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DEEP CONVOLUTIONAL NEURAL NETWORK BASED ANOMALY DETECTION FOR TRANSACTIVE ENERGY SYSTEMS
DEEP CONVOLUTIONAL NEURAL NETWORK BASED ANOMALY DETECTION FOR TRANSACTIVE ENERGY SYSTEMS
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机译:交易能量系统基于深度卷积神经网络的异常检测
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摘要
A computer-implemented method for power grid anomaly detection using a convolutional neural network (CNN) trained to detect anomalies in electricity demand data and electricity supply data includes receiving (i) electricity demand data comprising time series measurements of consumption of electricity by a plurality of consumers, and (ii) electricity supply data comprising time series measurements of availability of electricity by one or more producers. An input matrix is generated that comprises the electricity demand data and the electricity supply data. The CNN is applied to the input matrix to yield a probability of anomaly in the electricity demand data and the electricity supply data. If the probability of anomaly is above a threshold value, an alert message is generated for one or more system operators.
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