Staff View: Energy time series forecasting : efficient and accurate forecasting of evolving time Series from the energy domain

Energy time series forecasting : efficient and accurate forecasting of evolving time Series from the energy domain

Continuous balancing of electric power consumption and production is a fundamental prerequisite for the stability and efﬁciency of electricity grids. This balancing task requires accurate forecasts of future electricity demand and supply at any point in time. For this purpose, today’s energy data ma...

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Bibliographic Details
Main Author:	Lars Dannecker
Format:	Book
Language:	English
Published:	Springer 2020
Subjects:	Energy, energy consumption, electric power consumption, electricity demand
Online Access:	http://dspace.uniten.edu.my/jspui/handle/123456789/13420
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id	my.uniten.dspace-13420
record_format	dspace
spelling	my.uniten.dspace-134202020-02-17T05:04:43Z Energy time series forecasting : efficient and accurate forecasting of evolving time Series from the energy domain Lars Dannecker Energy, energy consumption, electric power consumption, electricity demand Continuous balancing of electric power consumption and production is a fundamental prerequisite for the stability and efﬁciency of electricity grids. This balancing task requires accurate forecasts of future electricity demand and supply at any point in time. For this purpose, today’s energy data management systems (EDMS) typically use quantitative models—called forecast models—that already provide accurate predictions. However, recent developments in the energy domain such as real-time intra-day trading and the integration of more renewable energy sources also require more efﬁcient forecasting calculations and a rapid provisioning of forecasting results. Furthermore, today’s EDMSs fulﬁll a number of different tasks, each exhibiting different requirements for the calculation of forecasts with respect to runtime and accuracy. Thus, it is necessary to ﬂexibly adapt the forecasting process with respect to the needs of the current requests. In contrast, currently employed forecasting approach esare rather time-consuming and inﬂexible.One reason is the very expensive estimation of the forecast model parameters, involving a large number of simulations in a search space that increases exponential with the number of parameters. 2020-02-10T01:55:23Z 2020-02-10T01:55:23Z 2015 Book http://dspace.uniten.edu.my/jspui/handle/123456789/13420 en Springer
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
language	English
topic	Energy, energy consumption, electric power consumption, electricity demand
spellingShingle	Energy, energy consumption, electric power consumption, electricity demand Lars Dannecker Energy time series forecasting : efficient and accurate forecasting of evolving time Series from the energy domain
description	Continuous balancing of electric power consumption and production is a fundamental prerequisite for the stability and efﬁciency of electricity grids. This balancing task requires accurate forecasts of future electricity demand and supply at any point in time. For this purpose, today’s energy data management systems (EDMS) typically use quantitative models—called forecast models—that already provide accurate predictions. However, recent developments in the energy domain such as real-time intra-day trading and the integration of more renewable energy sources also require more efﬁcient forecasting calculations and a rapid provisioning of forecasting results. Furthermore, today’s EDMSs fulﬁll a number of different tasks, each exhibiting different requirements for the calculation of forecasts with respect to runtime and accuracy. Thus, it is necessary to ﬂexibly adapt the forecasting process with respect to the needs of the current requests. In contrast, currently employed forecasting approach esare rather time-consuming and inﬂexible.One reason is the very expensive estimation of the forecast model parameters, involving a large number of simulations in a search space that increases exponential with the number of parameters.
format	Book
author	Lars Dannecker
author_facet	Lars Dannecker
author_sort	Lars Dannecker
title	Energy time series forecasting : efficient and accurate forecasting of evolving time Series from the energy domain
title_short	Energy time series forecasting : efficient and accurate forecasting of evolving time Series from the energy domain
title_full	Energy time series forecasting : efficient and accurate forecasting of evolving time Series from the energy domain
title_fullStr	Energy time series forecasting : efficient and accurate forecasting of evolving time Series from the energy domain
title_full_unstemmed	Energy time series forecasting : efficient and accurate forecasting of evolving time Series from the energy domain
title_sort	energy time series forecasting : efficient and accurate forecasting of evolving time series from the energy domain
publisher	Springer
publishDate	2020
url	http://dspace.uniten.edu.my/jspui/handle/123456789/13420
_version_	1662758859330027520
score	13.214268

Energy time series forecasting : efficient and accurate forecasting of evolving time Series from the energy domain

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