Peak Load Forecast using Long Short Term Memory Networks.
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I.O.E. Pulchowk Campus
Abstract
Forecasting is the technique of making scientific predictions of the future based on historical data
trends. With increasing smart grid technologies and penetration of intermittent renewable energy
technologies, peak load forecasting has been an important task in recent years for optimizing power
grid operations. In recent years, machine learning has been one of the popular methods for load
forecasting. In this paper, a deep learning single Long Short TermMemory (LSTM) based model is
proposed for predicting the peak load demand of Nepal and compared with other statistical models.
Comparing the evaluation metrics, it is deduced that the proposed LSTM model with 32 LSTM
unit and a lookback of 30 has better forecast accuracy with Mean Absolute Error (MAE) of 34.01
MW, Mean Squared Error(MSE) of 2880.75MW, Root Mean Squared Error(RMSE) of 53.67MW,
Mean Absolute Percent Error(MAPE) of 2.950% and 𝑅2 Score of 0.933. Among the statistical
models considered, theWeighted Moving Average with a loopback of 30 days had the least forecast
errors with a Mean Absolute Error(MAE) of 34.77 MW, Mean Squared Error(MSE) of 3271.68
MW, Root Mean Squared Error(RMSE) of 57.19 MW, Mean Absolute Percent Error(MAPE) of
3.005% and 𝑅2 Score of 0.921.
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Description
Forecasting is the technique of making scientific predictions of the future based on historical data
trends. With increasing smart grid technologies and penetration of intermittent renewable energy
technologies, peak load forecasting has been an important task in recent years for optimizing power
grid operations.