Although the Photovoltaic energy production has not yet an expressive share when compared to traditional sources, it is estimated, as seen previously, that it will rise in importance in the coming years. This kind of energy has insurmountable capabilities to explore, and is the biggest source of energy mankind has access to. Also, the continuous pressures of environmental institutions backed by Kyoto’s protocol, makes it a priority to develop and increase the use of renewable energies. Thus, power production forecasting is increasingly gaining importance, as a tool to improve the control capability and usability of these energies, once they are very intermittent and hard to predict.
The main objective of the present thesis was to develop a new and innovative forecasting method, for short-term photovoltaic forecasting, in this case for a span of 72 hours ahead. The main conclusions are presented next.
A method for photovoltaic power production forecasting has been developed using a relatively new technology called Extreme Learning Machines. The developed method forecasts the photovoltaic power production for the next 72 hours, and a forecast is made for each hour of the span, i.e. 72 different networks were trained and tested. One particularity of the present method is the capability of varying the starting hour of the forecast, i.e. it can start at 0:00 UTC or at 9:00 UTC or at which hour it is preferred or suits better the user.
The method focused on Extreme Learning Machines algorithms analyzed three different forecasting models: One using only data of production past values, i.e. purely autoregressive; another using only Numerical Weather Predictions; and the last one being a hybrid model between the two previously referred models, i.e. using series of past values and NWP. The conclusion was that the hybrid model provides better results, which infers that the use of NWP enhances the results and thus, they add value to the forecasting model.
Also, several NWP variables were tested in order to find the best combination for the present method. The available variables were temperature 2 meters above ground, direct normal irradiance, global horizontal irradiance, cloudiness and solar altitude. It was found that the minimum forecasting error was obtained when a conjugation of direct normal irradiance and global horizontal irradiance was used.
Several activation functions were also tested with the developed model and the best results were achieved when using the sigmoid function.
The developed model was also tested against other statistical models, e.g. ANN and SVM, and better results were achieved when using the same data sets for each model. The ANN obtains slightly worse results than the ELM, while the SVM is clearly the less reliable model in this case. Also, one important feature of the Extreme Learning Machines relating to other methods is its learning speed, which overwhelms the other models. Learning speed is very important characteristic for forecasting models, once it very important that the forecast are ready before the opening of the markets, and thus, the forecasts cannot take too much time to be done.
The available data set for this thesis covers only the span of one year (2010), which is not ideal for this kind of work. To try to lessen the influence of this detail, the training and testing sets were rearranged in order to avoid training the model with mostly summer and spring months and testing with autumn and winter months.
The objectives of the proposed thesis were entirely covered and fulfilled. A forecasting model of Extreme Learning Machines using a hybrid combination of series of past values and NWP was develop and tested, and the results were found fairly good, reaching an error below 6% for the first hour and steadily rising to around 9% for the following 4 hours, once they use series of past power production values as input, for the span of the hours 6-72 the Numerical Weather Predictions were found to be a more relevant input with an error always rounding 11%. Nevertheless further work can be done to improve the model.
