標(biāo)題: Titlebook: Statistical Learning Tools for Electricity Load Forecasting; Anestis Antoniadis,Jairo Cugliari,Jean-Michel Pogg Book 2024 The Editor(s) (i [打印本頁] 作者: 宗派 時間: 2025-3-21 17:53
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書目名稱Statistical Learning Tools for Electricity Load Forecasting讀者反饋學(xué)科排名
作者: Bmd955 時間: 2025-3-21 22:05 作者: 去世 時間: 2025-3-22 03:37
veral pathogenic yeasts, including Candida, Cryptococcus, Malassezia and yeasts of emerging importance. The importance of laboratory diagnosis, antifungal susceptibility testing, antifungal resistance and yeast diseases in animals are reviewed..978-3-642-26141-1978-3-642-03150-2Series ISSN 2626-885X Series E-ISSN 2626-8868 作者: Ischemic-Stroke 時間: 2025-3-22 05:30
Anestis Antoniadis,Jairo Cugliari,Matteo Fasiolo,Yannig Goude,Jean-Michel Poggiveral pathogenic yeasts, including Candida, Cryptococcus, Malassezia and yeasts of emerging importance. The importance of laboratory diagnosis, antifungal susceptibility testing, antifungal resistance and yeast diseases in animals are reviewed..978-3-642-26141-1978-3-642-03150-2Series ISSN 2626-885X Series E-ISSN 2626-8868 作者: ovation 時間: 2025-3-22 10:43
Anestis Antoniadis,Jairo Cugliari,Matteo Fasiolo,Yannig Goude,Jean-Michel Poggiveral pathogenic yeasts, including Candida, Cryptococcus, Malassezia and yeasts of emerging importance. The importance of laboratory diagnosis, antifungal susceptibility testing, antifungal resistance and yeast diseases in animals are reviewed..978-3-642-26141-1978-3-642-03150-2Series ISSN 2626-885X Series E-ISSN 2626-8868 作者: 袋鼠 時間: 2025-3-22 15:52 作者: considerable 時間: 2025-3-22 17:45 作者: ARENA 時間: 2025-3-23 00:45 作者: 使厭惡 時間: 2025-3-23 03:37 作者: 災(zāi)禍 時間: 2025-3-23 05:34 作者: 打擊 時間: 2025-3-23 10:14 作者: Pulmonary-Veins 時間: 2025-3-23 15:51 作者: 解凍 時間: 2025-3-23 21:48
Short-Term Electricity Load Forecasting for Fine-Grained Data with PLAM additive components. However, lower aggregation levels result in high-resolution data that is highly volatile, and forecasting the average load using GAM models with smooth components does not provide meaningful information about the future demand. To enhance the forecast accuracy, we need to incor作者: 有說服力 時間: 2025-3-23 22:18 作者: 腐敗 時間: 2025-3-24 06:11
Anestis Antoniadis,Jairo Cugliari,Matteo Fasiolo,Yannig Goude,Jean-Michel Poggi作者: VEIL 時間: 2025-3-24 08:22 作者: 虛弱的神經(jīng) 時間: 2025-3-24 13:47 作者: Cirrhosis 時間: 2025-3-24 15:45
Anestis Antoniadis,Jairo Cugliari,Matteo Fasiolo,Yannig Goude,Jean-Michel Poggi作者: crescendo 時間: 2025-3-24 21:04
Anestis Antoniadis,Jairo Cugliari,Matteo Fasiolo,Yannig Goude,Jean-Michel Poggi作者: EWER 時間: 2025-3-25 00:36
Statistical Learning Tools for Electricity Load Forecasting作者: 倔強不能 時間: 2025-3-25 04:02
2662-5555 ion of relevant variables for prediction, construction of prediction bands, peak demand prediction, and use of individual consumer data...This text is intended for practitioners, researchers, and post-graduate 978-3-031-60341-9978-3-031-60339-6Series ISSN 2662-5555 Series E-ISSN 2662-5563 作者: ensemble 時間: 2025-3-25 11:27
978-3-031-60341-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: spondylosis 時間: 2025-3-25 12:26 作者: 騷擾 時間: 2025-3-25 19:22 作者: 別名 時間: 2025-3-25 23:03
Functional State Space ModelsThe aim of this case study is to present an adaptation of the functional time series (FTS) framework described before, in order to obtain a setting well suited to handle state space models. We recall that the FTS allows us to capture in a natural way the underlying continuous nature of the electrical load curve.作者: Entrancing 時間: 2025-3-26 00:39
Anestis Antoniadis,Jairo Cugliari,Jean-Michel PoggIntroduces modern forecasting methods and tools for creating customized electricity forecasting models.Demonstrates implementation of modeling strategies using real-world data together with relevant R作者: Sigmoidoscopy 時間: 2025-3-26 04:28
Statistics for Industry, Technology, and Engineeringhttp://image.papertrans.cn/s/image/876459.jpg作者: Binge-Drinking 時間: 2025-3-26 09:46 作者: 不適 時間: 2025-3-26 13:52 作者: preeclampsia 時間: 2025-3-26 18:50 作者: MAL 時間: 2025-3-26 23:33
Introduction,sis on their functional data analysis aspect. The book should be useful beyond the electrical context because it discusses methods and models that extend to other applications, such as forecasting of seasonal phenomena, possibly influenced by external factors (e.g., call centers activity, public hot water supply, airport passenger traffic, etc.).作者: 逗它小傻瓜 時間: 2025-3-27 04:02
Aggregation of Expertsstic assumptions are made on the data generative process, and the theoretical developments are based on the theory of individual sequences where the goal is to derive aggregation strategies achieving good forecasting results on all sequences of observations.作者: Between 時間: 2025-3-27 05:58 作者: Neolithic 時間: 2025-3-27 11:50 作者: V切開 時間: 2025-3-27 17:41
Forecasting Daily Peak Demand Using GAMse same time resolution as the demand (e.g., half-hourly temperatures) or less frequently (e.g., the day of the week). Here we consider the problem of forecasting the size and time of the daily peak demand which are, of course, observed on a daily basis, using covariates that are observed more frequently.作者: 壓迫 時間: 2025-3-27 21:48 作者: CURB 時間: 2025-3-28 00:13
Introduction,ial and scientific applications. We illustrate the basic theory and practical utility of several up-to-date statistical methods, with particular emphasis on their functional data analysis aspect. The book should be useful beyond the electrical context because it discusses methods and models that ext作者: 文藝 時間: 2025-3-28 03:52
Additive Modelling of Electricity Demand with se distribution and several covariates is modelled nonparametrically, typically via spline bases expansions. Here we focus on standard GAMs, where only one parameter of the response distribution (typically controlling the mean or location) is modelled additively, while the remaining parameters do no作者: 露天歷史劇 時間: 2025-3-28 08:46 作者: 同時發(fā)生 時間: 2025-3-28 12:49 作者: 財政 時間: 2025-3-28 15:20 作者: Neuralgia 時間: 2025-3-28 22:08
Mixed Effects Models for Electricity Load Forecastingodels that have some connection to some of the other approaches that were developed in this part, such as semi-parametric approaches using either the semi-parametric generalized additive models (GAM) or random forests based machine learning techniques and which have produced relatively parsimonious 作者: 發(fā)誓放棄 時間: 2025-3-29 00:10 作者: 愛好 時間: 2025-3-29 06:24 作者: LAITY 時間: 2025-3-29 10:04
Short-Term Electricity Load Forecasting for Fine-Grained Data with PLAMstems and grid management. While electricity load forecasting at the aggregate level across many households has been extensively studied, electrical load forecasting at fine-grained geographical scales of households, which is the case studied in this chapter, is more difficult as we move toward lowe作者: amphibian 時間: 2025-3-29 14:19 作者: 貪婪性 時間: 2025-3-29 17:50
Forecasting During the Lockdown Periods are closed and citizens are ordered to stay at home. One of the consequences of this policy is a significant change in electricity consumption patterns. Load forecasting models often need years of data to achieve good performances, and they are thus slow to react to such sudden changes. As present作者: glacial 時間: 2025-3-29 22:07 作者: 散步 時間: 2025-3-30 00:21
Random Forestsng. They are now one of the favorite methods in the toolbox of statisticians..Let us start this chapter by describing RF in the classical regression framework, without any reference to the time series context. Since forests are made of trees, we also briefly mention the CART (Classification and Regression Trees) algorithm.作者: Antagonism 時間: 2025-3-30 04:50
Aggregation of Multiscale Experts for Bottom-Up Load Forecastingtions in their area and survey data on their electricity equipment, social class, or building characteristics. In this context, we propose an online learning approach to forecast the total consumption of this group exploiting individual load measurements in real time.作者: MOAN 時間: 2025-3-30 09:46
rtance.Reviews the importance of laboratory diagnosis, antif.Mycological studies of yeasts are entering a new phase, with the sequencing of multiple fungal genomes informing our understanding of their ability to cause disease and interact with the host. At the same time, the ongoing use of tradition
