AUTHORS:
Lorenzo Gigoni, Alessandro Betti, Emanuele Crisostomi, Alessandro Franco, Mauro Tucci, Fabrizio Bizzarri and Debora Mucci.
ABSTRACT:
The ability to accurately forecast power generation from renewable sources is nowadays recognized as a fundamental skill to improve the operation of power systems. Despite the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, and infer the impact of single components in providing accurate predictions. In this paper, we extensively compare simple forecasting methodologies with more sophisticated ones over 32 photovoltaic (PV) plants of different sizes and technology over a whole year. Also, we try to evaluate the impact of weather conditions and weather forecasts on the prediction of PV power generation.
Solar Forecasting
i-EM contributed to the solar forecasting chapter of NREL report
The Photovoltaic Power System (PVPS) Programme of the International Energy Agency (IEA) has recently focused on the...
i-EM presentation on power generation forecasting at EUPVSEC 2020
As many other events, also the EU PVSEC 2020 will be held completely online next week, from September 7th to 11th. We...
i-EM contributed to IEA PVPS “Regional solar power forecasting” report
The Photovoltaic Power System (PVPS) Programme of the International Energy Agency (IEA) has recently focused on the...
Data Analysis for Solar Forecasting: “(I) Miss Sunshine!”. How to deliver the optimum output from Solar Forecasting
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New webinar video available: demo on Wind and PV Forecasting
This demonstration is focused on wind and solar forecasting, which is one of the topics of our research activity in...
Data Analysis: “The Treasure Hunt”. Uncover hidden gems in your data
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How to deliver the optimum output from wind and PV forecasting
Our next webinar will be focused on wind and solar forecasting, which is one of the topics of our research activity in...
So far, so good. But: failure is coming… Predictive maintenance for PV solar assets
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MOWGLI (MicrO reneWable Grid for ruraL Indian areas) project on ETN magazine
AUTHORS:
Ciro Lanzetta and Fabrizio Ruffini.
ABSTRACT:
The Mowgli feasibility study started in 2018 funded by the European Space Agency (ESA) with the involvement of the India Energy Storage Alliance (IESA) as stakeholder. The aim is to evaluate the technical and economic feasibility of satellite-based services for microgrids. Designed by i-EM, Mowgli is a solution that provides a set of services for optimal microgrid planning, designing and operational and maintenance (O&M) applications in urban and rural areas developing countries, with focus on India as a user case.
A Machine Learning model for long-term power generation forecasting at bidding zone level
AUTHORS:
Michela Moschella, Alessandro Betti, Emanuele Crisostomi and Mauro Tucci.
ABSTRACT:
The increasing penetration level of energy generation from renewable sources is demanding for more accurate and reliable forecasting tools to support classic power grid operations (e.g., unit commitment, electricity market clearing or maintenance planning). For this purpose, many physical models have been employed, and more recently many statistical or machine learning algorithms, and data-driven methods in general, are becoming subject of intense research. While generally the power research community focuses on power forecasting at the level of single plants, in a short future horizon of time, in this time we are interested in aggregated macro-area power generation (i.e., in a territory of size greater than 100000 km2) with a future horizon of interest up to 15 days ahead. Real data are used to validate the proposed forecasting methodology on a test set of several months.
A Decision Support System based on Earth observation exploitation for renewable energy plants management
AUTHORS:
Andrea Masini, Ciro Lanzetta, Giuseppe Leotta, Gian Lorenzo Giuliattini Burbui, Pasquale Guerrisi and Maria Luisa Lo Trovato.
ABSTRACT:
Nowadays remote sensing information can be exploited to support the management of renewable energy plants during all its life cycles. Earth observation satellite can provide a continuous monitoring of each location of the earth. Low-resolution and high-resolution imagery can be exploited to obtain accurate descriptions of the monitored scenarios/plants. Moreover, the use of unmanned air vehicle can provide complementary information to monitor important features not detectable with satellite sensors. i-EM and Flyby developed and tested a system, called 4D-REDSS (4D Renewable plants Decision Support System) aimed to exploit remote sensed data to advantage and support the management of solar plant during the construction and pre-commissioning phase.
Photovoltaic power forecasting with ensemble of learners: large test case from PV plants in Italy, Zambia and Australia
AUTHORS:
Lorenzo Gigoni, Alessandro Betti, Fabrizio Ruffini, Ciro Lanzetta, Antonio Piazzi, Mauro Tucci e Michela Moschella.
ABSTRACT:
An innovative ensamble-based forecasting method based on a number of machine learning techniques independently trained and combined in a cooperative ensemble fashion.
Day-ahead hourly forecasting of power generation from photovoltaic plants
AUTHORS:
Lorenzo Gigoni, Alessandro Betti, Emanuele Crisostomi, Alessandro Franco, Mauro Tucci, Fabrizio Bizzarri and Debora Mucci.
ABSTRACT:
The ability to accurately forecast power generation from renewable sources is nowadays recognized as a fundamental skill to improve the operation of power systems. Despite the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, and infer the impact of single components in providing accurate predictions. In this paper, we extensively compare simple forecasting methodologies with more sophisticated ones over 32 photovoltaic (PV) plants of different sizes and technology over a whole year. Also, we try to evaluate the impact of weather conditions and weather forecasts on the prediction of PV power generation.