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Fast Solution Method for the Large-scale Unit Commitment Problem with Long-term Storage

Chinese Journal of Electrical engineering 2023年第3期9卷 39-49页

作者： Bo Li Chunjie Qin Ruotao Yu Wei Dai Mengjun Shen Ziming Ma Jianxiao Wang School of Electrical Engineering Guangxi UniversityNanning 530004China State Grid Sichuan Electric Power Company Chengdu 610041China National Power Dispatching and Control Center State Grid Corporation of ChinaBeijing 100031China National Engineering Laboratory for Big Data Analysis and Applications(Peking University) Beijing 100871China

Long-term storage(LTS)can provide various services to address seasonal fluctuations in variable renewable energy by reducing energy ***,long-term unit commitment(UC)with LTS involves mixed-integer programming with large-scale coupling constraints between consecutive intervals(state-of-charge(SOC)constraint of LTS,ramping rate,and minimum up/down time constraints of thermal units),resulting in a significant computational ***,an iterative-based fast solution method is proposed to solve the long-term UC with ***,the UC with coupling constraints is split into several sub problems that can be solved in ***,the solutions of the sub problems are adjusted to obtain a feasible solution that satisfies the coupling ***,a decoupling method for long-term time-series coupling constraints is proposed to determine the global optimization of the SOC of the *** price-arbitrage model of the LTS determines the SOC boundary of the LTS for each sub ***,the sub problem with the SOC boundary of the LTS is iteratively solved *** proposed method was verified using a modified IEEE 24-bus *** results showed that the computation time of the unit combination problem can be reduced by 97.8%,with a relative error of 3.62%.

关键词： Constraint splitting long-term storage mix-integer programming unit commitment

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Deep learning approaches in flow visualization

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Advances in Aerodynamics 2022年第1期4卷 327-340页

作者： Can Liu Ruike Jiang Datong Wei Changhe Yang Yanda Li Fang Wang Xiaoru Yuan Key Laboratory of Machine Perception(Ministry of Education) and School of AIPeking UniversityBeijingChina China Aerodynamics Research and Development Center MianyangChina National Engineering Laboratory for Big Data Analysis and Application Peking UniversityBeijingChina

With the development of deep learning(DL)techniques,many tasks in flow visualization that used to rely on complex analysis algorithms now can be replaced by DL *** reviewed the approaches to deep learning technology in flow visualization and discussed the technical benefits of these *** also analyzed the prospects of the development of flow visualization with the help of deep learning.

关键词： Deep learning Flow visualization data management Feature extraction Particle tracing

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Evaluation of Learning Effect Based on Online data 27th

Evaluation of Learning Effect Based on Online Data

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27th China Conference on Information Retrieval, CCIR 2021

作者： Liu, Zhaohui Yan, Hongfei Chen, Chong Su, Qi Peking University Beijing China Beijing Normal University Beijing China National Engineering Laboratory for Big Data Analysis and Application Technology Center for Big Data Research Peking University Beijing China

ISBN: (纸本)9783030881887

With the development of Internet technology, online learning has gradually been widely used, which has further enriched the teaching methods. However, the indicators provided by online learning platforms to characterize students’ learning behaviors are still relatively small, it is hard to effectively evaluate students’ learning effects and provide personalized education for students. While students produce a large number of online data records during the learning process, if these data can be used to evaluate the learning effect of students and find indicators that are significantly related to the learning effect, it can help teachers know the learning status of students and provide them with more targeted guidance. The framework of this article includes: firstly collect online data of students’ learning, and determine major types of factors that affect the learning effect. Next, the original data is processed, and new features are constructed from the original data according to the factors that affect the learning effect. Then use several different methods to select the features that have the most significant impact on the learning effect, and use several regression methods to predict students’ academic performance. Finally, this article selects important features that are more relevant to learning effects, studies the relationship between these features. © 2021, Springer Nature Switzerland AG.

关键词： Students

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Empirical research on futures trading strategy based on time series algorithm 26th

Empirical research on futures trading strategy based on time...

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26th China Conference on Information Retrieval, CCIR 2020

作者： Yao, Shi Hongfei, Yan Siping, Ying Chong, Chen Qi, Su Peking University Beijing China Beijing Normal University Beijing China National Engineering Laboratory for Big Data Analysis and Application Technology Center for Big Data Research Peking University Beijing China

ISBN: (纸本)9783030567248

This article attempts to establish a trading strategy framework based on deep neural networks for the futures market, which consists of two parts: time series forecasting and trading strategies based on trading signals. In the time series forecasting task, we experimented with three types of methods with different entry points, namely recurrent neural networks with gate structure, networks combining time and frequency domain information, and network structures using attention mechanism. In the trading strategy part, the buying and selling signals and the corresponding trading volume are established according to the prediction results, and trading is conducted with the frequency of hours. In the empirical exploration part, we tested the prediction effect and strategic rate of return of various models on the copper contract. The data shows that in general, the best strategy can obtain a relatively stable income growth that has nothing to do with market fluctuations, but lacks countermeasures for rare external events with greater impact. © Springer Nature Switzerland AG 2020.

关键词： Deep neural networks

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Hierarchical attention network in stock prediction 26th

Hierarchical attention network in stock prediction

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26th China Conference on Information Retrieval, CCIR 2020

作者： Huang, Liming Yan, Hongfei Ying, Siping Li, Yansong Miao, Rui Chen, Chong Su, Qi Peking University Beijing China Beijing Normal University Beijing China National Engineering Laboratory for Big Data Analysis and Application Technology Center for Big Data Research Peking University Beijing China

ISBN: (纸本)9783030567248

To solve the stock prediction problem, we propose a deep learning model base on a hierarchical attention network. Our model is divided into two models. The first model is the article selection attention network that transfers the news into a low dimension vector. This model could identify the important factors in the news that affect the stock price. The second model is a time series attention network which combines the output of the first model and the transaction data as input. In this model, we could figure out the potential impact between different dates and summarize the historical data to predict whether the stock price will rise or fall. The most innovative concept in this paper is stock encoding. The model learns the difference between each stock and make predictions more accurate by using the stock encoding. The experimental result shows that the model fully utilize text features and make better predictions than related research papers. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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An integrated machine learning framework for stock price prediction 26th

An integrated machine learning framework for stock price pre...

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26th China Conference on Information Retrieval, CCIR 2020

作者： Bi, Quanzhi Yan, Hongfei Chen, Chong Su, Qi Peking University Beijing China Beijing Normal University Beijing China National Engineering Laboratory for Big Data Analysis and Application Technology Center for Big Data Research Peking University Beijing China

ISBN: (纸本)9783030567248

Predicting the future price of financial assets has always been an important research topic in the field of quantitative finance. This paper attempts to use the latest artificial intelligence technologies to design and implement a framework for financial asset price prediction. The framework we use is divided into three modules: Feature engineering, Regressor, and Hyper Optimizer. The Feature engineering module extract multiple features using technical indicators, FinBERT, FFT, ARIMA, stacked auto-encoder, PCA and XGBoost. The Regressor module consists of a generative adversarial network, where the generator is Seq2Seq and the discriminator is GRU. The HyperOptimizer module will tune the parameter in GAN using the Bayesian optimization algorithm. Finally, we conducted numerical experiments on our framework, which shows that the framework implemented in this paper performs better than the benchmark method. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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Semi-supervised sentiment analysis for chinese stock texts in scarce labeled data scenario and price prediction 26th

Semi-supervised sentiment analysis for chinese stock texts i...

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26th China Conference on Information Retrieval, CCIR 2020

作者： Zhaoyan, Ji Hongfei, Yan Siping, Ying Chong, Chen Qi, Su Peking University Beijing China Beijing Normal University Beijing China National Engineering Laboratory for Big Data Analysis and Application Technology Center for Big Data Research Peking University Beijing China

ISBN: (纸本)9783030567248

The application of neural network in stock prediction is developing rapidly these years because of its excellency in series data processing. However, as most of research are conducted in English, data sources and labeled data are inadequate in Chinese. Especially for natural language processing tasks in specific domain where specialized labeled data are required to train models to adapt to terminology processing, specialized labeled Chinese in text data are very scarce, such as financial text data. To tackle this challenge, we proposed a semi-supervised learning method to generate well-labeled data and train BERT, a leading natural language processing model, to obtain a trained sentiment machine. Then we got stock-related text data sentiment score based on this machine and further combine the sentiment score and other transaction data as inputs for different neural networks to predict stock price. The experimental results on a large scale of Chinese stock data and texts showed that our proposed method successfully improved prediction accuracy compared to other established methods. Besides, we also examined our method’s applicability combined with different neural networks when predicting different types of stock. © Springer Nature Switzerland AG 2020.

关键词： Deep neural networks

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Differentiable Feature Aggregation Search for Knowledge Distillation 16th

Differentiable Feature Aggregation Search for Knowledge Dist...

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16th European Conference on Computer Vision, ECCV 2020

作者： Guan, Yushuo Zhao, Pengyu Wang, Bingxuan Zhang, Yuanxing Yao, Cong Bian, Kaigui Tang, Jian Peking University Beijing China Technology Inc Beijing China National Engineering Laboratory for Big Data Analysis and Applications Beijing China DiDi AI Labs Beijing China

ISBN: (纸本)9783030585198

Knowledge distillation has become increasingly important in model compression. It boosts the performance of a miniaturized student network with the supervision of the output distribution and feature maps from a sophisticated teacher network. Some recent works introduce multi-teacher distillation to provide more supervision to the student network. However, the effectiveness of multi-teacher distillation methods are accompanied by costly computation resources. To tackle with both the efficiency and the effectiveness of knowledge distillation, we introduce the feature aggregation to imitate the multi-teacher distillation in the single-teacher distillation framework by extracting informative supervision from multiple teacher feature maps. Specifically, we introduce DFA, a two-stage Differentiable Feature Aggregation search method that motivated by DARTS in neural architecture search, to efficiently find the aggregations. In the first stage, DFA formulates the searching problem as a bi-level optimization and leverages a novel bridge loss, which consists of a student-to-teacher path and a teacher-to-student path, to find appropriate feature aggregations. The two paths act as two players against each other, trying to optimize the unified architecture parameters to the opposite directions while guaranteeing both expressivity and learnability of the feature aggregation simultaneously. In the second stage, DFA performs knowledge distillation with the derived feature aggregation. Experimental results show that DFA outperforms existing distillation methods on CIFAR-100 and CINIC-10 datasets under various teacher-student settings, verifying the effectiveness and robustness of the design. © 2020, Springer Nature Switzerland AG.

关键词： Distillation

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Rethinking Node-wise Propagation for Large-scale Graph Learning 24

Rethinking Node-wise Propagation for Large-scale Graph Learn...

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33rd ACM Web Conference, WWW 2024

作者： Li, Xunkai Ma, Jingyuan Wu, Zhengyu Su, Daohan Zhang, Wentao Li, Rong-Hua Wang, Guoren Beijing Institute of Technology Beijing China Peking University National Engineering Laboratory for Big Data Analytics and Applications Beijing China

ISBN: (纸本)9798400701719

Scalable graph neural networks (GNNs) have emerged as a promising technique, which exhibits superior predictive performance and high running efficiency across numerous large-scale graph-based web applications. However, (i) Most scalable GNNs tend to treat all nodes with the same propagation rules, neglecting their topological uniqueness;(ii) Existing node-wise propagation optimization strategies are insufficient on web-scale graphs with intricate topology, where a full portrayal of nodes' local properties is required. Intuitively, different nodes in web-scale graphs possess distinct topological roles, and therefore propagating them indiscriminately or neglecting local contexts may compromise the quality of node representations. To address the above issues, we propose Adaptive Topology-aware Propagation (ATP), which reduces potential high-bias propagation and extracts structural patterns of each node in a scalable manner to improve running efficiency and predictive performance. Remarkably, ATP is crafted to be a plug-and-play node-wise propagation optimization strategy, allowing for offline execution independent of the graph learning process in a new perspective. Therefore, this approach can be seamlessly integrated into most scalable GNNs while remaining orthogonal to existing node-wise propagation optimization strategies. Extensive experiments on 12 datasets have demonstrated the effectiveness of ATP. © 2024 ACM.

关键词： Graph neural networks

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Inverse Markov Process Based Constrained Dynamic Graph Layout

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Journal of Computer Science & Technology 2021年第3期36卷 707-718页

作者： Shi-Ying Sheng Sheng-Tao Chen Xiao-Ju Dong Chun-Yuan Wu Xiao-Ru Yuan ParisTech Elite Institute of Technology Shanghai Jiao Tong UniversityShanghai 200240China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Machine Perception(Ministry of Education) National Engineering Laboratory for Big Data Analysis and ApplicationPeking UniversityBeijing 100080China

In online dynamic graph drawing,constraints over nodes and node pairs help preserve a coherent mental map in a sequence of *** the constraints is challenging due to the requirements of both preserving mental map and satisfying the visual aesthetics of a graph *** existing algorithms basically depend on local changes but fail to do proper evaluations on the global propagation when setting *** solve this problem,we introduce a heuristic model derived from PageRank which simulates the node movement as an inverse Markov process hence to give a global analysis of the layout's change,according to which different constraints can be *** constraints,along with stress function,generate layouts maintaining spatial positions and shapes of relatively stable substructures between adjacent *** demonstrate that our method preserves both structure and position similarity to help users track graph changes visually.

关键词： graph drawing data stream dynamic graph layout

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