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IEEE Power and Energy Conference at Illinois (PECI)

作者： Saranya, A. Swarup, k. Shanti Indian Inst Technol Madras Elect Engn Dept Chennai Tamil Nadu India

ISBN: (纸本)9781538641163

With the large-scale manufacturing and production, the per-unit cost of energy from photovoltaic modules have decreased, and the incentives given by the governments are slowly withdrawn. Consequently, the photovoltaic power plants are able to, and asked to, participate in the electricity markets to trade its power competing with conventional generators. This work contributes to the offering strategies for photovoltaic power plants in the day-ahead electricity market with balancing market. The uncertain variables considered are day-ahead electricity prices and actual solar power production for which scenarios are created from historical data. The photovoltaic power plant is modeled as a price taker and the offering problem is solved as a linear programming problem with stochastic optimization.

关键词： Offering strategy price taker electricity market photovoltaic power plant k-means clustering algorithm stochastic optimization linear programming

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Photovoltaic Power Prediction Model Based on Weather Forecast

Photovoltaic Power Prediction Model Based on Weather Forecas...

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Sustainable Power and Energy Conference (iSPEC)

作者： Wei Huang Chao Zhang Xinhe Zhang Junxia Meng Xiaohua Liu Bo Yuan China Electric Power Research Institute Beijing China Xinjiang Electric Power Company Wulumuqi China

ISBN: (纸本)9781728149318

In order to improve the accuracy of PV power generation forecasting, this paper proposes a photovoltaic power generation prediction model combined with weather forecast. In this paper, the k-means clustering algorithm is used to classify historical photovoltaic power generation data, and the correlation analysis method is used to determine the input variables, which reduces the dimension of the input variables and simplifies the calculation process. Then we propose a photovoltaic power generation prediction model based on long-short memory neural network (LSTM) combined with Attention mechanism. After verification using real data, the power prediction model proposed in this paper has higher accuracy and better application range. It can select the appropriate model for power prediction according to different weather conditions.

关键词： power prediction attention mechanism LSTM neural network k-means clustering algorithm Input variables Solar photovoltaics k-means algorithm computational procedure Weather Correlation analysis Power (Psychology) Energy sources Power production weather condition

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A Method of Automatically Identifying Learners' help-seeking Behavior Classifications in Online Learning Environment 2

A Method of Automatically Identifying Learners' help-seeking...

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2nd International Conference on Data Science and Business Analytics (ICDSBA)

作者： Ye, Junmin Zhao, Lixian Luo, Daxiong Wang, Zhifeng Chen, Shu CCNU Sch Comp Wuhan Hubei Peoples R China CCNU Sch Educ Informat Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9781538684313

In the online learning environment, identifying learners' behaviors in the learning process can help them improve their learning effect autonomously. Firstly, we use k-means algorithm to cluster the learner's help-seeking behavior data to get the classification label of the learner's help-seeking behavior. Secondly, we use the t-distributed Stochastic Neighbor Embedding(T-sne) algorithm to reduce the dimension of the data to visualize the clustering result. Finally, the learner's help-seeking behavior data and the help-seeking behavior classification labels are used as training data to train the Naive Bayesian model so as to automatically obtain the help-seeking behavior classification for the data generated by the new learner. Via the analysis and processing of the help-seeking behavior data using the method proposed in this paper, it shows that this method can effectively find online learners' help-seeking behavior classifications.

关键词： learner's help-seeking behavior classification k-means clustering algorithm t-distributed Stochastic Neighbor Embedding algorithm Naive Bayesian classification algorithm

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Impact of typical demand day selection on CCHP operational optimization

Impact of typical demand day selection on CCHP operational o...

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Applied Energy Symposium and Forum - Low-Carbon Cities and Urban Energy Systems (CUE)

作者： Gao, Yuan Liu, Qianying Wang, Shuxia Ruan, Yingjun Tongji Univ Mech & Energy Engn Sch 4800 Caoan Highway Shanghai 201804 Peoples R China

Due to high variability of energy demand in a whole year, optimizing the configuration and operation of a CCHP system will take very high and unfeasible computational time expenses. To overcome this problem, this paper presents a new and creative method to get few representative days that adequately preserve significant demand characteristics. Typical demand days are selected based on the use of k-means clustering algorithm and average method. A hypothetical CCHP system is optimized with mixed-integer linear programming algorithm (MILP) in order to confirm the selection of typical days in this paper. This paper also imitates traditional method. A case study of a Qingdao office building is discussed to demonstrate the proposed method. The results illustrate that the magnitude of Mean Absolute Percentage Errors (MAPE) between actual demand load and typical days load can affect actual operational effect. In conclusion, optimal number of typical days for actual CCHP operation can obtain very low MAPE and lowest annual total cost. Copyright (C) 2018 Elsevier Ltd. All rights reserved.

关键词： CCHP system k-means clustering algorithm typical demand days MILP

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Research on Discovery and Classification Technology of Electric Power Marketing Field Terminals

Research on Discovery and Classification Technology of Elect...

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作者： Xianzhou Gao Ruxia Yang Wei Chen Congcong Shi Global Energy Interconnection Research Institute State Grid Key Laboratory of Information & Network Security

In view of the characteristics of diversity,openness,complexity of Electric Power Marketing Field *** may be some security risks such as illegal terminal *** the problem of discovery and classification of Electric Power Marketing Field Terminals is need to be solved,and then we can identify the types of illegal terminals in time and take corresponding *** paper aims at the diversity of Electric Power Marketing Field Terminals and their own differentiation characteristics,and proposes a technology without *** installing client,it can automatically realize terminal discovery,and so solve the traditional problem of non-agent terminal *** the same time,through k-means clustering algorithm terminal model identification,using unsupervised algorithm to extract and identify terminal type fingerprint information,it can achieve accurate classification of terminals,and provide timely alarm information and equipment data for network control and security protection.

关键词： Unsupervised algorithm k-means clustering algorithm non-agent accurate classification terminal type fingerprint

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Research on intrusion detection based on feature extraction of autoencoder and the improved k-means algorithm

Research on intrusion detection based on feature extraction ...

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作者： Wang, Xingang Wang, Linlin School of Information Qilu University of Technology Jinan China

ISBN: (纸本)9781538636749

Nowadays, intrusion detection is a technology to effectively avoid a number of risks of network intrusion. The k-means algorithm is widely used in intrusion detection. But, the algorithm has some shortcomings, such as random selection of k value, sensitive selection of initial cluster centers, and low accuracy in clustering high-dimensional data. In order to make up for these shortcomings of the k-means algorithm, this paper proposes an AE-kmeans architecture which combines an autoencoder with the improved k-means algorithm. The AE-kmeans architecture realizes the dimension reduction and feature extraction of these original data by introducing an autoencoder, and uses the improved k-means algorithm to cluster these processed data. These improvements of the improved k-means algorithm mainly include two aspects: the first aspect, a new method is introduced to select initial cluster centers;the second aspect, the algorithm calculates the weight of each attribute with the coefficient of variation, and then uses these weights in Euclidean distance formula, resulting in a weighted Euclidean distance formula. Finally, the AE-kmeans architecture uses kDD CUP99 data set for intrusion detection simulation experiments. The Experimental results show that the AE-kmeans architecture not only enhances the ability to deal with high-dimensional data, but also improves the detection rate and reduces the error-detection rate compared with k-means algorithm. © 2017 IEEE.

关键词： AE-kmeans architecture Autoencoder Feature extraction Intrusion detection k-means clustering algorithm Weighted Euclidean distance formula

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POD-DEIM model order reduction for strain-softening viscoplasticity

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2017年 317卷 458-479页

作者： Ghavamian, F. Tiso, P. Simone, A. Delft Univ Technol Fac Civil Engn & Geosci POB 5048 NL-2600 GA Delft Netherlands Swiss Fed Inst Technol Inst Mech Syst Leonhardstr 21 CH-8092 Zurich Switzerland

We demonstrate a Model Order Reduction technique for a system of nonlinear equations arising from the Finite Element Method (FEM) discretization of the three-dimensional quasistatic equilibrium equation equipped with a Perzyna viscoplasticity constitutive model. The procedure employs the Proper Orthogonal Decomposition-Galerkin (POD-G) in conjunction with the Discrete Empirical Interpolation Method (DEIM). For this purpose, we collect samples from a standard full order FEM analysis in the offline phase and cluster them using a novel k-means clustering algorithm. The POD and the DEIM algorithms are then employed to construct a corresponding reduced order model. In the online phase, a sample from the current state of the system is passed, at each time step, to a nearest neighbor classifier in which the cluster that best describes it is identified. The force vector and its derivative with respect to the displacement vector are approximated using DEIM, and the system of nonlinear equations is projected onto a lower dimensional subspace using the POD-G. The constructed reduced order model is applied to two typical solid mechanics problems showing strain-localization (a tensile bar and a wall under compression) and a three-dimensional squarefooting problem. (C) 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.

关键词： Model order reduction Proper orthogonal decomposition Discrete empirical interpolation method Perzyna viscoplasticity Strain-softening Machine learning k-means clustering algorithm

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Daily Maximum Electric Load Forecasting with RBF Optimized by AFSA in k-means clustering algorithm

Daily Maximum Electric Load Forecasting with RBF Optimized b...

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International Conference on Materials, Mechatronics and Automation (ICMMA 2011)

作者： Shen, Wei Sun, Yueshi N China Elect Power Univ Coll Econ & Management Beijing Peoples R China

ISBN: (纸本)9783037850176

Electric load forecasting is an important aspect in the operation of energy market. Many researchers have tried various methods and have achieved considerable results. In this paper, we used Radial Basis Function Neural Network (RBFN) to train data and forecast daily maximum electric load of a costal city in North China. In order to have a better result, we introduced Artificial Fish Swarm algorithm (AFSA) to optimize RBF and adjust the center of k-means clustering algorithm. Data mining techniques were also employed to select indicators with impact on electric load. By comparing the forecast values and actual data, we arrived at conclusion that RBF optimized by AFSA could produce accurate result in forecasting daily maximum electric load. We also found that climate factors (temperature, humidity and air-pressure) had significant impact on daily maximum electric load.

关键词： AFSA RBFN k-means clustering algorithm Data Mining Daily Maximum Electric Load

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Extracting the symmetry axes of partially occluded single apples in natural scene using convex hull theory and shape context algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第12期76卷 14075-14089页

作者： Niu, Leilei Zhou, Weicong Wang, Dandan He, Dongjian Zhang, Haihui Song, Huaibo Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China

Accurate identification of apples partially occluded by branches and leaves is an urgent and key issue for a picking robot. The objective of this study was to detect the symmetry axes of partially occluded single apples accurately using the convex hull theory and Shape Context algorithm. Firstly, apple regions were obtained by using k-means clustering algorithm. Secondly, image pre-processing steps such as image binarization, hole filling, area opening and edge detection were applied. Thirdly, false contours were removed based on the convex hull theory to enhance the accuracy and stability of this method. Finally, the point matching relationship of each two contours and the two best symmetrical contours were found by using the Shape Context algorithm and Hungarian algorithm. Then the symmetry axes of apples were extracted using the matching point pairs. Least squares ellipses fitting algorithm and moment of inertia algorithm were used to compare with the presented algorithm. The angle difference between extracted symmetry axis and ideal symmetry axis for every method was computed, and the execution time of program as well. Ninety partially occluded single apple images were tested. The experimental results showed that the average angle error of the Shape Context algorithm were 7.72A degrees, 37.5 % of the ellipses fitting algorithm and 31.3 % of the inertia moment algorithm. And its average execution time is 1.86 s, 103 % of the ellipses fitting algorithm and 106 % of the inertia moment algorithm. In conclusion, it was feasible to use the proposed method to extract the symmetry axes of partially occluded apples.

关键词： Occluded apple Symmetric axis extraction Shape context Convex hull theory k-means clustering algorithm Threshold method

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Machine learning for creation of generalized lumped parameter tank models of low temperature geothermal reservoir systems

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GEOTHERMICS 2017年 70卷 62-84页

作者： Li, Y. Juliusson, E. Palsson, H. Stefansson, H. Valfells, A. Tianjin Univ Key Lab Efficient Utilizat Low & Medium Grade Ene Minist Educ 92 Weijin Rd Tianjin 300072 Peoples R China Reykjavik Univ Sch Sci & Engn Menntavegi 1 IS-105 Reykjavik Iceland Landsvirkjun Haaleitisbraut 68 IS-103 Reykjavik Iceland Univ Iceland Fac Ind Engn Mech Engn & Comp Sci Hjardarhagi 2-6 IS-107 Reykjavik Iceland

Lumped parameter tank models have gained renewed interest in recent years as an alternative tool for geothermal reservoir analysis and production planning. The models can be structured in various ways regarding the number of tanks, connections between the tanks and the parameters representing the physical properties of the geothermal system. It usually requires a time consuming and difficult process of trials and errors to manually decide the optimal configuration of a tank model. Inspired by recent development in the use of machine learning methods, we propose a method for automatically generating accurate and computationally feasible generalized tank models for isothermal, single phase, reservoirs. This is an extension of earlier work on complexity reduction of generalized tank models (Li et al., 2016). Here, a recursive "switch-back" method is constructed to maximize prediction accuracy of the model. It is also shown how the k-means clustering algorithm can be used to aggregate production wells in generalized tank models. One synthetic example and one field application from t Reykir geothermal fields in Iceland are used to illustrate the effectiveness of these methods.

关键词： Geothermal reservoir modeling Lumped-parameter models Machine learning Complexity analysis Information criterion Complexity reduction algorithm Switch-back method Model selection k-means clustering algorithm

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