A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network

By Chujie Tian, Jian Ma, Chunhong Zhang, Panpan Zhan. Energies, 2018

359 citations recorded by the source corpus.

Accurate electrical load forecasting is of great significance to help power companies in better scheduling and efficient management. Since high levels of uncertainties exist in the load time series, it is a challenging task to make accurate short-term load forecast (STLF). In recent years, deep learning approaches provide better performance to predict electrical load in real world cases. The convolutional neural network (CNN) can extract the local trend and capture the same pattern, and the long short-term memory (LSTM) is proposed to learn the relationship in time steps. In this paper, a new deep neural network framework that integrates the hidden feature of the CNN model and the LSTM model is proposed to improve the forecasting accuracy. The proposed model was tested in a real-world case, and detailed experiments were conducted to validate its practicality and stability. The forecasting performance of the proposed model was compared with the LSTM model and the CNN model. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were used as the evaluation indexes. The experimental results demonstrate that the proposed model can achieve better and stable performance in STLF.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/a37fcfdc10d08d81e47a6434c3441d68100d20d1

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Citations in the corpus

Deep Learning for Time Series Forecasting: The Electric Load Case (2019, methodology): “…Variables Dataset (Location) [18] LSTM, GRU, ERNN, NARX, ESN D - Rome, Italy [18] LSTM, GRU, ERNN, NARX, ESN D T New England [ CNN D C, TI Sceaux, France [25] CNN + LSTM D T, C, TI North-China [26] CNN + LSTM D - North-Italy Table 1: Time Horizon: H(hour), D (day), W(week), M(month), Y(year), ?”
Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach (2019, methodology): “A combination of CNN and LSTM structures was proposed in [48], where the model was used for short-term load forecasting.”
Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting (2020, methodology): “Other approaches consider combining the features extracted in parallel from a CNN and a LSTM to improve the forecasting using electricity demand data [22] or financial data [23].”