Abstract
Short-term power load prediction is crucial for scheduling generators, securing transmission capacity, and determining economic market price. This paper proposes a Bigdata-based Artificial Neural Network (B-ANN) model for a short-term load forecasting by utilizing decision-tree method. The bigdata has been composed of 2018 time index, meteorological data, exchange rates, oil price, and past power demand in Seoul. The decision-tree method is applied to classify the data in accordance with the value of decreasing entropy. In addition, the resilient back propagation algorithm (RPROP) is applied for ANN that derives fast results and result in reduced error through variable learning rates. The applicability and effectiveness of the proposed model are verified by conducting simulations for forecasting 1-day hourly power load. The results confirm that the proposed model has decreased the mean absolute percentage error (MAPE) dramatically in comparison with other forecasting methods such as multiple regression analysis, time series analysis, and auto-regressive integrated moving average (ARIMA) model.
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