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chicken swarm optimization with Deep Learning Based Packaged Rooftop Units Fault Diagnosis Model

Computer Systems Science & Engineering 2023年第10期47卷 221-238页

作者： G.Anitha N.Supriya Fayadh Alenezi E.Laxmi Lydia Gyanendra Prasad Joshi Jinsang You Department of Electronics and Instrumentation Engineering B.S.Abdur Rahman Crescent Institute of Science and TechnologyChennai600048India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Seculayer Company Ltd.Seoul04784Korea

Rooftop units(RTUs)were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive.Fault detection and diagnosis(FDD)tools can be employed for RTU methods to ensure essential faults are addressed promptly.In this aspect,this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units(ODBNFDC-PRTU)model.The ODBNFDC-PRTU technique considers fault diagnosis as amulti-class classification problem and is handled usingDL models.For fault diagnosis in RTUs,the ODBNFDC-PRTU model exploits the deep belief network(DBN)classification model,which identifies seven distinct types of faults.At the same time,the chicken swarm optimization(CSO)algorithm-based hyperparameter tuning technique is utilized for resolving the trial and error hyperparameter selection process,showing the novelty of the work.To illustrate the enhanced performance of the ODBNFDC-PRTU algorithm,a comprehensive set of simulations are applied.The comparison study described the improvement of the ODBNFDC-PRTU method over other recent FDD algorithms with maximum accuracy of 99.30%and TPR of 93.09%.

关键词： Rooftop units chicken swarm optimization hyperparameter metaheuristics deep learning fault diagnosis

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Wind Speed Prediction Using chicken swarm optimization with Deep Learning Model

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Computer Systems Science & Engineering 2023年第9期46卷 3371-3386页

作者： R.Surendran Youseef Alotaibi Ahmad F.Subahi Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiIndia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia

High precision and reliable wind speed forecasting have become a challenge for meteorologists.Convective events,namely,strong winds,thunderstorms,and tornadoes,along with large hail,are natural calamities that disturb daily life.For accurate prediction of wind speed and overcoming its uncertainty of change,several prediction approaches have been presented over the last few decades.As wind speed series have higher volatility and nonlinearity,it is urgent to present cutting-edge artificial intelligence(AI)technology.In this aspect,this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning(IWSP-CSODL)method.The presented IWSP-CSODL model estimates the wind speed using a hybrid deep learning and hyperparameter optimizer.In the presented IWSP-CSODL model,the prediction process is performed via a convolutional neural network(CNN)based long short-term memory with autoencoder(CBLSTMAE)model.To optimally modify the hyperparameters related to the CBLSTMAE model,the chicken swarm optimization(CSO)algorithm is utilized and thereby reduces the mean square error(MSE).The experimental validation of the IWSP-CSODL model is tested using wind series data under three distinct scenarios.The comparative study pointed out the better outcomes of the IWSP-CSODL model over other recent wind speed prediction models.

关键词： Weather wind speed predictive model chicken swarm optimization hybrid deep learning

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Application research of compressed-air energy storage under high proportion of renewable energy

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Clean Energy 2022年第2期6卷 305-312页

作者： Bin Feng Bo Yu State Grid Ningxia Electric Power Co.ltd YinchuanNingxiaChina State Grid Ningxia Economic and Technological Research Institute YinchuanNingxiaChina

China will strive to achieve a‘dual carbon’target:‘carbon peak’by 2030 and‘carbon-neutral’by 2060.In this context,improving the efficiency of renewable energy and reducing the use of thermal power are important ways to achieve the target.Clean,efficient and large-capacity energy-storage technology is the key to improving the utilization rate of renewable energy.First,this paper proposes to use compressed-air energy-storage technology instead of the old energy-storage technology to build an economical and environmentally friendly comprehensive energy park capacity optimization configuration model.Second,this paper uses the newly proposed improved chicken swarm optimization algorithm to solve the model,which is more accurate and faster.Finally,this paper analyzes a comprehensive energy park in north-west China.Through case analysis,it can be seen that the average utilization rate of renewable energy can reach 73.87%through the model proposed in this paper,while the average power-abandonment rate is only 9.32%.

关键词： renewable energy compressed-air energy storage power-abandonment rate chicken swarm optimization

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Genetic-chicken swarm Algorithm for Minimizing Energy in Wireless Sensor Network

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Computer Systems Science & Engineering 2023年第2期44卷 1451-1466页

作者： A.Jameer Basha S.Aswini S.Aarthini Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of ComputationalMathematics Science and Engineering(CMSE) Michigan State University East LansingMI48824USA.

Wireless Sensor Network(WSN)technology is the real-time applica-tion that is growing rapidly as the result of smart environments.Battery power is one of the most signiﬁcant resources in WSN.For enhancing a power factor,the clustering techniques are used.During the forward of data in WSN,more power is consumed.In the existing system,it works with Load Balanced Cluster-ing Method(LBCM)and provides the lifespan of the network with scalability and reliability.In the existing system,it does not deal with end-to-end delay and deliv-ery of packets.For overcoming these issues in WSN,the proposed Genetic Algo-rithm based on chicken swarm optimization(GA-CSO)with Load Balanced Clustering Method(LBCM)is used.Genetic Algorithm generates chromosomes in an arbitrary method then the chromosomes values are calculated using Fitness Function.chicken swarm optimization(CSO)helps to solve the complex opti-mization problems.Also,it consists of chickens,hens,and rooster.It divides the chicken into clusters.Load Balanced Clustering Method(LBCM)maintains the energy during communication among the sensor nodes and also it balances the load in the gateways.The proposed GA-CSO with LBCM improves the life-span of the network.Moreover,it minimizes the energy consumption and also bal-ances the load over the network.The proposed method outperforms by using the following metrics such as energy efﬁciency,ratio of packet delivery,throughput of the network,lifetime of the sensor nodes.Therefore,the evaluation result shows the energy efﬁciency that has achieved 83.56%and the delivery ratio of the packet has reached 99.12%.Also,it has attained linear standard deviation and reduced the end-to-end delay as 97.32 ms.

关键词： Energy efﬁciency sensor nodes chicken swarm optimization load balanced clustering method wireless sensor network cluster heads load-balancing ﬁtness function

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