Development and testing of a micro-grid excess power production forecasting algorithms. (October 2017)

• Record Type: Journal Article
• Authors: Mavrigiannaki, Angeliki; Kampelis, Nikos; Kolokotsa, Denia; Marchegiani, Daniele; Standardi, Laura; Isidori, Daniela; Christalli, Cristina
• Is Part Of: Energy procedia. Volume 134(2017)
• Page Start: 654
• Page End: 663
• Publication Date: 2017-10
• Subjects: micro-grid -- forecasting -- integration -- artificial neural network; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2017.09.583
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: Traditional electricity grids lack flexibility in power generation and load operation in contrast to smart-micro grids that form semi-autonomous entities with energy management capabilities. Load forecasting is invaluable to smart micro-grids towards assisting the implementation of energy management schedules for cost-efficient and secure operation. In the present paper is examined the 24h forecasting of excess production in an existing micro-grid. Alternative input parameters are considered for achieving an accurate prediction. The prediction can be used for scheduling the charging process of a thermal storage during weekends based on excess power production levels.