Sammendrag
Hydropower is a major form of energy
production in the Nordic power market. Hence, high accuracy
inflow forecasting plays an essential role in predicting the
power price. This paper proposed PCA-LSTM prediction
model that combines machine learning technology, e.g., long
short-term memory (LSTM), with the data-driven approach,
e.g., principal component analysis (PCA). Based on the
proposed approaches and the available inflow and hydrological
variables, three different scenarios are conducted to validate
the performance. Three performance criteria, i.e., Mean
Relative Error (MRE), Mean Absolute Error (MAE) and Root
Mean Square Error (RMSE), are applied to evaluate the model
performance. The results show that the prediction accuracy is
increased after introducing the hydro-metrological data into
the model. Furthermore, the two-step approach based on the
inflow data and hydro-metrological data has the best
performance. This implies that it is better to apply the data-driven approach for the hydrological data before implementing the machine learning technology
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