Uncertainity in Renewable Energy Time Series Prediction using Neural Networks
Sammanfattning: With the increasing demand for solar energy, the forecast of the PV station energy production has to be as precisely as possible. To make the prediction more robust, also correlated infor- mation about the weather can be added to the previous energy production of the PV station. This thesis is part of a project, which has the goal to build an energy marketplace for a smart energy grid between households. To make the decisions of the prosumer more accurate, a forecast for the PV station energy production has to be as accurate as possible. Because not every household or even some smart grids will contain a weather station, also interpolated weather information has to be considered. The objective of this work is the evaluation of the accuracy difference between precise weather information, located directly at the PV station and interpolated weather data. The errors of the data were recorded due to misfunctions in the sensors and were cleared with the usage of winsorization. The unnecessary weather features have been detected with several feature selection methods. For the forecast of the energy production three established machine learning algorithms were used: Random Forest, LSTM and Facebook Prophet. For the com- parison of the performance different performance metrics were used. The validation of the three models was carried out by a walk-forward cross validation with unseen data. Further- more, for each of the two datasets one of the three machine learning model were trained. For the performance measurement i.e., the LSTM model trained on precise weather information also received the interpolated data as an input for the prediction and vice versa. As a conclu- sion, the Random Forest model performed better than the other two model types, with an av- erage normalized error of 0.15. Whereas the LSTM model received an error of 0.37 and the Prophet model 0.58. For the difference between interpolated and actual weather information the results prove, that the uncertainity in those variables also affects the prediction of the PV station energy outcome. The LSTM model MSE increased by 14 percent and the Random Forest results with an increasement of 16 percent. The end of the thesis includes a discussion about the results and possible tasks for future work takes place.
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