Titlebook: Energy Time Series Forecasting; Efficient and Accura Lars Dannecker Book 2015 Springer Fachmedien Wiesbaden 2015 Der Europ?ische Strommarkt

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書目名稱Energy Time Series Forecasting影響因子(影響力)




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The Online Forecasting Process: Efficiently Providing Accurate Predictions,ocess that tackles these challenges and requirements. The online forecasting process uses forecast model materialization in conjunction with flexible, iterative parameter estimation to rapidly provide accurate forecasts in an application-aware manner.

RENAL

Optimizations on the Logical Layer: Context-Aware Forecasting, domain we can observe a strong dependence of the time series development on external factors and background processes (compare Section 3.1.3). The energy domain is unique in the fact that we can observe most of this time series context. We propose to exploit this information to further improve the online forecasting process.

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OUTRE

Conclusion and Future Work, new requirements on the forecasting calculation process. The electricity market changed from a static oneday ahead market to a more flexible and dynamic trading in real-time. In this markets the number of market participants substantially increased and deals can be closed shortly before the target fulfillment date of the negotiated contract.

generic

Book 2015the move from static day ahead electricity trading towards more dynamic real-time marketplaces. The online forecasting process is realized by a number of approaches on the logical as well as on the physical layer that we introduce in the course of this book..Nominated for the Georg-Helm-Preis 2015 awarded by the Technische Universit?t Dresden..

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cogent

mproves how forecasts are calculated. It increases forecasting efficiency and accuracy, as well as allowing the process to adapt to different situations and applications. Improving the forecasting efficiency is a key pre-requisite for ensuring stable electricity grids in the face of an increasing am

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肥料

Strategisches Management — Grundlagenocess that tackles these challenges and requirements. The online forecasting process uses forecast model materialization in conjunction with flexible, iterative parameter estimation to rapidly provide accurate forecasts in an application-aware manner.