Next Article in Journal
A Novel Methodology for Estimating State-Of-Charge of Li-Ion Batteries Using Advanced Parameters Estimation
Previous Article in Journal
A Review of Lithium-Air Battery Modeling Studies
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Energies 2017, 10(11), 1754;

Quantification of Forecast Error Costs of Photovoltaic Prosumers in Italy

Department of Mechanical, Energy and Management Engineering—DIMEG, University of Calabria, Arcavacata di Rende, 87036 Rende (CS), Italy
Author to whom correspondence should be addressed.
Received: 25 September 2017 / Revised: 13 October 2017 / Accepted: 24 October 2017 / Published: 1 November 2017
Full-Text   |   PDF [2055 KB, uploaded 1 November 2017]   |  


In recent years, the diffusion of electric plants based on renewable non-dispatchable sources has caused large imbalances between the power generation schedule and the actual generation in real time operations, resulting in increased costs for dispatching electric power systems. Although this type of source cannot be programmed, their production can be predicted using soft computing techniques that consider weather forecasts, reducing the imbalance costs paid to the transmission system operator (TSO). The problem is mainly that the forecasting procedures used by the TSO, distribution system operator (DSO) or large producers and they are too expensive, as they use complex algorithms and detailed meteorological data that have to be bought, this can represent an excessive charge for small-scale producers, such as prosumers. In this paper, a cheap photovoltaic (PV) production forecasting method, in terms of reduced computational effort, free-available meteorological data and implementation is discussed, and the economic results regarding the imbalance costs due to the utilization of this method are analyzed. The economic analysis is carried out considering several factors, such as the month, the day type, and the accuracy of the forecasting method. The user can utilize the implemented method to know and reduce the imbalance costs, by adopting particular load management strategies. View Full-Text
Keywords: building integrated PV forecasting; neural network; solar power; imbalance costs building integrated PV forecasting; neural network; solar power; imbalance costs

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Brusco, G.; Burgio, A.; Menniti, D.; Pinnarelli, A.; Sorrentino, N.; Vizza, P. Quantification of Forecast Error Costs of Photovoltaic Prosumers in Italy. Energies 2017, 10, 1754.

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.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top