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Energies 2013, 6(5), 2624-2643; doi:10.3390/en6052624
Article

Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity

1
, 1
, 2
, 3,*  and 2
1 Faculty of Engineering, University of Porto, Dr. Roberto Frias, Porto s/n 4200-465, Portugal 2 Electrical Engineering Department, University of La Rioja, Luis de Ulloa 20, LogroƱo 26004, Spain 3 Electrical Engineering Department, University of Zaragoza, Maria de Luna 3, Zaragoza 50018, Spain
* Author to whom correspondence should be addressed.
Received: 28 December 2012 / Revised: 15 May 2013 / Accepted: 15 May 2013 / Published: 22 May 2013
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
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Abstract

This paper proposes a new model for short-term forecasting of electric energy production in a photovoltaic (PV) plant. The model is called HIstorical SImilar MIning (HISIMI) model; its final structure is optimized by using a genetic algorithm, based on data mining techniques applied to historical cases composed by past forecasted values of weather variables, obtained from numerical tools for weather prediction, and by past production of electric power in a PV plant. The HISIMI model is able to supply spot values of power forecasts, and also the uncertainty, or probabilities, associated with those spot values, providing new useful information to users with respect to traditional forecasting models for PV plants. Such probabilities enable analysis and evaluation of risk associated with those spot forecasts, for example, in offers of energy sale for electricity markets. The results of spot forecasting of an illustrative example obtained with the HISIMI model for a real-life grid-connected PV plant, which shows high intra-hour variability of its actual power output, with forecasting horizons covering the following day, have improved those obtained with other two power spot forecasting models, which are a persistence model and an artificial neural network model.
Keywords: power forecasting; solar energy; data mining; genetic algorithm power forecasting; solar energy; data mining; genetic algorithm
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.

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Monteiro, C.; Santos, T.; Fernandez-Jimenez, L.A.; Ramirez-Rosado, I.J.; Terreros-Olarte, M.S. Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity. Energies 2013, 6, 2624-2643.

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