  Accurate electrical load forecasting is of great importance for the efficient
operation and control of modern power systems. In this work, a hybrid long
short-term memory (LSTM)-based model with online correction is developed for
day-ahead electrical load forecasting. Firstly, four types of features are
extracted from the original electrical load dataset, including the historical
time series, time index features, historical statistical features, and
similarity features. Then, a hybrid LSTM-based electrical load forecasting
model is designed, where an LSTM neural network block and a fully-connected
neural network block are integrated that can model both temporal features
(historical time series) and non-temporal features (the rest features). A
gradient regularization-based offline training algorithm and an output layer
parameter fine-tuning-based online model correction method are developed to
enhance the model's capabilities to defend against disturbance and adapt to the
latest load data distribution, thus improving the forecasting accuracy. At
last, extensive experiments are carried out to validate the effectiveness of
the proposed electrical load forecasting strategy with superior accuracy
compared with commonly used forecasting models.
