic mapping to expand the initial solution space, the incorporation of adaptive step-size updates to improve convergence efficiency, and the integration of the Cauchy function to strengthen global search capabilities, thereby effectively avoiding local optima. Simulation results have demonstrated that the improved algorithm achieves significant improvements over traditional bat algorithms and particle swarm optimization (PSO) in terms of optimization efficiency, with total revenue increases of 21.9% and 24.6%, respectively. The optimized scheduling plan is shown to fully utilize the flexible regulation capabilities of pumped storage, mitigating the adverse effects of wind and photovoltaic output fluctuations on grid operations and achieving a balanced trade-off between economic and environmental objectives.

关键词： renewable energy systems wind-photovoltaic-pumped storage optimal scheduling method improved bat algorithm

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improved bat algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

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Computers, Materials & Continua 2023年第2期74卷 3151-3166页

作者： Marwa Obayya Nadhem NEMRI Lubna A.Alharbi Mohamed K.Nour Mrim M.Alnfiai Mohammed Abdullah Al-Hagery Nermin M.Salem Mesfer Al Duhayyim Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science and Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has *** evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based *** Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in *** to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from *** such scenario computer-assisted automated diagnostic tools are important for classification of ECG *** current study devises an improved bat algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)*** accomplish this,the proposed IBADL-BECGC model initially pre-processes the input ***,IBADL-BECGC model applies NasNet model to derive the features from test ECG *** addition,improved bat algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet ***,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification *** presented IBADL-BECGC model was experimentally validated utilizing benchmark *** comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

关键词： Data science ECG signals improved bat algorithm deep learning biomedical data data classification machine learning

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improved bat algorithm for UAV Path Planning in Three-Dimensional Space

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IEEE ACCESS 2021年 9卷 20100-20116页

作者： Zhou, Xianjin Gao, Fei Fang, Xi Lan, Zehong Wuhan Univ Technol Sch Sci Wuhan 43007 Peoples R China Wuhan Univ Sch Econ & Management Wuhan 430072 Peoples R China

This paper describes the flight path planning for unmanned aerial vehicles (UAVs) based on the advanced swarm optimization algorithm of the bat algorithm (BA) in a static environment. The main purpose of this work is that the UAVs can obtain an accident-free, shorter, and safer flight path between the starting point and the endpoint in the complex three-dimensional battlefield environment. Based on the characteristics of the standard BA and the artificial bee colony algorithm (ABC), a new modification of the BA algorithm is proposed in this work, namely, the improved bat algorithm integrated into the ABC algorithm (IBA). The IBA mainly uses ABC to modify the BA and solves the problem of poor local search ability of the BA. This article demonstrates the convergence of the IBA and performs simulations in MATLAB environment to verify its effectiveness. The simulations showed that the time required for the IBA to obtain the optimum solution is approximately 50% lower than the BA, and that the quality of the optimum solution is about 14% higher than the ABC. Furthermore, by comparing with other traditional and improved swarm intelligent path planning algorithms, the IBA can plan a faster, shorter, safer, accident-free flight path for UAVs. Finally, this article proves that IBA also has good performance in optimizing functions and has broad application potential.

关键词： Path planning Optimization Heuristic algorithms Planning Convergence Radar Particle swarm optimization battlefield environment path planning improved bat algorithm convergence local search

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improved bat algorithm for the detection of myocardial infarction

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SPRINGERPLUS 2015年第1期4卷 1-18页

作者： Kora, Padmavathi Kalva, Ramakrishna GRIET Dept ECE Hyderabad 500090 Andhra Pradesh India Velagapudi Ramakrishna Siddhartha Engn Coll Dept ECE Kanuru Vijayawada India

The medical practitioners study the electrical activity of the human heart in order to detect heart diseases from the electrocardiogram (ECG) of the heart patients. A myocardial infarction (MI) or heart attack is a heart disease, that occurs when there is a block (blood clot) in the pathway of one or more coronary blood vessels (arteries) that supply blood to the heart muscle. The abnormalities in the heart can be identified by the changes in the ECG signal. The first step in the detection of MI is Preprocessing of ECGs which removes noise by using filters. Feature extraction is the next key process in detecting the changes in the ECG signals. This paper presents a method for extracting key features from each cardiac beat using improved bat algorithm. Using this algorithm best features are extracted, then these best (reduced) features are applied to the input of the neural network classifier. It has been observed that the performance of the classifier is improved with the help of the optimized features.

关键词： Myocardial infarction ECG improved bat algorithm Neural network classifier

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improved bat algorithm for structural reliability assessment: application and challenges

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MULTIDISCIPLINE MODELING IN MATERIALS AND STRUCTURES 2016年第2期12卷 218-253页

作者： Chakri, Asma Khelif, Rabia Benouaret, Mohamed Badji Mokhtar Univ UBMA Dept Mech Engn Ind Mech Lab Annaba Algeria

Purpose - The first order reliability method requires optimization algorithms to find the minimum distance from the origin to the limit state surface in the normal space. The purpose of this paper is to develop an improved version of the new metaheuristic algorithm inspired from echolocation behaviour of bats, namely, the bat algorithm (BA) dedicated to perform structural reliability analysis. Design/methodology/approach - Modifications have been embedded to the standard BA to enhance its efficiency, robustness and reliability. In addition, a new adaptive penalty equation dedicated to solve the problem of the determination of the reliability index and a proposition on the limit state formulation are presented. Findings - The comparisons between the improved bat algorithm (iBA) presented in this paper and other standard algorithms on benchmark functions show that the iBA is highly efficient, and the application to structural reliability problems such as the reliability analysis of overhead crane girder proves that results obtained with iBA are highly reliable. Originality/value - A new iBA and an adaptive penalty equation for handling equality constraint are developed to determine the reliability index. In addition, the low computing time and the ease implementation of this method present great advantages from the engineering viewpoint.

关键词： Metaheuristic Adaptive penalty equation FORM improved bat algorithm Most probable point Reliability index

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Optimal sizing of battery energy storage for micro-grid operation management using a new improved bat algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 56卷 42-54页

作者： Bahmani-Firouzi, Bahman Azizipanah-Abarghooee, Rasoul Islamic Azad Univ Dept Elect Engn Marvdasht Branch Marvdasht Iran

In recent years, due to large integration of Renewable Energy Sources (RESs) like wind turbine and photovoltaic unit into the Micro-Grid (MG), the necessity of battery Energy Storage (BES) has increased dramatically. The BES has several benefits and advantages in the MG-based applications such as short term power supply, power quality improvement, facilitating integration of RES, ancillary service and arbitrage. This paper presents the cost-based formulation to determine the optimal size of the BES in the operation management of MG. Also, some restrictions, i.e. power capacity of Distributed Generators (DGs), power and energy capacity of BES, charge/discharge efficiency of BES, operating reserve and load demand satisfaction should be considered as well. The suggested problem is a complicated optimization problem, the complexity of which is increased by considering the above constraints. Therefore, a robust and strong optimization algorithm is required to solve it. Herein, this paper proposes a new evolutionary technique named improved bat algorithm that is used for developing corrective strategies and to perform least cost dispatches. The performance of the approach is evaluated by one grid-connected low voltage MG where the optimal size of BES is determined professionally. (c) 2013 Elsevier Ltd. All rights reserved.

关键词： battery energy storage sizing Distributed generation improved bat algorithm Micro-grid Operation management Renewable energy sources

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Cleaning decision model of MBR membrane based on Bandelet neural network optimized by improved bat algorithm

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APPLIED SOFT COMPUTING 2020年 91卷

作者： Zhao, Bin Chen, Hao Gao, Diankui Xu, Lizhi Zhang, Yuanyuan Liaoning Shihua Univ Sch Mech Engn Fushun 113001 Liaoning Peoples R China

The membrane fouling is an important factor of restricting wide application of MBR (Membrane Bio-Reactor), which causes the fall of membrane flux and reduces the membrane cleaning period. So the Bandelet neural network is proposed through combining Bandelet transform and neural network, which predicts membrane flux and its recovery rate for making proper membrane cleaning decision. Firstly, the main affecting factors of membrane fouling are discussed. Secondly, the architecture of Bandelet neural network is designed with Bandelet function and its scale function as activation functions of hidden and output layers respectively. Thirdly, the improved bat algorithm is established, which is applied to improve the optimization effect of parameters of Bandelet neural network. Finally, the simulation analysis is carried out, the improved bat algorithm has higher performance than the traditional bat algorithm through analyzing the single objective optimization problem from 2018 CEC competition, the optimal number of nodes in hidden layer is confirmed based on comparison analysis and statistical tests. The proposed BNN-IBA has obvious superiority in prediction accuracy and speed according to prediction simulation results of membrane fouling of MBR, which has better prediction results than other state-of-art prediction models optimized by the novel optimal algorithms. In addition, the proper membrane cleaning period and method are confirmed according to the prediction results of membrane flux and its recovery rate. (C) 2020 Elsevier B.V. All rights reserved.

关键词： MBR Membrane cleaning Bandelet neural network improved bat algorithm Prediction

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Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm

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ENERGY CONVERSION AND MANAGEMENT 2017年 143卷 410-430页

作者： Xiao, Liye Qian, Feng Shao, Wei Univ Elect Sci & Technol China Sch Phys Elect Chengdu 610054 Peoples R China Peking Univ Dept Elect Engn & Comp Sci Beijing Peoples R China

As one of the most promising sustainable energy sources, wind energy plays an important role in energy development because of its cleanliness without causing pollution. Generally, wind speed forecasting, which has an essential influence on wind power systems, is regarded as a challenging task. Analyses based on single-step wind speed forecasting have been widely used, but their results are insufficient in ensuring the reliability and controllability of wind power systems. In this paper, a new forecasting architecture based on decomposing algorithms and modified neural networks is successfully developed for multi-step wind speed forecasting. Four different hybrid models are contained in this architecture, and to further improve the forecasting performance, a modified bat algorithm (BA) with the conjugate gradient (CG) method is developed to optimize the initial weights between layers and thresholds of the hidden layer of neural networks. To investigate the forecasting abilities of the four models, the wind speed data collected from four different wind power stations in Penglai, China, were used as a case study. The numerical experiments showed that the hybrid model including the singular spectrum analysis and general regression neural network with CG-BA (SSA-CG-BA-GRNN) achieved the most accurate forecasting results in one-step to three-step wind speed forecasting. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Hybrid forecasting architecture improved bat algorithm Singular spectrum analysis, wind speed forecasting

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improved bat algorithm Based Energy Efficient Congestion Control Scheme for Wireless Sensor Networks

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Wireless Sensor Network 2016年第11期8卷 229-241页

作者： Mukhdeep Singh Manshahia Mayank Dave Satya Bir Singh Department of Mathematics Punjabi University Patiala India Department of Computer Engineering NIT Kurukshetra India

Energy conservation and congestion control are widely researched topics in Wireless Sensor Networks in recent years. The main objective is to develop a model to find the optimized path on the basis of distance between source and destination and the residual energy of the node. This paper shows an implementation of nature inspired improved bat algorithm to control congestion in Wireless Sensor Networks at transport layer. The algorithm has been applied on the fitness function to obtain an optimum solution. Simulation results have shown improvement in parameters like network lifetime and throughput as compared with CODA (Congestion Detection and Avoidance), PSO (Particle Swarm Optimization) algorithm and ACO (Ant Colony Optimization).

关键词： improved bat algorithm Congestion Control Wireless Sensor Networks

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