E3S Web Conf.
Volume 182, 20202020 10th International Conference on Power, Energy and Electrical Engineering (CPEEE 2020)
|Number of page(s)||5|
|Section||Advanced Power Generation Technology and Application|
|Published online||31 July 2020|
LSTM-based Short-term Electrical Load Forecasting and Anomaly Correction
1 State Grid Shanghai Electric Power Research Institute, Department of Power Source, Shanghai 200437, China
2 State Grid Shanghai Electric Power Research Institute, General Manager Office, Shanghai 200437, China
3 State Grid Shanghai Electric Power Research Institute, Department of Human Resource, Shanghai 200437, China
* Corresponding author: email@example.com
The Emergence of the Ubiquitous Power Internet of Things(UPIoT) facilitates data sharing and service expansion for the power system. Based on the architecture of the UPIoT and combined with deep learning technology, short-term electrical load forecasting and anomaly correction could be used to improve the overall performance. Since short-term electrical loads are non-linear and non-stationary  and could be easily affected by external interference, traditional load forecasting algorithms cannot recognize the correlation between the time sequence thus rendering low prediction accuracy. In this article, a Long Short-Term Memory (LSTM) based algorithm is proposed to improve the prediction accuracy by utilizing the correlation between the hourly load sequence. Then, the real-time forecasting outputs are compared to the raw data in order to detect and dynamically repair the anomaly so as to further improve the performance. Experiment results show that the proposed approach outputs low Mean Square Error (MSE) of around 0.2 and could still hold it at around 0.3 with corrected data when the anomaly is detected, which proves the accuracy and robustness of the algorithm.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.