Next Article in Journal
An IoT Architecture for Continuous Livestock Monitoring Using LoRa LPWAN
Previous Article in Journal
WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning
Previous Article in Special Issue
LLC Resonant Voltage Multiplier-Based Differential Power Processing Converter Using Voltage Divider with Reduced Voltage Stress for Series-Connected Photovoltaic Panels under Partial Shading
Open AccessArticle

Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study

1
Department of Energy, Politecnico di Milano, 20156 Milano, Italy
2
Department of Electrical Engineering, Higher Polytechnic School of Algeciras, University of Cadiz, 11202 Algeciras (Cádiz), Spain
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Electronics 2019, 8(12), 1434; https://doi.org/10.3390/electronics8121434
Received: 24 October 2019 / Revised: 18 November 2019 / Accepted: 20 November 2019 / Published: 1 December 2019
(This article belongs to the Special Issue Emerging Technologies for Photovoltaic Solar Energy)
Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG L a b 2 ) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications. View Full-Text
Keywords: photovoltaic; power forecast; day ahead; artificial neural network; short term photovoltaic; power forecast; day ahead; artificial neural network; short term
Show Figures

Figure 1

MDPI and ACS Style

Nespoli, A.; Mussetta, M.; Ogliari, E.; Leva, S.; Fernández-Ramírez, L.; García-Triviño, P. Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study. Electronics 2019, 8, 1434.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop