Identifying IP Usage Scenarios: Problems, Data, and Benchmarks

Zhang, Fan; Zhang, Weifeng; Wang, Yong; Zhong, Ting; Trajcevski, Goce; Khokhar, Ashfaq

doi:10.1109/mnet.012.2100293

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…In recent years, the field of hydropower has become the focus of many experts and scholars in the new energy sustainable development industry (Kougias et al, 2019). The traditional runoff hydropower forecasting method is mainly based on the trend extrapolation method, which has fast calculation speed and is suitable for forecasting with small load fluctuations (Zhou et al, 2022). However, the modeling process of this method is relatively complex and requires high stability of the historical data trend, which has certain limitations.…”

Section: Introductionmentioning

confidence: 99%

A runoff-based hydroelectricity prediction method based on meteorological similar days and XGBoost model

Wu,

Xie,

et al. 2024

Front. Energy Res.

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This paper proposes a runoff-based hydroelectricity prediction method based on meteorological similar days and XGBoost model. Accurately predicting the hydroelectricity supply and demand is critical for conserving resources, ensuring power supply, and mitigating the impact of natural disasters. To achieve this, historical meteorological and runoff data are analyzed to select meteorological data that are similar to the current data, forming a meteorological similar day dataset. The XGBoost model is then trained and used to predict the meteorological similar day dataset and obtain hydroelectricity prediction results. To evaluate the proposed method, the hydroelectricity cluster in Yunnan, China, is used as sample data. The results show that the method exhibits high prediction accuracy and stability, providing an effective approach to hydroelectricity prediction. This study demonstrates the potential of using meteorological similar days and the XGBoost model for hydroelectricity prediction and highlights the importance of accurate hydroelectricity prediction for water resource management and electricity production.

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Section: Introductionmentioning

confidence: 99%

A runoff-based hydroelectricity prediction method based on meteorological similar days and XGBoost model

Wu,

Xie,

et al. 2024

Front. Energy Res.

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“…In addition, China adopts UHVDC (ultra-high-voltage direct current) transmission technology to transport surplus energy in the western region to the load center in the central and eastern regions over long distances, which breaks the reverse distribution of energy resources and energy consumption in China (Fang et al, 2022), where power is usually transmitted between regions through multiple inter-provincial AC/DC channels. At present, the provincial government of the interprovincial AC and DC channels usually signs an inter-provincial power transmission framework agreement to agree on the annual power transmission scale (Zhou et al, 2022), generally including the annual power transmission, transmission load, and peak-to-valley ratio, but it is difficult to stipulate the specific power transmission curve throughout the year with the inter-provincial power transmission framework agreement. The daily output curve of the tie-line is determined by the decomposition mechanism, and the contract power decomposition curve obtained by different decomposition mechanisms may be different.…”

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confidence: 99%

Key technologies for hybrid energy system planning and operation

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Measuring and classifying IP usage scenarios: a continuous neural trees approach

Li,

Zhou,

Wang

et al. 2024

Sci Rep

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Understanding user behavior via IP addresses is a crucial measure towards numerous pragmatic IP-based applications, including online content delivery, fraud prevention, marketing intelligence, and others. While profiling IP addresses through methods like IP geolocation and anomaly detection has been thoroughly studied, the function of an IP address—e.g., whether it pertains to a private enterprise network or a home broadband—remains underexplored. In this work, we initiate the first attempt to address the IP usage scenario classification problem. We collect data consisting of IP addresses from four large-scale regions. A novel continuous neural tree-based ensemble model is proposed to learn IP assignment rules and complex feature interactions. We conduct extensive experiments to evaluate our model in terms of classification accuracy and generalizability. Our results demonstrate that the proposed model is capable of efficiently uncovering significant higher-order feature interactions that enhance IP usage scenario classification, while also possessing the ability to generalize from the source region to the target one.

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Identifying IP Usage Scenarios: Problems, Data, and Benchmarks

Cited by 3 publications

References 8 publications

A runoff-based hydroelectricity prediction method based on meteorological similar days and XGBoost model

A runoff-based hydroelectricity prediction method based on meteorological similar days and XGBoost model

Key technologies for hybrid energy system planning and operation

Measuring and classifying IP usage scenarios: a continuous neural trees approach

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