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A data decomposition and End-to-End Optimization-Based Monthly Carbon Emission Intensity of Electricity Forecasting Method

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2025年第1期2025卷

作者： Yan, Yue Feng, Haoran Song, Jinwei Zhang, Shixu Zhang, Shize He, Qi BDC State Grid Corp China Beijing 100052 Peoples R China Tsinghua Univ Sichuan Energy Internet Res Inst Beijing 610299 Peoples R China Beijing Tsintergy Technol Co Ltd Beijing 100096 Peoples R China

Accurate high-resolution carbon emission intensity of electricity forecasting (CIF) can assist multi-staker in timely adjusting their electricity consumption strategies to gain benefits. Few studies attempt to perform high-resolution (monthly and above) CIF due to the limited carbon emission data. High-resolution electricity data is easily available, and there is a coupling relationship between electricity and carbon emission data, making it possible to perform high-resolution CIF. Therefore, the paper proposes an end-to-end monthly CIF approach using annual carbon emission and monthly electricity consumption data, which can be divided into two stages. In Stage I, a monthly carbon emission data generator based on the Denton decomposition method is proposed. In Stage II, support vector machine (SVM), known for their effectiveness in small-sample prediction, are employed for monthly CIF. To ensure that the decomposed data effectively improves the predictor's performance, we propose an end-to-end optimization strategy. This strategy feeds back the predictor's performance on actual monthly data as optimization target to the generator and uses differential evolution algorithms (DEA) to optimize and adjust the decomposed data. Case studies conducted using actual data from Guangdong Province, China, demonstrate that the proposed method can effectively enhance monthly data, thereby improving prediction accuracy.

关键词： carbon emission intensity of electricity data decomposition end-to-end monthly forecasting small sample learning support vector machine

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Predicting water demand for spraying operations in dry bulk ports: A hybrid approach based on data decomposition and deep learning

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ADVANCED ENGINEERING INFORMATICS 2025年 65卷

作者： Guo, Jiaqi Wang, Wenyuan Kwong, Chi Wai Peng, Yun Xia, Zicheng Li, Xin Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China Univ Adelaide Sch Chem Engn & Adv Mat Adelaide SA 5005 Australia

Dust pollution from materials in dry bulk ports (DBPs) significantly impacts air quality and public health in coastal cities. Spraying operations are the primary dust control measures in ports and accurately predicting water demand for these operations helps optimize water scheduling and conserve resources. However, challenges remain in addressing non-stationary time series and improving prediction accuracy. Additionally, existing studies rarely consider the impacts of port operations on water demand for spraying. Therefore, this study proposes a hybrid approach based on data decomposition and deep learning to predict water demand for spraying operations in DBPs. Port operational data is specifically integrated into the input features. An optimal variational mode decomposition (OVMD) method is introduced to reduce data non-stationarity. Compared to other methods, OVMD adaptively selects the optimal modes and effectively mitigates mode mixing issues. The 1-D Convolutional Neural Network integrated with an Attention BILSTM model, combined with OVMD, an Artificial Neural Network, and Error Correction, is employed to capture long-term temporal dependencies. Moreover, the relationship between material surface moisture content and water consumption for spraying operations is uniquely incorporated into the prediction process. This approach is compared with benchmark models using a dataset from a DBP in northern China. The results demonstrate that the proposed method achieves superior predictive performance, with a MAE of 0.47, a RMSE of 0.71, and an R2 of 0.95. The proposed approach enables port operators to accurately determine water consumption for spraying operations, thereby promoting the intelligent and sustainable development of dust control in DBPs.

关键词： Dry bulk ports Spraying operations Water demand prediction data decomposition Deep learning

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data decomposition of Monte Carlo particle transport simulations via tally servers

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JOURNAL OF COMPUTATIONAL PHYSICS 2013年 252卷 20-36页

作者： Romano, Paul K. Siegel, Andrew R. Forget, Benoit Smith, Kord MIT Dept Nucl Sci & Engn Cambridge MA 02139 USA Argonne Natl Lab Argonne IL 60439 USA

An algorithm for decomposing large tally data in Monte Carlo particle transport simulations is developed, analyzed, and implemented in a continuous-energy Monte Carlo code, OpenMC. The algorithm is based on a non-overlapping decomposition of compute nodes into tracking processors and tally servers. The former are used to simulate the movement of particles through the domain while the latter continuously receive and update tally data. A performance model for this approach is developed, suggesting that, for a range of parameters relevant to LWR analysis, the tally server algorithm should perform with minimal overhead on contemporary supercomputers. An implementation of the algorithm in OpenMC is then tested on the Intrepid and Titan supercomputers, supporting the key predictions of the model over a wide range of parameters. We thus conclude that the tally server algorithm is a successful approach to circumventing classical on-node memory constraints en route to unprecedentedly detailed Monte Carlo reactor simulations. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Monte Carlo Neutron transport data decomposition Exascale Tally server

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data decomposition method for parallel polygon rasterization considering load balancing

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COMPUTERS & GEOSCIENCES 2015年第PartA期85卷 196-209页

作者： Zhou, Chen Chen, Zhenjie Liu, Yongxue Li, Feixue Cheng, Liang Zhu, A-xing Li, Manchun Nanjing Univ Dept Geog Informat Sci Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Jiangsu Prov Key Lab Geog Informat Sci & Technol Nanjing 210023 Jiangsu Peoples R China Nanjing Normal Univ Sch Geog Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Jiangsu Peoples R China Univ Wisconsin Dept Geog Madison WI 53706 USA

It is essential to adopt parallel computing technology to rapidly rasterize massive polygon data. In parallel rasterization, it is difficult to design an effective data decomposition method. Conventional methods ignore load balancing of polygon complexity in parallel rasterization and thus fail to achieve high parallel efficiency. In this paper, a novel data decomposition method based on polygon complexity (DMPC) is proposed. First, four factors that possibly affect the rasterization efficiency were investigated. Then, a metric represented by the boundary number and raster pixel number in the minimum bounding rectangle was developed to calculate the complexity of each polygon. Using this metric, polygons were rationally allocated according to the polygon complexity, and each process could achieve balanced loads of polygon complexity. To validate the efficiency of DMPC, it was used to parallelize different polygon rasterization algorithms and tested on different datasets. Experimental results showed that DMPC could effectively parallelize polygon rasterization algorithms. Furthermore, the implemented parallel algorithms with DMPC could achieve good speedup ratios of at least 15.69 and generally outperformed conventional decomposition methods in terms of parallel efficiency and load balancing. In addition, the results showed that DMPC exhibited consistently better performance for different spatial distributions of polygons. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Geo-processing Polygon rasterization Parallel computing data decomposition Load balancing

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data decomposition in Monte Carlo neutron transport simulations using global view arrays

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2015年第3期29卷 348-365页

作者： Dun, Nan Fujita, Hajime Tramm, John R. Chien, Andrew A. Siegel, Andrew R. Univ Chicago Dept Comp Sci Chicago IL 60637 USA Argonne Natl Lab Math & Comp Sci Div Argonne IL 60439 USA MIT Dept Nucl Sci & Engn Cambridge MA 02139 USA

Accommodating large tally data can be a challenging problem for Monte Carlo neutron transport simulations. Current approaches include either simple data replication, or are based on application-controlled decomposition such as domain partitioning or client/server models, which are limited by either memory cost or performance loss. We propose and analyze an alternative solution based on global view arrays. By using global view arrays, tallies are naturally partitioned into small globally addressable blocks that fit in the limited on-node memory of compute nodes, achieving both highly scalable memory and performance efficiency. This approach also greatly simplifies the programmability compared with application-controlled approaches. Our implementation is based on integrating a global view library built on MPI one-sided communication, global view resilience (GVR), into the OpenMC Monte Carlo transport code. The remote memory access (RMA)-based global view array implementation is able to achieve 85% efficiency at 16,384 processes compared with 1,000 processes with 2.39TB mesh tally across 1,366 nodes on a Cray XC30 supercomputer. Our results improve scalability significantly compared with the tally server approach and are better than any other published results, indicating that global view array is a promising alternative to enable full-core light water reactor analysis on current and future computer systems.

关键词： Monte Carlo neutron transport data decomposition global array one-sided communication exascale computing

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An empirical study of data decomposition for software parallelization

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JOURNAL OF SYSTEMS AND SOFTWARE 2017年 125卷 401-416页

作者： Meade, Anne Deeptimahanti, Deva Kumar Buckley, Jim Collins, J. J. Univ Limerick Dept Comp Sci & Informat Syst Lero Limerick Ireland

Context: Multi-core architectures are becoming increasingly ubiquitous and software professionals are seeking to leverage the capabilities of distributed-memory architectures. The process of parallelizing software applications can be very tedious and error-prone, in particular the task of data decomposition. Empirical studies investigating the complexity of data decomposition and communication are lacking. Objective: Our objective is threefold: (i) to gain an empirical-based understanding of the task of data decomposition as part of the parallelization of software applications;(ii) to identify key requirements for tools to assist developers in this task, and (iii) assess the current state-of-the-art. Methods: Our empirical investigation employed a multi-method approach, using an interview study, participant-observer case study, focus group study, and a sample survey. The empirical investigation involved collaborations with three industry partners: IBM's High Performance Computing Center, the Irish Centre for High-End Computing (ICHEC), and JBA Consulting. Results: This article presents data decomposition as one of the most prevalent tasks of parallelizing applications for multi-core architectures. Based on our studies, we identify ten key requirements for tool support to help HPC developers in this area. Our evaluation of the state-of-the-art shows that none of the extant tool support implements all 10 requirements. Conclusion: While there is a considerable body of research in the area of HPC, a few empirical studies exist which explicitly focus on the challenges faced by practitioners in this area;this research aims to address this gap. The empirical studies in this article provide insights that may help researchers and tool vendors to better understand the needs of parallel programmers. (C) 2016 Elsevier Inc. All rights reserved.

关键词： data decomposition Parallelization Empirical studies

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Attribute reduction: a horizontal data decomposition approach

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SOFT COMPUTING 2016年第3期20卷 951-966页

作者： Honko, Piotr Bialystok Tech Univ Fac Comp Sci Wiejska 45A PL-15351 Bialystok Poland

Ever-growing data generate a need for new solutions to the problem of attribute reduction. Such solutions are required to deal with limited memory capacity and with many computations needed for large data processing. This paper proposes new definitions of attribute reduction using horizontal data decomposition. Algorithms for computing reducts of an information system and decision table are developed and evaluated. In the proposed approach, the size of subtables obtained during the decomposition can be arbitrarily small. The reduct sets of subtables are computed independently from one another using any heuristic method for attribute reduction. Compared with standard attribute reduction methods, the proposed approach can produce the same reducts with less space complexity and with the same or less theoretical time complexity. Experiments conducted under this work show that for information systems with fewer attributes or reducts the time needed for computing the reduct set can be shorten.

关键词： Attribute reduction Rough set theory data decomposition FPGA

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Robust Batch Steganography in Social Networks With Non-Uniform Payload and data decomposition

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IEEE ACCESS 2018年 6卷 29912-29925页

作者： Li, Fengyong Wu, Kui Zhang, Xinpeng Yu, Jiang Lei, Jingsheng Wen, Mi Shanghai Univ Elect Power Coll Comp Sci & Technol Shanghai 200090 Peoples R China Univ Victoria Comp Sci Dept Victoria BC V8W 3P6 Canada Shanghai Univ Sch Commun & Informat Engn Shanghai 200072 Peoples R China Shanghai Business Sch Sch Informat & Comp Shanghai 200235 Peoples R China

Batch steganography refers to a steganography method where a user tries to hide confidential payload within a batch of images from social networks. It is significantly different from the traditional laboratory steganography where a user only considers an individual image. To apply batch steganography in social media networks, we are faced with two nontrivial problems: 1) how to assign payload to multiple images? and 2) how to recover the hidden payload if some images are lost during transmission? We tackle the problems by: 1) developing an optimal payload embedding strategy and 2) designing a special type of data decomposition. In the former, an optimal non-uniform payload distribution for multiple images is obtained by iterative feature back replacement. In the later, we employ special matrix operation to expand original data and split them into multiple shares. These shares are then embedded into different covers following the optimal non-uniform payload distribution. Our solution is robust in the sense that the recipient can recover the hidden data even if some images are intercepted or lost during delivery. Comprehensive experimental results show that our method outperforms the state-of-the-art in terms of anti-detectability and robustness.

关键词： Information hiding batch steganography embedding strategy data decomposition robustness social networks

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An interval AQI combination prediction model based on multiple data decomposition and information aggregation operator

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2024年第9期31卷 14284-14302页

作者： Wang, Yixiang Li, Hao Dai, Xianchao Zhou, Ligang Anhui Univ Sch Big Data & Stat Hefei 230601 Anhui Peoples R China Anhui Univ Ctr Appl Math Hefei 230601 Anhui Peoples R China Anhui Univ Sch Math Sci Hefei 230601 Anhui Peoples R China

In this paper, an interval Air Quality Index (AQI) combination prediction model based on EEMD, VMD, and the weighted power average (WPA) operator is proposed. EEMD and VMD decompose complex AQI data effectively, while WPA operator reasonably aggregates the prediction results of different models. We validate the effectiveness of the proposed model using Shenzhen's daily interval AQI. Furthermore, three kinds of prediction models are compared with the proposed model to highlight its advantages from various perspectives. The results show that the introduction of data decomposition methods significantly improves the model's prediction accuracy, WPA operator further enhances the model's prediction capability, and the incorporation of EEMD and VMD enables the proposed model to have stronger feature extraction capabilities for complex time series. As a result, the model proposed in this paper demonstrates strong generalization ability and prediction accuracy, making it applicable not only for air quality prediction but also for other domains such as economics and environment.

关键词： Interval data Combination prediction Information aggregation operator data decomposition Air quality prediction

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A review of ultra-short-term forecasting of wind power based on data decomposition-forecasting technology combination model

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ENERGY REPORTS 2022年 8卷 14200-14219页

作者： Chen, Yulong Hu, Xue Zhang, Lixin Shihezi Univ Coll Mech & Elect Engn Shihezi 832000 Peoples R China Shihezi Univ Bingtuan Energy Dev Inst Shihezi 832000 Peoples R China

The development of renewable clean energy wind power is very rapid. However, the strong randomness and weak dispatchability of wind power bring significant challenges to wind power operation and maintenance, such as grid connection, power dispatch, and unit combination. At this stage, the combined model based on signal decomposition-prediction technology plays an essential role in ultra-short-term deterministic wind power prediction, promoting the development of the wind power industry. With the development of artificial intelligence technology, this paper discusses and summarizes the latest research progress on the critical technologies of the combined signal decomposition-prediction technology model to improve the prediction accuracy for the problem of high demand for ultra-short-term power prediction accuracy. data decomposition from two perspectives of decomposition techniques reduces input data complexity and feature extraction by eliminating redundant information. The prediction techniques are from two perspectives: prediction methods to demonstrate specific laws and parameter search to determine the optimal parameters. Three perspectives evaluated prediction performance: vertical error evaluation, horizontal error evaluation, and prediction error evaluation by analyzing the above standard methods in detail, including the principle, idea, application examples, advantages and disadvantages, key points, and application notes of each technique. And the prediction performance of the current combined models is compared extensively. Finally, the future research trends of wind power ultra-short term are discussed and foreseen to provide a reference for the development of ultra-short-term power prediction technology, prediction performance improvement, and safe and stable grid operation. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by- nc- nd/4.0/).

关键词： Ultra-short-term forecasting Forecasting accuracy data decomposition Machine learning Combined forecasting model

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