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12th IEEE PES Asia-Pacific power and Energy Engineering Conference, APPEEC 2020

作者： Zhang, Tao Li, Hao Lu, Jixiang Jun, Lu Jin Li, Hong Xie, Feng Technology Research Center Nari Group Corporation Nanjing China State Key Lab. of Intelligent Power Grid Protection and Operation Control Nari Group Corporation Technology Research Center Nanjing China

ISBN: (数字)9781728157481

ISBN: (纸本)9781728157481

power dispatching plays quite an important role in power grid operation in terms of safety and stability. Structured data has been well managed in the real-time power dispatching and controlling system. However, the management of unstructured data such as text data has not received enough attention. In this paper, we accomplish an application of auxiliary operation of power dispatching by doing semantic analysis on operation procedures, which belong to an important category of power dispatching text data for fault disposal. The method that we proposed in our application is achieved based on Natural Language Processing (NLP) technologies. Our contributions are mainly two-fold: 1) we accurately extract key information from text data through our proposed method, and 2) we help power dispatching operators reduce their work and improve efficiency by providing auxiliary operation advice. © 2020 IEEE.

关键词： Semantics

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A hybrid model based on convolutional neural network and long short-term memory for short-term load forecasting

A hybrid model based on convolutional neural network and lon...

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IEEE power and Energy Society General Meeting

作者： Jixiang LU Zhihong YANG Qipei ZHANG Mengfu TU Technology Research Center NARI Group Corporation Nanjing China State Key Laboratory of Intelligent Power Grid Protection and Operation Control Nanjing China

To better mine the effective information contained in massive data and improve the accuracy of short-term load forecasting, this paper proposes a hybrid model based on convolutional neural network and long short-term memory network (CNN-LSTM) for short-term load forecasting (STLF), which takes massive historical load data, meteorological data, date information, and peak-valley electric price data as input by constructing continuous feature maps with time sliding window. Convolutional neural network (CNN) is used to extract features and reshape them into vectors. The feature vectors are constructed in temporal and fed into long short-term memory network (LSTM) which is used to predict STLF. To the best of our knowledge, this is the first work to predict STLF using deep learning in both spatial and temporal domains. It is shown that the forecasting accuracy can be notably improved by CNN-LSTM hybrid model method. The effectiveness of the proposed method is validated through extensive comparison studies on a real-world dataset.

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A method of automatic generation of daily work report for power dispatching based on RoBERTa

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IOP Conference Series: Materials Science and Engineering 2020年第1期853卷

作者： Jie Li Tao Zhang Hao Li Jixiang Lu Jinjun Lu Ziqi Li State Grid Jiangsu Electric Power Co. LTD Technology Research Center of NARI Group Corporation Nanjing China State Key Laboratory of Intelligent Power Grid Protection and Operation Control Nanjing China

power dispatching operators need to read a large number of power dispatching logs every day and arrange them into daily work reports. It takes them a lot of time to do information extraction and transfer that information into work reports. In order to reduce the workload of power dispatching operators, we propose an intelligent method for automatic generation of daily work reports in this paper by adopting RoBERTa, which is an improved version of BERT. With the outstanding performance of RoBERTa in semantic analysis and entity recognition, the workload of power dispatching operators is reduced approximately 50% through our proposed method, which means the efficiency is greatly improved.

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