With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting m...With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is proposed.The algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM model.Secondly,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model.Finally,the correlation between wind power prediction errors and Numerical Weather Prediction(NWP)data is introduced as incremental updates to the initial wind power prediction model.During the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power models.Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate.The accuracy and precision of wind power generation prediction are effectively improved through the method.展开更多
In this paper, we propose two weighted learning methods for the construction of single hidden layer feedforward neural networks. Both methods incorporate weighted least squares. Our idea is to allow the training insta...In this paper, we propose two weighted learning methods for the construction of single hidden layer feedforward neural networks. Both methods incorporate weighted least squares. Our idea is to allow the training instances nearer to the query to offer bigger contributions to the estimated output. By minimizing the weighted mean square error function, optimal networks can be obtained. The results of a number of experiments demonstrate the effectiveness of our proposed methods.展开更多
Wireless Sensor Network is considered as the intermediate layer in the paradigm of Internet of things(IoT)and its effectiveness depends on the mode of deployment without sacrificing the performance and energy efficien...Wireless Sensor Network is considered as the intermediate layer in the paradigm of Internet of things(IoT)and its effectiveness depends on the mode of deployment without sacrificing the performance and energy efficiency.WSN provides ubiquitous access to location,the status of different entities of the environment and data acquisition for long term IoT monitoring.Achieving the high performance of the WSN-IoT network remains to be a real challenge since the deployment of these networks in the large area consumes more power which in turn degrades the performance of the networks.So,developing the robust and QoS(quality of services)aware energy-efficient routing protocol for WSN assisted IoT devices needs its brighter light of research to enhance the network lifetime.This paper proposed a Hybrid Energy Efficient Learning Protocol(HELP).The proposed protocol leverages the multi-tier adaptive framework to minimize energy consumption.HELP works in a two-tier mechanism in which it integrates the powerful Extreme Learning Machines for clustering framework and employs the zonal based optimization technique which works on hybrid Whale-dragonfly algorithms to achieve high QoS parameters.The proposed framework uses the sub-area division algorithm to divide the network area into different zones.Extreme learning machines(ELM)which are employed in this framework categories the Zone’s Cluster Head(ZCH)based on distance and energy.After categorizing the zone’s cluster head,the optimal routing path for an energy-efficient data transfer will be selected based on the new hybrid whale-swarm algorithms.The extensive simulations were carried out using OMNET++-Python userdefined plugins by injecting the dynamic mobility models in networks to make it a more realistic environment.Furthermore,the effectiveness of the proposed HELP is examined against the existing protocols such as LEACH,M-LEACH,SEP,EACRP and SEEP and results show the proposed framework has outperformed other techniques in terms of QoS parameters such as network lifetime,energy,latency.展开更多
基金funded by Liaoning Provincial Department of Science and Technology(2023JH2/101600058)。
文摘With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is proposed.The algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM model.Secondly,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model.Finally,the correlation between wind power prediction errors and Numerical Weather Prediction(NWP)data is introduced as incremental updates to the initial wind power prediction model.During the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power models.Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate.The accuracy and precision of wind power generation prediction are effectively improved through the method.
基金supported by the NSC under Grant No.NSC-100-2221-E-110-083-MY3 and NSC-101-2622-E-110-011-CC3"Aim for the Top University Plan"of the National Sun-Yat-Sen University and Ministry of Education
文摘In this paper, we propose two weighted learning methods for the construction of single hidden layer feedforward neural networks. Both methods incorporate weighted least squares. Our idea is to allow the training instances nearer to the query to offer bigger contributions to the estimated output. By minimizing the weighted mean square error function, optimal networks can be obtained. The results of a number of experiments demonstrate the effectiveness of our proposed methods.
文摘Wireless Sensor Network is considered as the intermediate layer in the paradigm of Internet of things(IoT)and its effectiveness depends on the mode of deployment without sacrificing the performance and energy efficiency.WSN provides ubiquitous access to location,the status of different entities of the environment and data acquisition for long term IoT monitoring.Achieving the high performance of the WSN-IoT network remains to be a real challenge since the deployment of these networks in the large area consumes more power which in turn degrades the performance of the networks.So,developing the robust and QoS(quality of services)aware energy-efficient routing protocol for WSN assisted IoT devices needs its brighter light of research to enhance the network lifetime.This paper proposed a Hybrid Energy Efficient Learning Protocol(HELP).The proposed protocol leverages the multi-tier adaptive framework to minimize energy consumption.HELP works in a two-tier mechanism in which it integrates the powerful Extreme Learning Machines for clustering framework and employs the zonal based optimization technique which works on hybrid Whale-dragonfly algorithms to achieve high QoS parameters.The proposed framework uses the sub-area division algorithm to divide the network area into different zones.Extreme learning machines(ELM)which are employed in this framework categories the Zone’s Cluster Head(ZCH)based on distance and energy.After categorizing the zone’s cluster head,the optimal routing path for an energy-efficient data transfer will be selected based on the new hybrid whale-swarm algorithms.The extensive simulations were carried out using OMNET++-Python userdefined plugins by injecting the dynamic mobility models in networks to make it a more realistic environment.Furthermore,the effectiveness of the proposed HELP is examined against the existing protocols such as LEACH,M-LEACH,SEP,EACRP and SEEP and results show the proposed framework has outperformed other techniques in terms of QoS parameters such as network lifetime,energy,latency.
