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In recent years, countries around the world have been advocating green development and gradually implementing new energy-based power grids. Due to the contradiction between the randomness of new energy generation and the growing demand for residential electricity, the stable operation of power grids is facing a huge challenge. Air conditioning load accounts for a large proportion of residential electricity consumption, so it is of great significance to monitor it. Through understanding the electricity consumption of air conditioning load, residents can be guided to use electricity reasonably, ensure the stable operation of the power grid, and provide a decision-making basis for demand response to alleviate the contradiction between supply and demand. At present, there are relatively few air conditioning load monitoring works, most of which are carried out as part of non-intrusive load monitoring. The selected input data mainly consider generality and seldom consider the operation rule of air conditioning and the influence of meteorological factors. Therefore, the accuracy of air conditioning monitoring is still insufficient. Based on this, a real-time monitoring method of air conditioning based on electrical characteristics and a twin-tower neural network is proposed in this paper. Based on Long Short-Term Memory (LSTM), the sequential branch was constructed to mine the sequential characteristics of air conditioning operation. Based on Back Propagation (BP), an electrical feature branch was constructed to capture the operation rules of air conditioning. Meanwhile, the experimental results on one year of real data from 20 users show that compared with the traditional LSTM model, this model can realize the accurate identification of air conditioning load and provide support for the demand response potential assessment of air conditioning load.
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