In the real world, the inputs of many complicated systems are time-varying functions or processes. In order to predict the outputs of these systems with high speed and accuracy, this paper proposes a time series predi...In the real world, the inputs of many complicated systems are time-varying functions or processes. In order to predict the outputs of these systems with high speed and accuracy, this paper proposes a time series prediction model based on the wavelet process neural network, and develops the corresponding learning algorithm based on the expansion of the orthogonal basis functions. The effectiveness of the proposed time series prediction model and its learning algorithm is proved by the Macke-Glass time series prediction, and the comparative prediction results indicate that the proposed time series prediction model based on the wavelet process neural network seems to perform well and appears suitable for using as a good tool to predict the highly complex nonlinear time series.展开更多
The simulation of wind power time series is a key process in renewable power allocation planning,operation mode calculation,and safety assessment.Traditional single-point modeling methods discretely generate wind powe...The simulation of wind power time series is a key process in renewable power allocation planning,operation mode calculation,and safety assessment.Traditional single-point modeling methods discretely generate wind power at each moment;however,they ignore the daily output characteristics and are unable to consider both modeling accuracy and efficiency.To resolve this problem,a wind power time series simulation model based on typical daily output processes and Markov algorithm is proposed.First,a typical daily output process classification method based on time series similarity and modified K-means clustering algorithm is presented.Second,considering the typical daily output processes as status variables,a wind power time series simulation model based on Markov algorithm is constructed.Finally,a case is analyzed based on the measured data of a wind farm in China.The proposed model is then compared with traditional methods to verify its effectiveness and applicability.The comparison results indicate that the statistical characteristics,probability distributions,and autocorrelation characteristics of the wind power time series generated by the proposed model are better than those of the traditional methods.Moreover,modeling efficiency considerably improves.展开更多
The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It au...The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. （2） An effective sparse hard-cut expec- tation maximization （SHC-EM） learning algorithm for the GPM model is proposed to improve the prediction performance. SHO-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval SHC-EM outperforms the traditional variational 1earning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning.展开更多
High-efficiency perovskite solar cells（PSCs） reported hitherto have been mostly prepared in a moisture and oxygen-free glove-box atmosphere, which hampers upscaling and real-time performance assessment of this excit...High-efficiency perovskite solar cells（PSCs） reported hitherto have been mostly prepared in a moisture and oxygen-free glove-box atmosphere, which hampers upscaling and real-time performance assessment of this exciting photovoltaic technology. In this work, we have systematically studied the feasibility of allambient-processing of PSCs and evaluated their photovoltaic performance. It has been shown that phasepure crystalline tetragonal MAPbI;perovskite films are instantly formed in ambient air at room temperature by a two-step spin coating process, undermining the need for dry atmosphere and post-annealing.All-ambient-processed PSCs with a configuration of FTO/TiO;/MAPbI;/Spiro-OMeTAD/Au achieve opencircuit voltage（990 mV） and short-circuit current density（20.31 mA/cm;） comparable to those of best reported glove-box processed devices. Nevertheless, device power conversion efficiency is still constrained at 5% by the unusually low fill-factor of 0.25. Dark current–voltage characteristics reveal poor conductivity of hole-transporting layer caused by lack of oxidized spiro-OMe TAD species, resulting in high seriesresistance and decreased fill-factor. The study also establishes that the above limitations can be readily overcome by employing an inorganic p-type semiconductor, copper thiocyanate, as ambient-processable hole-transporting layer to yield a fill-factor of 0.54 and a power conversion efficiency of 7.19%. The present findings can have important implications in industrially viable fabrication of large-area PSCs.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No 60572174)the Doctoral Fund of Ministry of Education of China (Grant No 20070213072)+2 种基金the 111 Project (Grant No B07018)the China Postdoctoral Science Foundation (Grant No 20070410264)the Development Program for Outstanding Young Teachers in Harbin Institute of Technology (Grant No HITQNJS.2007.010)
文摘In the real world, the inputs of many complicated systems are time-varying functions or processes. In order to predict the outputs of these systems with high speed and accuracy, this paper proposes a time series prediction model based on the wavelet process neural network, and develops the corresponding learning algorithm based on the expansion of the orthogonal basis functions. The effectiveness of the proposed time series prediction model and its learning algorithm is proved by the Macke-Glass time series prediction, and the comparative prediction results indicate that the proposed time series prediction model based on the wavelet process neural network seems to perform well and appears suitable for using as a good tool to predict the highly complex nonlinear time series.
基金supported by the China Datang Corporation project“Study on the performance improvement scheme of in-service wind farms”,the Fundamental Research Funds for the Central Universities(2020MS021)the Foundation of State Key Laboratory“Real-time prediction of offshore wind power and load reduction control method”(LAPS2020-07).
文摘The simulation of wind power time series is a key process in renewable power allocation planning,operation mode calculation,and safety assessment.Traditional single-point modeling methods discretely generate wind power at each moment;however,they ignore the daily output characteristics and are unable to consider both modeling accuracy and efficiency.To resolve this problem,a wind power time series simulation model based on typical daily output processes and Markov algorithm is proposed.First,a typical daily output process classification method based on time series similarity and modified K-means clustering algorithm is presented.Second,considering the typical daily output processes as status variables,a wind power time series simulation model based on Markov algorithm is constructed.Finally,a case is analyzed based on the measured data of a wind farm in China.The proposed model is then compared with traditional methods to verify its effectiveness and applicability.The comparison results indicate that the statistical characteristics,probability distributions,and autocorrelation characteristics of the wind power time series generated by the proposed model are better than those of the traditional methods.Moreover,modeling efficiency considerably improves.
基金Supported by the National Natural Science Foundation of China under Grant No 60972106the China Postdoctoral Science Foundation under Grant No 2014M561053+1 种基金the Humanity and Social Science Foundation of Ministry of Education of China under Grant No 15YJA630108the Hebei Province Natural Science Foundation under Grant No E2016202341
文摘The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. （2） An effective sparse hard-cut expec- tation maximization （SHC-EM） learning algorithm for the GPM model is proposed to improve the prediction performance. SHO-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval SHC-EM outperforms the traditional variational 1earning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning.
文摘High-efficiency perovskite solar cells（PSCs） reported hitherto have been mostly prepared in a moisture and oxygen-free glove-box atmosphere, which hampers upscaling and real-time performance assessment of this exciting photovoltaic technology. In this work, we have systematically studied the feasibility of allambient-processing of PSCs and evaluated their photovoltaic performance. It has been shown that phasepure crystalline tetragonal MAPbI;perovskite films are instantly formed in ambient air at room temperature by a two-step spin coating process, undermining the need for dry atmosphere and post-annealing.All-ambient-processed PSCs with a configuration of FTO/TiO;/MAPbI;/Spiro-OMeTAD/Au achieve opencircuit voltage（990 mV） and short-circuit current density（20.31 mA/cm;） comparable to those of best reported glove-box processed devices. Nevertheless, device power conversion efficiency is still constrained at 5% by the unusually low fill-factor of 0.25. Dark current–voltage characteristics reveal poor conductivity of hole-transporting layer caused by lack of oxidized spiro-OMe TAD species, resulting in high seriesresistance and decreased fill-factor. The study also establishes that the above limitations can be readily overcome by employing an inorganic p-type semiconductor, copper thiocyanate, as ambient-processable hole-transporting layer to yield a fill-factor of 0.54 and a power conversion efficiency of 7.19%. The present findings can have important implications in industrially viable fabrication of large-area PSCs.
