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 space-time 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. Then the correlation value is used to modify the time and space
dimensions of the test set's partial period prediction values.
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.
Probabilistic Forecasting of Battery Energy Storage State-of-Charge under Primary Frequency Control