Forecasting of energy demand in virtual power plants

Khavari, Farshad, Esmaily, Jamal and Shafiekhani, Morteza 2022. Forecasting of energy demand in virtual power plants. Zangeneh, Ali and Moeini-Aghtaie, Moein, eds. Scheduling and Operation of Virtual Power Plants: Technical Challenges and Electricity Markets, Elsevier, pp. 343-358. (10.1016/B978-0-32-385267-8.00020-2)

Full text not available from this repository.

Abstract

Electricity load forecasts can be generated from minutes and hours in advance to years and decades, which consist of short-term load forecasting (STLF), mid-term load forecasting (MTLF), and long-term load forecasting (LTLF). STLF, which is the main topic of this chapter, consists of hourly prediction of the load for a time ranging from one hour to several days. In this chapter, at first, historical weather and load data have been classified, which consists of modeling the data for each class by intervention analysis based on statistical methods and knowledge about electrical demand curves, then a proper tool for load forecasting has been chosen. Also, a real case study based on a modern method has been simulated and discussed.

Item Type:	Book Section
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISBN:	978-0-323-85267-8
Last Modified:	25 Oct 2023 14:00
URI:	https://orca.cardiff.ac.uk/id/eprint/162410

Actions (repository staff only)

Edit Item