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Electricity demand forecasting over Italy: Potential benefits using numerical weather prediction models

TitleElectricity demand forecasting over Italy: Potential benefits using numerical weather prediction models
Publication TypeArticolo su Rivista peer-reviewed
Year of Publication2013
AuthorsDe Felice, Matteo, Alessandri Andrea, and Ruti P.M.
JournalElectric Power Systems Research
Volume104
Pagination71-79
ISSN03787796
KeywordsDaily load forecasting, Electric load forecasting, Electricity, Electricity demand forecasting, Load forecasting, Mathematical models, Meteorology, Numerical weather prediction models, Operational forecasts, Performance improvements, Weather data, Weather forecast models, Weather forecasting
Abstract

Electricity demand forecasting is a critical task for energy management of power grids. Due to the wide use of refrigeration and residential air-conditioning devices, electricity demand in Italy is influenced by weather conditions, especially during summer. This paper performs daily load forecasting for Italy through statistical modeling with the aim of studying the influence of temperature. The actual capability of available weather forecasts to contribute in predicting electricity loads is evaluated by using weather data from numerical weather prediction (NWP) models. Time-series models have been used and compared with a naive predictor on working-days daily load during June and July in years 2003-2009 considering lead-times between one and five days. Results are analyzed both at the national level and at regional scale, using unprecedented historical load data provided by the Italian transmission grid manager. It is shown that the use of weather data provided by NWP models leads to performance improvements, especially for the hottest areas where the use of electricity is more heavily influenced by temperature. Furthermore, by observing the gap between load forecast models using reanalysis and operational forecast weather data we can obtain some clues about the limitations of the weather forecast models we used on specific geographic areas in Italy. © 2013 Elsevier B.V.

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URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84880749738&doi=10.1016%2fj.epsr.2013.06.004&partnerID=40&md5=bdd04791b75f3b5e1d1a9d08688a9aa4
DOI10.1016/j.epsr.2013.06.004
Citation KeyDeFelice201371