摘要：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 [1] 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 ShortTerm 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.
其他摘要：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 [1] 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.
