標(biāo)題: Titlebook: Data Analytics in Power Markets; Qixin Chen,Hongye Guo,Yi Wang Book 2021 Science Press 2021 Power markets.bidding strategy.machine learnin [打印本頁] 作者: 僵局 時(shí)間: 2025-3-21 19:20
書目名稱Data Analytics in Power Markets影響因子(影響力)
書目名稱Data Analytics in Power Markets影響因子(影響力)學(xué)科排名
書目名稱Data Analytics in Power Markets網(wǎng)絡(luò)公開度
書目名稱Data Analytics in Power Markets網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Analytics in Power Markets被引頻次
書目名稱Data Analytics in Power Markets被引頻次學(xué)科排名
書目名稱Data Analytics in Power Markets年度引用
書目名稱Data Analytics in Power Markets年度引用學(xué)科排名
書目名稱Data Analytics in Power Markets讀者反饋
書目名稱Data Analytics in Power Markets讀者反饋學(xué)科排名
作者: 性冷淡 時(shí)間: 2025-3-21 20:43 作者: Radiation 時(shí)間: 2025-3-22 03:54 作者: Ingratiate 時(shí)間: 2025-3-22 06:30
Book 2021oaches. It begins with an overview of the power market data and analyzes on their characteristics and importance for market clearing. Then, the first part of the book discusses the essential problem of bus load forecasting from the perspective of market organizers. The related works include load unc作者: PAC 時(shí)間: 2025-3-22 11:50 作者: Moderate 時(shí)間: 2025-3-22 16:29 作者: Moderate 時(shí)間: 2025-3-22 19:50
https://doi.org/10.1007/978-3-642-74748-9 obtained. On this basis, different quantile regression models are implemented to combine these point forecasts in order to form the final probabilistic forecasts. Case studies on a real-world dataset demonstrate the superiority of our proposed method.作者: Anthrp 時(shí)間: 2025-3-22 21:17
https://doi.org/10.1007/978-3-030-38456-2 to detect local fluctuation. How the data cleaning influences the forecasting performance is also investigated. Case studies on the load data of Fujian Province, China are conducted to verify the effectiveness of the proposed method.作者: 擁護(hù) 時(shí)間: 2025-3-23 02:33 作者: Notify 時(shí)間: 2025-3-23 09:22
Load Data Cleaning and Forecasting to detect local fluctuation. How the data cleaning influences the forecasting performance is also investigated. Case studies on the load data of Fujian Province, China are conducted to verify the effectiveness of the proposed method.作者: 細(xì)絲 時(shí)間: 2025-3-23 11:48
Introduction to Power Market Data,matic changes for system operators, generation companies, and electricity consumers. The operation of power markets constantly produces valuable market data which can support the decision of both market organizers and market participants. This chapter presents an introduction to power market data. F作者: 培養(yǎng) 時(shí)間: 2025-3-23 14:33 作者: 紋章 時(shí)間: 2025-3-23 20:48
Load Data Cleaning and Forecastingally due to cyber attacks and equipment failures. The bad data may result in bias for load forecasting and other data analytic applications. This chapter proposes a novel bad data identification and repairing method for load profiles. In the first stage, the Singular Value Thresholding (SVT) algorit作者: Progesterone 時(shí)間: 2025-3-23 23:54
Monthly Electricity Consumption Forecastingctricity consumption. To improve the accuracy and applicability of mid-term, especially monthly, electricity consumption forecasting, a novel monthly electricity consumption forecasting framework (denoted as SAS-SVECM for short) based on vector error correction model (VECM) and self-adaptive screeni作者: generic 時(shí)間: 2025-3-24 03:01 作者: FLUSH 時(shí)間: 2025-3-24 06:30 作者: thrombus 時(shí)間: 2025-3-24 13:38
Day-Ahead Electricity Price Forecastingt. Most electricity market organizers in the world release the data of LMP along with its three components, i.e., the energy, congestion, and loss components. The series of the three components have their own patterns and driving factors, and can be utilized to improve the accuracy of LMP forecastin作者: 腫塊 時(shí)間: 2025-3-24 17:13 作者: 有助于 時(shí)間: 2025-3-24 22:32 作者: 清晰 時(shí)間: 2025-3-25 01:18 作者: 偏離 時(shí)間: 2025-3-25 05:35
Aggregated Supply Curves Forecastingfficult to directly forecast the rivals’ individual bids due to the information privacy and volatile characteristics of individual bidding behaviors. From another point of view, the aggregation of individual bids, denoted as aggregated supply curve (ASC), might be helpful to offset the uncertainties作者: Connotation 時(shí)間: 2025-3-25 09:56 作者: Kidnap 時(shí)間: 2025-3-25 12:04
Reward Function Identification of?GENCOss accurately defining the individual reward function (or objective function). Considering the information barriers between market participants and researchers, the common way is to develop reward functions based on theoretical assumptions, which will inevitably cause deviations from the real world. 作者: 改革運(yùn)動(dòng) 時(shí)間: 2025-3-25 16:23 作者: 知識(shí)分子 時(shí)間: 2025-3-25 21:11 作者: 勉強(qiáng) 時(shí)間: 2025-3-26 03:25
https://doi.org/10.1007/978-981-16-4975-2Power markets; bidding strategy; machine learning; price forecasting; load forecasting作者: 舊病復(fù)發(fā) 時(shí)間: 2025-3-26 04:40 作者: lattice 時(shí)間: 2025-3-26 09:01
Correction to: Introduction to Power Market Data,In the original version of the book, the following belated corrections have been made作者: Acetaldehyde 時(shí)間: 2025-3-26 15:35
Meredith E. Safran,Monica S. Cyrinomatic changes for system operators, generation companies, and electricity consumers. The operation of power markets constantly produces valuable market data which can support the decision of both market organizers and market participants. This chapter presents an introduction to power market data. F作者: 欺騙世家 時(shí)間: 2025-3-26 17:12 作者: 宣稱 時(shí)間: 2025-3-26 23:23 作者: defile 時(shí)間: 2025-3-27 04:39 作者: outrage 時(shí)間: 2025-3-27 05:45 作者: ABHOR 時(shí)間: 2025-3-27 10:48
Parabolic Potential Theory: Basic Factsstigates the fundamental distribution of the congestion part of LMPs in high-dimensional Euclidean space using an unsupervised approach. LMP models based on the lossless and lossy DC optimal power flow (DC-OPF) are analyzed to show the overlapping subspace property of the LMP data. The congestion pa作者: adumbrate 時(shí)間: 2025-3-27 17:05 作者: 群居男女 時(shí)間: 2025-3-27 20:51 作者: 某人 時(shí)間: 2025-3-27 22:51 作者: 獨(dú)特性 時(shí)間: 2025-3-28 02:33 作者: 直覺沒有 時(shí)間: 2025-3-28 08:57 作者: 牢騷 時(shí)間: 2025-3-28 14:00
Distribution Functions of Dynamic Systemscurrent commonly used methods, which are equilibrium analysis and agent-based simulation, a data-driven bottom-up power market simulation framework is proposed based on learning from individual offering strategies. In detail, a deep neural network is proposed to learn the relations between the poten作者: enflame 時(shí)間: 2025-3-28 15:27 作者: predict 時(shí)間: 2025-3-28 20:09
Current, Resistance and Circuits,aptively, which appropriately addresses the contradiction between data quantity and data length. The SAS-SVECM achieves significant forecasting accuracy enhancement and good adaptability. Finally, an empirical example, using real monthly electricity consumption and macroeconomic data of China (2000–作者: Original 時(shí)間: 2025-3-29 01:43 作者: 懸崖 時(shí)間: 2025-3-29 05:50 作者: 使腐爛 時(shí)間: 2025-3-29 10:36
Mathematical Functions and Techniquesor level, and such calculation does not require any predefined forecasting results. Numerical results and discussions based on real-market price data are conducted to show the application of the proposed method.作者: 大笑 時(shí)間: 2025-3-29 12:47 作者: 潛伏期 時(shí)間: 2025-3-29 16:25 作者: 痛苦一生 時(shí)間: 2025-3-29 22:27
Distribution Functions of Dynamic Systemsd in this chapter. In detail, A paradigmatic data integration method is proposed to fix the unstructured data formats. A feature extraction method is developed to simplify the high dimensionality of ASC. Then, an LSTM model is customized to forecast ASCs. At last, real data from the Midcontinent Ind作者: FOVEA 時(shí)間: 2025-3-30 03:37 作者: 河潭 時(shí)間: 2025-3-30 07:39 作者: anticipate 時(shí)間: 2025-3-30 11:02 作者: MORT 時(shí)間: 2025-3-30 12:41
supply curve forecasting, market simulation, and reward function identification in bidding. These methods are especially useful for market organizers to understand the bidding behaviors of market participants a978-981-16-4977-6978-981-16-4975-2作者: 頂點(diǎn) 時(shí)間: 2025-3-30 19:48
Monthly Electricity Consumption Forecastingaptively, which appropriately addresses the contradiction between data quantity and data length. The SAS-SVECM achieves significant forecasting accuracy enhancement and good adaptability. Finally, an empirical example, using real monthly electricity consumption and macroeconomic data of China (2000–
