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Selected Papers from the 20th IEEE International Conference on Environment...
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Selected Papers from the 20th IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020) Special Session “Forecasting & Prognostic in Power Systems”

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Power and Energy Forecasting".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 6994

Special Issue Editors

Prof. Dr. Sonia Leva

E-Mail Website1 Website2
Guest Editor
Department of Energy, Politecnico di Milano, 20156 Milan, Italy
Interests: photovoltaic system; grid; power sharing; inverters; forecasting; nowcasting; machine learning; degradation; battery management systems; polymer solar cells; organic photovoltaics; electric vehicle; vehicle-to-grid; microgrid; energy systems; maximum power point trackers; electric power plant loads; electricity price; power markets; heterogeneous networks; base stations; energy efficiency; life cycle assessment; wind power; regenerative braking; bicycles; motorcycles; car sharing; autonomous vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Grimaccia

E-Mail Website
Guest Editor
Department of Energy, Politecnico Di Milano, Via Lambruschini 4, I-20156 Milano, Italy
Interests: electricity production and load forecasting; RES forecasting; evolutionary computation; energy harvesting devices (EHDs); unmanned aerial systems (UASs) for energy applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special EEEIC session is about power forecasting and prognostic techniques in power systems. The main topics can be summarized as follows:

Energy Forecasting:

Forecasting of intermittent energy resources;

Wind and solar power forecasting;

Forecasting of demand (load) and price of electricity;

Load and generation forecasting in smart grid and microgrids;

Forecasting methods in energy planning models.

Forecasting of Remaining Useful Life:

Forecasting methods for reliability;

Maintenance and safety;

Smart device and prediction of system reliability;

Prognostics and system health management;

Predictive and prescriptive maintenance.

Prof. Dr. Sonia Leva
Prof. Francesco Grimaccia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

11 pages, 409 KiB  
Article
Electrical Load Forecast by Means of LSTM: The Impact of Data Quality
by Alfredo Nespoli, Emanuele Ogliari, Silvia Pretto, Michele Gavazzeni, Sonia Vigani and Franco Paccanelli
Forecasting 2021, 3(1), 91-101; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3010006 - 08 Feb 2021
Cited by 19 | Viewed by 2927
Abstract
Accurate forecast of aggregate end-users electric load profiles is becoming a hot topic in research for those main issues addressed in many fields such as the electricity services market. Hence, load forecast is an extremely important task which should be understood more in [...] Read more.
Accurate forecast of aggregate end-users electric load profiles is becoming a hot topic in research for those main issues addressed in many fields such as the electricity services market. Hence, load forecast is an extremely important task which should be understood more in depth. In this research paper, the dependency of the day-ahead load forecast accuracy on the basis of the data typology employed in the training of LSTM has been inspected. A real case study of an Italian industrial load with samples recorded every 15 min for the year 2017 and 2018 was studied. The effect in the load forecast accuracy of different dataset cleaning approaches was investigated. In addition, the Generalised Extreme Studentized Deviate hypothesis testing was introduced to identify the outliers present in the dataset. The populations were constructed on the basis of an autocorrelation analysis that allowed for identifying a weekly correlation of the samples. The accuracy of the prediction obtained from different input dataset has been therefore investigated by calculating the most commonly used error metrics, showing the importance of data processing before employing them for load forecast. Full article
(This article belongs to the Special Issue Selected Papers from the 20th IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020) Special Session “Forecasting & Prognostic in Power Systems”)
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17 pages, 3874 KiB  
Article
Sun Position Identification in Sky Images for Nowcasting Application
by Alessandro Niccolai and Alfredo Nespoli
Forecasting 2020, 2(4), 488-504; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast2040026 - 16 Nov 2020
Cited by 6 | Viewed by 3363
Abstract
Very-short-term photovoltaic power forecast, namely nowcasting, is gaining increasing attention to face grid stability issues and to optimize microgrid energy management systems in the presence of large penetration of renewable energy sources. In order to identify local phenomena as sharp ramps in photovoltaic [...] Read more.
Very-short-term photovoltaic power forecast, namely nowcasting, is gaining increasing attention to face grid stability issues and to optimize microgrid energy management systems in the presence of large penetration of renewable energy sources. In order to identify local phenomena as sharp ramps in photovoltaic production, whole sky images can be used effectively. The first step in the implementation of new and effective nowcasting algorithms is the identification of Sun positions. In this paper, three different techniques (solar angle-based, image processing-based, and neural network-based techniques) are proposed, described, and compared. These techniques are tested on real images obtained with a camera installed at SolarTechLab at Politecnico di Milano, Milan, Italy. Finally, the three techniques are compared by introducing some performance parameters aiming to evaluate of their reliability, accuracy, and computational effort. The neural network-based technique obtains the best performance: in fact, this method is able to identify accurately the Sun position and to estimate it when the Sun is covered by clouds. Full article
(This article belongs to the Special Issue Selected Papers from the 20th IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020) Special Session “Forecasting & Prognostic in Power Systems”)
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