人工智能模型在窃电用户识别实际工业场景中应用时面临着高识别率与低成本之间的矛盾。对于训练完成的模型，通过设置模型判别阈值使其识别出更多窃电用户的同时也将导致更多非窃电用户被错误识别，造成现场排查成本的增加；反之，若追求较低的现场排查成本，那么只能识别出相对较少的窃电用户。针对此矛盾，本文提出了一种基于人机协同的窃电用户判别方法。在该方法中，电网公司首先根据自身特点与需求选取合适的模型判别阈值，然后结合模型判别依据与人工经验知识对模型初步判定结果进行筛查，最后只需对相对较少的用户进行现场排查，从而降低人力、物力成本。此外，电力公司可以根据模型从用户用电数据中学到的“知识”对自身领域知识进行补充，还可以对模型所使用的指标体系进行修正、改善。本文所提出的方法对于人工智能模型在窃电用户识别的应用方面具有一定的管理意义与实际应用价值。
There is a contradiction between high recognition rate and low cost in the real industrial condition of electricity theft users distinguishing when using artificial intelligence model. For a trained artificial intelligence model, there will be higher false alarm rate when the model discrimination threshold is set to identify more electricity theft users; on the contrary, only a relatively small number of electricity theft users can be identified if a lower on-site investigation cost is pursued. In the light of this contradiction, a human-machine cooperation method is proposed. In this method, the power grid company first selects the appropriate model discrimination threshold according to its own characteristics and needs, then combines the artificial experience and the model explanations to screen the preliminary judgment results of the model, and finally only needs to conduct on-site investigation for relatively few users, so as to reduce the cost of human and material resources. In addition, power grid companies can supplement their own domain knowledge according to the “knowledge” learned from the user electricity data. They can also improve the designing of features used in the model. The method proposed in this article has significance value of management and practical application of artificial intelligence model in distinguishing electricity theft users.
Choksi, K.A., Jain, S. and Pindoriya, N.M. (2020) Feature Based Clustering Technique for Investigation of Domestic Load Profiles and Probabilistic Variation Assessment: Smart Meter Dataset. Sustainable Energy, Grids and Networks, 22, Article ID: 100346. https://doi.org/10.1016/j.segan.2020.100346
Wang, X.L. and Ahn, S.-H. (2020) Real-Time Prediction and Anomaly Detection of Electrical Load in a Residential Community. Applied Energy, 259, Article ID: 114145. https://doi.org/10.1016/j.apenergy.2019.114145
Goldstein, A., Kapelner, A., Bleich, J., et al. (2015) Peeking inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation. Journal of Computational & Graphical Stats, 24, 44-65.
https://doi.org/10.1080/10618600.2014.907095
[14]
Ribeiro, M.T., Singh, S. and Guestrin, C. (2016) “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, 20-23 August 2006, 1135-1144. https://doi.org/10.1145/2939672.2939778
[15]
Tucker, A.W. (1959) Contributions to the Theory of Games. Princeton University Press, Princeton.
https://doi.org/10.1515/9781400882168
[16]
？trumbelj, E. and Kononenko, I. (2010) An Efficient Explanation of Individual Classifications Using Game Theory. Journal of Machine Learning Research, 11, 1-18.
