Comparison of Deep Learning and Incremental Learning Model for Net Load Forecasting
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Abstract
This paper presents hourly net load forecasting, which is the forecasting of the difference between the hourly demand and the hourly power produced from the Photovoltaic (PV) system, which is the load that the utility should supply to the consumer. By comparing the forecasting of the 3 models, 1) Long Short-Term Memory (LSTM), which is a deep learning model, 2) Fully Online Sequential Extreme Learning Machine (FOS-ELM), which is an incremental learning model that does not require initial training data and 3) Online Sequential Extreme Learning Machine (OS-ELM), a model that can be incrementally learned as FOS-ELM. In addition, we proposed the initial training method for the OS-ELM model by taking the first sample obtained from working to synthesize a sufficient amount of sample for the initial training of the OS-ELM model. It was found from the experiment that in the case of fixed PV penetration rate, the LSTM model had slightly lower of error in forecasting than the other two models. In the case of increasing PV penetration rate, the FOS-ELM, and OS-ELM models, with incremental learning capacity, had significantly lower errors in forecasting than the LSTM model. When comparing only the OS-ELM model using the proposed method with the FOS-ELM model, it was found that the OS-ELM model gave lower errors in forecasting than the FOS-ELM model because it was initially trained by the synthetic sample properly,
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