An effective deep learning neural network model for short-term load forecasting

摘要

Energy load forecasting plays an important role in the smart grid, which can affect the promoting energy production and consumption decision-making processes. In this paper, the state-of-the-art deep learning (DL) neural models are used in the short-term load forecasting, including the multilayer perceptron (MLP), the convolutional neural network (CNN), and the long short-term memory (LSTM). A novel loss function is proposed for the load forecasting, and two commonly used benchmarks are used to verify the validity of the proposed function. The simulation results show that the mean absolute percentage error (MAPE) of the proposed loss function is 19.63% lower than cross-entropy and 2.34% lower than mean absolute error (MAE). We compared the mentioned neural networks in different aspects, and the results show that in energy load forecasting, CNN has superior performance than MLP and LSTM in terms of high accuracy and robustness to weather changes.