Day-Ahead Forecast of Electrical Load Based on EMD-MLP Combination Model

Abstract

[Introduction] Accurate load forecasting underpins the operation optimization of the electricity systems and is an indispensable aspect of energy management within such systems. Given the low accuracy and high computational complexity inherent in traditional methodologies that combine data decomposition and machine learning models, this study proposes a novel Empirical Mode Decomposition and Multi-Layer Perceptron (EMD-MLP) model for predicting day-ahead electrical load. [Method] Initially, the EMD method decomposed the original load time series into multiple Intrinsic Mode Function (IMF). These IMFs were then reconstructed into high-frequency and low-frequency components using extreme point partitioning, simplifying the prediction target. Subsequently, each reconstructed components was modeled separately for prediction, and the results were cumulatively used to provide the forecasted electrical load value. [Result] The proposed model is tested using real-world electrical load data of 2018 and 2019 from the Australian electricity market. [Conclusion] Comparing the extrapolative capabilities of our EMD-MLP model with persistence model, standalone MLP model and traditional EMD ensemble model confirms the effectiveness of our model in enhancing prediction accuracy. Moreover, while ensuring accuracy, the proposed EMD-MLP model simplifies the complexity and improves the efficiency of the forecasting process, thereby providing a practical solution for both day-ahead and real-time electrical load forecasting.

Keywords

• electrical load forecast
• day-ahead forecast
• empirical mode decomposition
• component reconstruction
• emd-mlp