LSTM-based Short-term Electrical Load Forecasting and Anomaly Correction

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.

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Theory-guided deep-learning for electrical load forecasting (TgDLF) via ensemble long short-term memory

Advances in Applied Energy ◽

10.1016/j.adapen.2020.100004 ◽

2021 ◽

Vol 1 ◽

pp. 100004

Author(s):

Yuntian Chen ◽

Dongxiao Zhang

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Load Forecasting ◽

Electrical Load ◽

Short Term ◽

Term Memory ◽

Electrical Load Forecasting ◽

Long Short Term Memory

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Long-Short-Term Memory Network Based Hybrid Model for Short-Term Electrical Load Forecasting

Information ◽

10.3390/info9070165 ◽

2018 ◽

Vol 9 (7) ◽

pp. 165 ◽

Cited By ~ 13

Author(s):

Liwen Xu ◽

Chengdong Li ◽

Xiuying Xie ◽

Guiqing Zhang

Keyword(s):

Hybrid Model ◽

Short Term Memory ◽

Load Forecasting ◽

Electrical Load ◽

Short Term ◽

Term Memory ◽

Memory Network ◽

Electrical Load Forecasting ◽

Long Short Term Memory

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An Ultra-Short-Term Electrical Load Forecasting Method Based on Temperature-Factor-Weight and LSTM Model

Energies ◽

10.3390/en13184875 ◽

2020 ◽

Vol 13 (18) ◽

pp. 4875

Author(s):

Dengyong Zhang ◽

Haixin Tong ◽

Feng Li ◽

Lingyun Xiang ◽

Xiangling Ding

Keyword(s):

Short Term Memory ◽

Load Forecasting ◽

Temperature Factor ◽

Memory Model ◽

Electrical Load ◽

Short Term ◽

Term Memory ◽

Forecasting Method ◽

Electrical Load Forecasting ◽

Long Short Term Memory

Ultra-short-term electrical load forecasting is an important guarantee for the safety and efficiency of energy system operation. Temperature is also an important factor affecting the changes in electric load. However, in different cases, the impact of temperature on load forecasting will vary greatly, and sometimes even lead to the decrease of forecasting accuracy. This often brings great difficulties to researchers’ work. In order to make more scientific use of temperature factor for ultra-short-term electrical load forecasting, especially to avoid the negative influence of temperature on load forecasting, in this paper we propose an ultra-short-term electrical load forecasting method based on temperature factor weight and long short-term memory model. The proposed method evaluates the importance of the current prediction task’s temperature based on the change magnitude of the recent load and the correlation between temperature and load, and therefore the negative impacts of the temperature model can be avoided. The mean absolute percentage error of proposed method is decreased by 1.24%, 1.86%, and 6.21% compared with traditional long short-term memory model, back-propagation neural network, and gray model on average, respectively. The experimental results demonstrate that this method has obvious advantages in prediction accuracy and generalization ability.

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