6533b7d9fe1ef96bd126bfcd

RESEARCH PRODUCT

Comparing Recurrent Neural Networks using Principal Component Analysis for Electrical Load Predictions

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Electrical demand forecasting is essential for power generation capacity planning and integrating environment-friendly energy sources. In addition, load predictions will help in developing demand-side management in coordination with renewable power generation. Meteorological conditions influence urban area load pattern; therefore, it is vital to include weather parameters for load predictions. Machine Learning algorithms can effectively be used for electrical load predictions considering impact of external parameters. This paper explores and compares the basic Recurrent Neural Networks (RNN); Simple Recurrent Neural Networks (Vanilla RNN), Gated Recurrent Units (GRU), and Long Short-Term Memory networks (LSTM). Vanilla RNNs are fully connected neural networks where the output from the previous time step is being fed to the next time step. GRUs are networks with a gating mechanism: a forget gate. LSTM networks also, in addition to a forget gate, include an output gate. Even though the recurrent structure in itself is robust for efficient forecasting, pre-processing of data (including load, weather) is important to enhance the performance. Principal Component Analysis (PCA) reduces and extracts the main components of available data. This work shows that PCA improves the performance of RNNs with use of weather parameters. The historical electrical load dataset from Sydney region is used to test the load forecasting using these techniques considering meteorological parameters. Through load forecasting, it is observed that for the 30 minutes predictions, GRU trained with a reduced number of principal components performs best for a typical period with a mean absolute percentage error (MAPE) of 0.74%.