Papers › Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution

Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution

15 Aug 2023PeerJ Computer Science 2023 8archive 2025-07-28

Irshad Ullah, Syed Muhammad Hasanat, Khursheed Aurangzeb, Musaed Alhussein, Muhammad Rizwan, Muhammad Shahid Anwar

Precise short-term load forecasting (STLF) plays a crucial role in the smooth operation of power systems, future capacity planning, unit commitment, and demand response. However, due to its non-stationary and its dependency on multiple cyclic and non-cyclic calendric features and non-linear highly correlated metrological features, an accurate load forecasting with already existing techniques is challenging. To overcome this challenge, a novel hybrid technique based on long short-term memory (LSTM) and a modified split-convolution (SC) neural network (LSTM-SC) is proposed for single-step and multi-step STLF. The concatenating order of LSTM and SC in the proposed hybrid network provides an excellent capability of extraction of sequence-dependent features and other hierarchical spatial features. The model is evaluated by the Pakistan National Grid load dataset recorded by the National Transmission and Dispatch Company (NTDC). The load data is preprocessed and multiple other correlated features are incorporated into the data for performance enhancement. For generalization capability, the performance of LSTM-SC is evaluated on publicly available datasets of American Electric Power (AEP) and Independent System Operator New England (ISO-NE). The effect of temperature, a highly correlated input feature, on load forecasting is investigated either by removing the temperature or adding a Gaussian random noise into it. The performance evaluation in terms of RMSE, MAE, and MAPE of the proposed model on the NTDC dataset are 500.98, 372.62, and 3.72% for multi-step while 322.90, 244.22, and 2.38% for single-step load forecasting. The result shows that the proposed method has less forecasting error, strong generalization capability, and satisfactory performance on multi-horizon.

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Tasks

Data Ablation Load Forecasting Missing Elements Multivariate Time Series Forecasting Outlier Interpretation Time Series Forecasting Univariate Time Series Forecasting

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Multivariate Time Series Forecasting	AEP	LSTM-SC	12 steps MAPE	2.58	#1 of 1	Archive leaderboard	report
Multivariate Time Series Forecasting	AEP	LSTM-SC	12 steps RMSE	549.92	#1 of 1	Archive leaderboard	report
Univariate Time Series Forecasting	AEP	LSTM-SC	MAPE (%)	0.67	#1 of 1	Archive leaderboard	report
Univariate Time Series Forecasting	AEP	LSTM-SC	RMSE	97.78	#1 of 1	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1D CNN 1x1 Convolution Auxiliary Classifier Average Pooling Convolution Dense Connections Dropout Electric GoogLeNet Inception Module LSTM Local Response Normalization MAE Max Pooling ReLU Sigmoid Activation Softmax Tanh Activation

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