Background: The day-ahead load forecasting is an essential guideline for power generating,
and it is of considerable significance in power dispatch.
Objective: Most of the existing load probability prediction methods use historical data to predict a single
area, and rarely use the correlation of load time and space to improve the accuracy of load prediction.
Methods: This paper presents a method for day-ahead load probability prediction based on spacetime
correction. Firstly, the kernel density estimation (KDE) is employed to model the prediction
error of the long short-term memory (LSTM) model, and the residual distribution is obtained. The
correlation value is then used to modify the time and space dimensions of the test set's partial period
Results: The experiment selected three years of load data in 10 areas of a city in northern China.
The MAPE of the two modified models on their respective test sets can be reduced by an average of
10.2% and 6.1% compared to previous results. The interval coverage of the probability prediction
can be increased by an average of 4.2% and 1.8% than before.
Conclusion: The test results show that the proposed correction schemes are feasible.