Citation
Bengu Akyurek, Alper Sinan Akyurek, Jan Kleissl, Tajana Simunic Rosing. "TESLA: Taylor Expanded Solar Analog Forecasting". 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), 6, 3, November, 2014.

Abstract
With the increasing penetration of renewable energy resources within the Smart Grid, solar forecasting has become an important problem for hour-ahead and day-ahead planning. Within this work, we analyze the Analog Forecast method family, which uses past observations to improve the forecast product. We first show that the frequently used euclidean distance metric has drawbacks and leads to poor performance relatively. In this paper, we introduce a new method, TESLA forecasting, which is very fast and light, and we show through case studies that we can beat the persistence method, a state of the art comparison method, by up-to 50% in terms of root mean square error to give an accurate forecasting result. An extension is also provided to improve the forecast accuracy by decreasing the forecast horizon.

Electronic downloads

• https://www.src.org/library/publication/p071297/. (Link to paper on SRC website.)
• http://dx.doi.org/10.1109/SmartGridComm.2014.7007634

• HTML

  Bengu Akyurek, Alper Sinan Akyurek, Jan Kleissl, Tajana
  Simunic Rosing. <a
  href="http://www.terraswarm.org/pubs/343.html"
  >TESLA: Taylor Expanded Solar Analog
  Forecasting</a>, 2014 IEEE International Conference on
  Smart Grid Communications (SmartGridComm), 6, 3, November,
  2014.

• Plain text

  Bengu Akyurek, Alper Sinan Akyurek, Jan Kleissl, Tajana
  Simunic Rosing. "TESLA: Taylor Expanded Solar Analog
  Forecasting". 2014 IEEE International Conference on
  Smart Grid Communications (SmartGridComm), 6, 3, November,
  2014.

• BibTeX

  @inproceedings{AkyurekAkyurekKleisslRosing14_TESLATaylorExpandedSolarAnalogForecasting,
      author = {Bengu Akyurek and Alper Sinan Akyurek and Jan
                Kleissl and Tajana Simunic Rosing},
      title = {TESLA: Taylor Expanded Solar Analog Forecasting},
      booktitle = {2014 IEEE International Conference on Smart Grid
                Communications (SmartGridComm)},
      pages = {6},
      day = {3},
      month = {November},
      year = {2014},
      abstract = {With the increasing penetration of renewable
                energy resources within the Smart Grid, solar
                forecasting has become an important problem for
                hour-ahead and day-ahead planning. Within this
                work, we analyze the Analog Forecast method
                family, which uses past observations to improve
                the forecast product. We first show that the
                frequently used euclidean distance metric has
                drawbacks and leads to poor performance
                relatively. In this paper, we introduce a new
                method, TESLA forecasting, which is very fast and
                light, and we show through case studies that we
                can beat the persistence method, a state of the
                art comparison method, by up-to 50% in terms of
                root mean square error to give an accurate
                forecasting result. An extension is also provided
                to improve the forecast accuracy by decreasing the
                forecast horizon.},
      URL = {http://terraswarm.org/pubs/343.html}
  }
  

Posted by Alper Sinan Akyurek on 8 Aug 2014.