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第41届中国控制会议

作者： Xiaofeng Yang Cunzhi Tong Feng Zhu Yuan Huang Xianchuan Lu Dong Xie Zhesi Liu Xinyi Ying Bing Wang State Grid Zhejiang Electric Power Co. LTD. School of Energy and Electrical Engineering Hohai University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The research on spatial load is of great significance to distribution network *** existing research focuses on the study of spatial load prediction,but the study of various spatial load distribution law is not ***,a research method of spatial load distribution based on geographic feature extraction and non-parametric kernel density estimation is proposed in the ***,the functional communities are generated according to the actual land use of a certain place,the geographic feature information of the functional communities is extracted,and the land use types are ***,a clustering algorithm is applied to classify the functional communities according to the extracted *** that,the non-parametric kernel density estimation method is used to extract the typical distribution characteristics of the load density of functional communities of different geographical categories and different land-use ***,the distribution characteristics are analyzed to derive the distribution patterns of various spatial loads in the research *** reasonableness and practicality of this method are verified by the simulation of various spatial load distribution law in a certain geographical area of a first-tier city.

关键词： Spatial load distribution law Geographic feature extraction Fuzzy c-means clustering non-parametric kernel density estimation

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Wind Power Prediction Errors Model and Algorithm Based on non-parametric kernel density estimation 5

Wind Power Prediction Errors Model and Algorithm Based on No...

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5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT)

作者： Liao, Guodong Ming, Jie Wei, Boyuan Xiang, Hongji Jiang, Nan Ai, Peng Dai, Chaohua Xie, Xintao Li, Mengjiao State Grid Hunan Elect Power Corp Econ & Tech Res Inst Changsha Hunan Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China State Grid Hunan Elect Power Corp Econ & Tech Res Inst Changsha Hunan Peoples R China

ISBN: (纸本)9781467371063

For the high randomness and fluctuation of wind power, as well as the low precision of the power prediction, the traditional prediction of wind power point is not able to describe the uncertainty of wind power. A normal distribution is usually used to model wind power forecast error, but it is not valid in some special cases. In this paper, non-parametric kernel density estimation is adopted to calculate the probability density errors of wind power prediction at different levels. According to system reserve capacity requirements, safety and economy of power generation dispatching, a wind power prediction interval with three spline interpolation is acquired which satisfies the certain confidence interval. The three spline interpolation is the wind power error's distribution function. An equality constrained optimization problem was simplified into an unconstrained optimization problem and Newton with the characteristics of non-parametric kernel was presented. Given a probability value at a certain precision, it's useful to use Newton to search for arguments. Then, the upper and lower range are obtained. The calculation results show that the used wind power interval forecasting method can provide wind power prediction curve and its variation range, and is more suitable for wind power uncertainly.

关键词： wind power prediction error distribution confidence interval non-parametric kernel density estimation optimization model and algorithm

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KNN non-parametric kernel density estimation method for motion foreground detection based on Gaussian filtering 11

KNN non-parametric kernel density estimation method for moti...

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11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Yang, Xiaoqiang Feng, Tianju Xian Univ Sci & Technol Coll Comp Sci & Technol Xian Peoples R China

ISBN: (纸本)9781728118598

Background subtraction is an important method in moving target detection. Its difficulty lies in the establishment of dynamic background model. All kinds of non-parametric density estimation methods are effective to solve this problem, but there are obvious shadow on the foreground image and can not clearly outline the contour of the target when the target moves forward and backward, left and right in a short distance. In order to solve this problem, K-Nearest Neighbor non-parametric kernel density estimation method based on Gaussian filter is proposed for target detection. K-Nearest Neighbor non-parametric kernel density estimation is used to analyze the probability density of pixels in the image and extract the dynamic target. Meanwhile, Gaussian filter is used to conduct high-pass filtering on the sampled video frame in the frequency domain. The resulting image is then fused with the foreground image in bit level to obtain the final target image. Experiments show that this method has certain generality, obvious effect of eliminating shadow, clear target contour, and can detect dynamic target in complex environment.

关键词： Background subtraction non-parametric kernel density estimation Gaussian filter

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Consideration of Wind-Solar Uncertainty and V2G Mode of Electric Vehicles in Bi-Level Optimization Scheduling of Microgrids

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ENERGIES 2025年第4期18卷

作者： Chang, Zezhou Liu, Xinyuan Zhang, Qian Zhang, Ying Wang, Ziren Zhang, Yuyuan Li, Wei State Grid Shanxi Elect Power Co Elect Power Res Inst Taiyuan 030001 Peoples R China North China Elect Power Univ Dept Econ Management Baoding 071000 Peoples R China

In recent years, the global electric vehicle (EV) sector has experienced rapid growth, resulting in major load variations in microgrids due to uncontrolled charging behaviors. Simultaneously, the unpredictable nature of distributed energy output complicates effective integration, leading to frequent limitations on wind and solar energy utilization. The combined integration of distributed energy sources with electric vehicles introduces both opportunities and challenges for microgrid scheduling;however, relevant research to inform practical applications is currently insufficient. This paper tackles these issues by first introducing a method for generating typical wind-solar output scenarios through kernel density estimation and a combination strategy using Frank copula functions, accounting for the complementary traits and uncertainties of wind and solar energy. Building on these typical scenarios, a two-level optimization model for a microgrid is created, integrating demand response and vehicle-to-grid (V2G) interactions of electric vehicles. The model's upper level aims to minimize operational and environmental costs, while the lower level seeks to reduce the total energy expenses of electric vehicles. Simulation results demonstrate that this optimization model improves the economic efficiency of the microgrid system, fosters regulated EV electricity consumption, and mitigates load variations, thus ensuring stable microgrid operation.

关键词： non-parametric kernel density estimation Frank copula function V2G technology mobile energy storage grid integration microgrid

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A Coordinated Dispatching Model Considering Generation and Operation Reserve in Wind Power-Photovoltaic-Pumped Storage System

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ENERGIES 2020年第18期13卷

作者： Cui, Dai Xu, Fei Ge, Weichun Huang, Pengxiang Zhou, Yunhai Shenyang Univ Technol Sch Elect Engn Shenyang 100084 Peoples R China State Grid Liaoning Elect Power Co Ltd Shenyang 100084 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443000 Peoples R China

Large-scale grid integration of renewable energy increases the uncertainty and volatility of power systems, which brings difficulties to output planning and reserve decision-making of power system units. In this paper, we innovatively combined the non-parametric kernel density estimation method and scenario method to describe the uncertainty of renewable energy outputs, and obtained a representative set of renewable energy output scenarios. In addition, we proposed a new method to determine the reserve capacity demand. Further, we derived the quantitative relationship between the reserve demand and the power system reliability index, which was used as the constraint condition of a day-ahead power generation dispatch. Finally, a coordinated dispatching model of power generation and reserve was established, which had the lowest penalty for curtailment of wind power and photovoltaic, as well as the lowest total operating cost for thermal power units, gas power units, and pumped storage power station. By simulating three different working conditions, the proposed model was compared with the traditional deterministic model. Results showed that our proposed method significantly improved system efficiency while maintaining system reliability.

关键词： pumped storage renewable energy operation reserve non-parametric kernel density estimation scenario method coordinated dispatching

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Robust minimum distance estimation of a linear regression model with correlated errors in the presence of outliers

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2021年第23期50卷 5488-5498页

作者： Piradl, Sajjad Shadrokh, Ali Payame Noor Univ Dept Stat Tehran 193954697 Iran

A linear regression model, involves independent errors. But in practice, it is often violated. A linear regression with correlated errors appears in many contexts. Correlated errors can seriously affect the robustness of the linear regression model. The robustness of the parameter estimators in the linear regression model, can be saved using the M-estimator. However, it acquires robustness for the sake of efficiency. While, it has been shown that the minimum Matusita distance estimators, achieves both robustness and efficiency. On the other hand, because the estimators obtained from the Cochrane and Orcutt adjusted least squares estimation are not affected by the correlation of errors, so they have a good efficiency. This article, using a non-parametric kernel density estimation, proposes a new method for obtaining minimum Matusita distance estimator of the linear regression model with correlated errors in the presence of outliers. Simulation and real data study are carried out for the proposed estimation. In simulation, the proposed estimator in the linear regression model with correlated errors, represents smaller biases and mean squared errors than the M-estimator and the Cochran and Orcutt adjusted least squares estimator. In real data, the proposed estimator, has smaller standard errors than two other estimators.

关键词： Minimum Matusita distance estimation M-estimation Cochrane and Orcutt adjusted least squares estimation non-parametric kernel density estimation correlated errors outliers

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Coupling coordination degree and spatial dynamic evolution of a regional green competitiveness system - A case study from China

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ECOLOGICAL INDICATORS 2019年 104卷 489-500页

作者： Cheng, Xiu Long, Ruyin Chen, Hong Li, Qianwen China Univ Min & Technol Sch Management A504 Nanhu Campus Xuzhou 221116 Jiangsu Peoples R China

The coordinated development of the economy, society, and the environment is an important issue for enhancing regional green competitiveness. Therefore, we established a model to evaluate the coupling coordination degree of green competitiveness. Taking China's 30 provinces as an example, spatial econometric analysis methods and non-parametric kernel density models were used to explore the spatial distribution and dynamic evolution mechanism of system coupling coordination. Results showed that: (1) the economic subsystems and the social subsystems were synchronized and the coupling coordination degree was increasing. There was a disconnection between the economic subsystems and the resource-environmental subsystems, and also between the social subsystems and the resource-environmental subsystems. Furthermore, the coupling coordination degrees were declining. (2) The coupling coordination degree of the regional green competitiveness system had significant spatial dependence and formed two stable agglomeration areas. The first was a high coupling coordination degree agglomeration area centered on Shanghai with an overspreading southward. The second was a low coupling coordination degree agglomeration area centered on Qinghai with an overspreading eastward. (3) There were different dynamic trends among two agglomeration areas and the median contiguous areas. The average coupling coordination degree declined with an expanded gap in high agglomeration areas. The average coupling coordination degree remained stable with a narrowed gap, and the agglomeration center moved toward a higher value area in low agglomeration areas. The average coupling coordination degree showed a downward trend with an increased gap, and two peaks that converged toward lower and higher values were observed in the median contiguous areas. The conclusions have important practical significance for green competitiveness improving.

关键词： Coupling coordination degree Dynamic evolution non-parametric kernel density estimation Regional green competitiveness Spatial characteristic

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Wind power prediction based on PSO-Kalman

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ENERGY REPORTS 2022年 8卷 958-968页

作者： Li, Daoqing Yu, Xiaodong Liu, Shulin Dong, Xia Zang, Hongzhi Xu, Rui Qilu Univ Technol Shandong Acad Sci Sch Elect Engn & Automat Jinan 250353 Peoples R China State Grid Shandong Elect Power Co Econ & Technol Res Inst Jinan Peoples R China Huaneng JinanHuangtai Power Generat Co Ltd Jinan Peoples R China

Because of its clean and green, wind power is broadly used all over the world. Wind power is random and unstable, so wind power integration will inevitably bring great impact to power system. Accurate wind power prediction can effectively alleviate the impact caused by wind power uncertainty. In order to increase the accuracy of wind power prediction, this article uses paper swarm optimization algorithm (PSO) to improve the traditional Kalman filter, and PSO-Kalman wind power point prediction model is established. The proposed model solves the problem of low prediction accuracy of traditional Kalman filter caused by observation noise and process noise. Finally, based on point prediction error, non-parametric kernel density estimation is used for interval prediction. By experimental simulation, by comparing the error evaluation indexes of point prediction and interval prediction, it can be found that the point prediction error of PSO-Kalman is the smallest, indicating that PSO can effectively improve the prediction accuracy of Kalman. On this basis, the interval prediction performance is also better than before. Moreover, the model proposed in this article converges fast and has better general applicability. (C) 2022 The Authors. Published by Elsevier Ltd.

关键词： Wind power prediction Particle swarm optimization algorithm (PSO) Kalman filter algorithm non-parametric kernel density estimation

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A texton-based kernel density estimation approach for background modeling under extreme conditions

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COMPUTER VISION AND IMAGE UNDERSTANDING 2014年 122卷 74-83页

作者： Spampinato, C. Palazzo, S. Kavasidis, I. Univ Catania Dept Elect Elect & Comp Engn Catania Italy

Background modeling is a well-know approach to detect moving objects in video sequences. In recent years, background modeling methods that adopt spatial and texture information have been developed for dealing with complex scenarios. However, none of the investigated approaches have been tested under extreme conditions, such as the underwater domain, on which effects compromising the video quality affect negatively the performance of the background modeling process. In order to overcome such difficulties, more significant features and more robust methods must be found. In this paper, we present a kernel density estimation method which models background and foreground by exploiting textons to describe textures within small and low contrasted regions. Comparison with other texture descriptors, namely, local binary pattern (LBP) and scale invariant local ternary pattern (SILTP) shown improved performance. Besides, quantitative and qualitative performance evaluation carried out on three standard datasets showing very complex conditions revealed that our method outperformed state-of-the-art methods that use different features and modeling techniques and, most importantly, it is able to generalize over different scenarios and targets. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Background and foreground modeling non-parametric kernel density estimation Texture analysis Video analysis

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A comparative analysis of Machine Learning Techniques for short-term grid power forecasting and uncertainty analysis of Wave Energy Converters

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 138卷

作者： Crespo, Rafael Natalio Fontana Aliberti, Alessandro Bottaccioli, Lorenzo Pasta, Edoardo Sirigu, Sergej Antonello Macii, Enrico Mattiazzo, Giuliana Patti, Edoardo Politecn Torino Dept Control & Comp Engn I-10129 Turin Italy Politecn Torino Energy Ctr Lab I-10129 Turin Italy Politecn Torino Dept Mech & Aerosp Engn I-10129 Turin Italy

Wave Energy is one of the renewable sources with greatest potential. Since power coming from waves fluctuates, the grid integration of wave energy involves several power conditioning stages to comply with grid quality requirements. However, to ensure full integration of wave energy in a smart grid scenario and unlock advanced monitoring and control techniques (e.g. Demand/Response), it is crucial to forecast the output power. This work proposes a methodology to forecast in short-term horizons (i.e. 15 min to 240 min) the power delivered to the grid of the Inertial Sea Wave Energy Converter (ISWEC), a device that harnesses wave power through the inertial effect of a gyroscope. Therefore, we designed, optimized and compared the performance of five known machine learning techniques for time series point forecasting: Random Forest, Support Vector Regression, Long Short-Term Memory Neural Network, Transformer Neural Network and 1 Dimensional Convolutional Neural Network. Additionally, we studied the efficacy of downsampling technique aggregating original dataset sampled every 0.1 s in time steps of 1min, 3min, 5min and 15min to compare the performance behaviour of the different machine learning models for these datasets. Furthermore, we implemented Prediction Intervals (PIs) to calculate the inherent uncertainties associated with the previously mentioned machine learning techniques. These PIs were built based on the non-parametric kernel density Estimator technique. The point forecasting and the PIs results showed that models' performance improved as the downsampling increased. Moreover, the Random Forest model was the worst-performing in all cases. Finally, none of the other models can be considered the best overall.

关键词： Renewable energy Wave energy converter Energy forecast Neural networks non-parametric kernel density estimation Uncertainty analysis Prediction intervals

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