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Tesi etd-08292018-092234


Thesis type
Tesi di laurea magistrale
Author
CAMPINOTTI, DAVIDE
URN
etd-08292018-092234
Title
Multi-step ahead short-term wind power forecasting for a wind farm exploiting machine learning algorithms
Struttura
INGEGNERIA DELL'ENERGIA, DEI SISTEMI, DEL TERRITORIO E DELLE COSTRUZIONI
Corso di studi
INGEGNERIA ELETTRICA
Commissione
relatore Tucci, Mauro
tutor Betti, Alessandro
Parole chiave
  • machine learning
  • neural network
  • svm
  • wind power nowcast
  • short term forecast
Data inizio appello
28/09/2018;
Consultabilità
parziale
Data di rilascio
28/09/2021
Riassunto analitico
In the wake of the ever growing level of wind power penetration into the electric grid, many a challenge has been posed to the Transmission System Operators (TSOs) which aim is to guarantee both the system quality and reliability. The presence of a very variable and hardly predictable variable as wind is, may jeopardize the balance that should be kept between production and consumption lest it occur a frequency transient. <br><br>Aim of this work, is to predict with the due accuracy the electric power generated up to 6 hours ahead with a sampling rate of 10 minutes of a 18 MW wind farm located in the south of Italy. <br><br><br>To this end both historic and NWP data have been exploited and, albeit some researchers had considered the latter of no avail for such a short time horizon, this work has widely shown the opposite.<br>These data, especially historic data, have undergone an intensive pre-processing phase so as to get rid of not meaningful data and compute their availability.<br>The pre-processed data have been fed to a bunch of selected machine learning algorithms whose training strategies had been previously evaluated on a validation set in order to tune accurately their hyper-parameters and find the most performing ones.<br><br>A ensemble structure have thus been developed to use these algorithms and obtain a even better predictive performance taking advantage of the single algorithm best features and compensating its weaknesses.<br><br>Eventually, a post-process phase occurs whence, inter alia, a metric error has been evaluated to provide a way to quantitatively compare the performance of competing models amongst each others and with the benchmark.
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