In this paper, a data-driven linear clustering(DLC) method is proposed to solve the long-term system load forecasting problem caused by load fluctuation in some developed cities. A large substation load dataset with a...In this paper, a data-driven linear clustering(DLC) method is proposed to solve the long-term system load forecasting problem caused by load fluctuation in some developed cities. A large substation load dataset with annual interval is utilized and firstly preprocessed by the proposed linear clustering method to prepare for modelling.Then optimal autoregressive integrated moving average(ARIMA) models are constructed for the sum series of each obtained cluster to forecast their respective future load. Finally, the system load forecasting result is obtained by summing up all the ARIMA forecasts. From error analysis and application results, it is both theoretically and practically proved that the proposed DLC method can reduce random forecasting errors while guaranteeing modelling accuracy, so that a more stable and precise system load forecasting result can be obtained.展开更多
From the mesoscopic point of view,a definition of soft point is introduced by considering the attributes of geometric profile and mass distribution.After that,this concept is used to develop the soft matching techniqu...From the mesoscopic point of view,a definition of soft point is introduced by considering the attributes of geometric profile and mass distribution.After that,this concept is used to develop the soft matching technique to simulate the chaotic behaviors of the equations.Especially,a tennis model with deformation factor a(t)is proposed to derive a generalized Newton-Stokes equation v′(t)=λ(v T-a(t)v(t)).Furthermore,a concept of duality of deformation factor a(t)and velocity v(t)with respect to the generalized NewtonStokes equation is established.To solve this equation,two data-driven models of a(t)are provided,one is based on the concept of soft matching,while the other is by using the amplitude modulation.Finally,the related iterative algorithm is developed to simulate the motion of the falling body via the duality of a(t)and v(t).Numerical examples successfully demonstrate the phenomenon of chaos,which consists of the continual random oscillations and sudden accelerations.Moreover,the algorithm is tested by using larger coefficients corresponding to the terminal velocity and shows more satisfactory results.It may enable us to characterize the total energy of the dynamical system more accurately.展开更多
Surrogate models have shown to be effective in assisting evolutionary algorithms(EAs)for solving computationally expensive complex optimization problems.However,the effectiveness of the existing surrogate-assisted evo...Surrogate models have shown to be effective in assisting evolutionary algorithms(EAs)for solving computationally expensive complex optimization problems.However,the effectiveness of the existing surrogate-assisted evolutionary algorithms still needs to be improved.A data-driven evolutionary sampling optimization(DESO)framework is proposed,where at each generation it randomly employs one of two evolutionary sampling strategies,surrogate screening and surrogate local search based on historical data,to effectively balance global and local search.In DESO,the radial basis function(RBF)is used as the surrogate model in the sampling strategy,and different degrees of the evolutionary process are used to sample candidate points.The sampled points by sampling strategies are evaluated,and then added into the database for the updating surrogate model and population in the next sampling.To get the insight of DESO,extensive experiments and analysis of DESO have been performed.The proposed algorithm presents superior computational efficiency and robustness compared with five state-of-the-art algorithms on benchmark problems from 20 to 200 dimensions.Besides,DESO is applied to an airfoil design problem to show its effectiveness.展开更多
The 1st International Conference on Data-driven Knowledge Discovery： When Data Science Meets Information Science took place at the National Science Library （NSL）, Chinese Academy of Sciences （CAS） in Beijing from...The 1st International Conference on Data-driven Knowledge Discovery： When Data Science Meets Information Science took place at the National Science Library （NSL）, Chinese Academy of Sciences （CAS） in Beijing from June 19 till June 22, 2016. The Conference was opened by NSL Director Xiangyang Huang, who placed the event within the goals of the Library, and lauded the spirit of intemational collaboration in the area of data science and knowledge discovery. The whole event was an encouraging success with over 370 registered participants and highly enlightening presentations. The Conference was organized by the Journal of Data andlnformation Science （JDIS） to bring the Joumal to the attention of an international and local audience.展开更多
This paper proposes a dynamic-decision-based realtime dispatch method to coordinate the economic objective with multiple types of security dispatch objectives while reducing constraint violations in the process of adj...This paper proposes a dynamic-decision-based realtime dispatch method to coordinate the economic objective with multiple types of security dispatch objectives while reducing constraint violations in the process of adjusting the system operation point to the optimum.In each decision moment,the following tasks are executed in turn:①locally linearizing the system model at the current operation point with the online model identification by using measurements;②narrowing down the gaps between unsatisfied security requirements and their security thresholds in order of priority;③minimizing the generation cost;④minimizing the security indicators within their security thresholds.Compared with the existing real-time dispatch strategies,the proposed method can adjust the deviations caused by unpredictable power flow fluctuations,avoid dispatch bias caused by model parameter errors,and reduce the constraint violations in the dispatch decision process.The effectiveness of the proposed method is verified with the IEEE 39-bus system.展开更多
基金supported by the National Energy(Shanghai)Smart Grid Research Centerthe National Natural Science Foundation of China(No.51377103)
文摘In this paper, a data-driven linear clustering(DLC) method is proposed to solve the long-term system load forecasting problem caused by load fluctuation in some developed cities. A large substation load dataset with annual interval is utilized and firstly preprocessed by the proposed linear clustering method to prepare for modelling.Then optimal autoregressive integrated moving average(ARIMA) models are constructed for the sum series of each obtained cluster to forecast their respective future load. Finally, the system load forecasting result is obtained by summing up all the ARIMA forecasts. From error analysis and application results, it is both theoretically and practically proved that the proposed DLC method can reduce random forecasting errors while guaranteeing modelling accuracy, so that a more stable and precise system load forecasting result can be obtained.
基金supported by the National Natural Science Foundation of China(Nos.12061160462,11631015)。
文摘From the mesoscopic point of view,a definition of soft point is introduced by considering the attributes of geometric profile and mass distribution.After that,this concept is used to develop the soft matching technique to simulate the chaotic behaviors of the equations.Especially,a tennis model with deformation factor a(t)is proposed to derive a generalized Newton-Stokes equation v′(t)=λ(v T-a(t)v(t)).Furthermore,a concept of duality of deformation factor a(t)and velocity v(t)with respect to the generalized NewtonStokes equation is established.To solve this equation,two data-driven models of a(t)are provided,one is based on the concept of soft matching,while the other is by using the amplitude modulation.Finally,the related iterative algorithm is developed to simulate the motion of the falling body via the duality of a(t)and v(t).Numerical examples successfully demonstrate the phenomenon of chaos,which consists of the continual random oscillations and sudden accelerations.Moreover,the algorithm is tested by using larger coefficients corresponding to the terminal velocity and shows more satisfactory results.It may enable us to characterize the total energy of the dynamical system more accurately.
基金supported by the National Natural Science Foundation of China(62076225,62073300)the Natural Science Foundation for Distinguished Young Scholars of Hubei(2019CFA081)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(CUGGC03).
文摘Surrogate models have shown to be effective in assisting evolutionary algorithms(EAs)for solving computationally expensive complex optimization problems.However,the effectiveness of the existing surrogate-assisted evolutionary algorithms still needs to be improved.A data-driven evolutionary sampling optimization(DESO)framework is proposed,where at each generation it randomly employs one of two evolutionary sampling strategies,surrogate screening and surrogate local search based on historical data,to effectively balance global and local search.In DESO,the radial basis function(RBF)is used as the surrogate model in the sampling strategy,and different degrees of the evolutionary process are used to sample candidate points.The sampled points by sampling strategies are evaluated,and then added into the database for the updating surrogate model and population in the next sampling.To get the insight of DESO,extensive experiments and analysis of DESO have been performed.The proposed algorithm presents superior computational efficiency and robustness compared with five state-of-the-art algorithms on benchmark problems from 20 to 200 dimensions.Besides,DESO is applied to an airfoil design problem to show its effectiveness.
文摘The 1st International Conference on Data-driven Knowledge Discovery： When Data Science Meets Information Science took place at the National Science Library （NSL）, Chinese Academy of Sciences （CAS） in Beijing from June 19 till June 22, 2016. The Conference was opened by NSL Director Xiangyang Huang, who placed the event within the goals of the Library, and lauded the spirit of intemational collaboration in the area of data science and knowledge discovery. The whole event was an encouraging success with over 370 registered participants and highly enlightening presentations. The Conference was organized by the Journal of Data andlnformation Science （JDIS） to bring the Joumal to the attention of an international and local audience.
基金This work was supported by the National Natural Science Foundation of China(No.51761145106)the Guangdong Provincial Natural Science Foundation of China(No.2018B030306041)+1 种基金the Fundamental Research Funds for the Central Universities(No.2019SJ01)the China Scholarship Council(No.201806155019).
文摘This paper proposes a dynamic-decision-based realtime dispatch method to coordinate the economic objective with multiple types of security dispatch objectives while reducing constraint violations in the process of adjusting the system operation point to the optimum.In each decision moment,the following tasks are executed in turn:①locally linearizing the system model at the current operation point with the online model identification by using measurements;②narrowing down the gaps between unsatisfied security requirements and their security thresholds in order of priority;③minimizing the generation cost;④minimizing the security indicators within their security thresholds.Compared with the existing real-time dispatch strategies,the proposed method can adjust the deviations caused by unpredictable power flow fluctuations,avoid dispatch bias caused by model parameter errors,and reduce the constraint violations in the dispatch decision process.The effectiveness of the proposed method is verified with the IEEE 39-bus system.
