Machine learning in electricity fraud detection in smart grids with multivariate Gaussian distribution
References
- Aziz, S., Naqvi, S. Z. H., Khan, M. U., & Aslam, T. (2020). Electricity Theft Detection using Empirical Mode Decomposition and K-Nearest Neighbors. In <em>2020 International Conference on Emerging Trends in Smart Technologies, ICETST 2020</em>. <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1109/ICETST49965.2020.9080727" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1109/ICETST49965.2020.9080727</a>">https://doi.org/10.1109/ICETST49965.2020.9080727</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1109/ICETST49965.2020.9080727" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/ICETST49965.2020.9080727</a></dgdoi:pub-id>
- Basu, K., Debusschere, V., Douzal-Chouakria, A., & Bacha, S. (2015). Time series distance-based methods for non-intrusive load monitoring in residential buildings. <em>Energy and Buildings</em>. <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1016/j.enbuild.2015.03.021" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.enbuild.2015.03.021</a>">https://doi.org/10.1016/j.enbuild.2015.03.021</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.enbuild.2015.03.021" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.enbuild.2015.03.021</a></dgdoi:pub-id>
- Cody, C., Ford, V., & Siraj, A. (2016). Decision tree learning for fraud detection in consumer energy consumption. In <em>Proceedings – 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015</em>. <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1109/ICMLA.2015.80" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1109/ICMLA.2015.80</a>">https://doi.org/10.1109/ICMLA.2015.80</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1109/ICMLA.2015.80" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/ICMLA.2015.80</a></dgdoi:pub-id>
- Coma-Puig, B., Carmona, J., Gavalda, R., Alcoverro, S., & Martin, V. (2016). Fraud detection in energy consumption: A supervised approach. In <em>Proceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016</em>. <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1109/DSAA.2016.19" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1109/DSAA.2016.19</a>">https://doi.org/10.1109/DSAA.2016.19</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1109/DSAA.2016.19" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/DSAA.2016.19</a></dgdoi:pub-id>
- Jain, S., Choksi, K. A., & Pindoriya, N. M. (2019). Rule-based classification of energy theft and anomalies in consumers load demand profile. <em>IET Smart Grid</em>. <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1049/ietstg.2019.0081" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1049/ietstg.2019.0081</a>">https://doi.org/10.1049/ietstg.2019.0081</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1049/iet-stg.2019.0081" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1049/iet-stg.2019.0081</a></dgdoi:pub-id>
- Lyu, L., Jin, J., Rajasegarar, S., He, X., & Palaniswami, M. (2017). Fog-empowered anomaly detection in IoT using hyperellipsoidal clustering. <em>IEEE Internet of Things Journal</em>. <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1109/JIOT.2017.2709942" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1109/JIOT.2017.2709942</a>">https://doi.org/10.1109/JIOT.2017.2709942</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1109/JIOT.2017.2709942" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/JIOT.2017.2709942</a></dgdoi:pub-id>
- Massaferro, P., Martino, J. M. Di, & Fernandez, A. (2020). Fraud Detection in Electric Power Distribution: An Approach That Maximizes the Economic Return. <em>IEEE Transactions on Power Systems</em>. <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1109/TPWRS.2019.2928276" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1109/TPWRS.2019.2928276</a>">https://doi.org/10.1109/TPWRS.2019.2928276</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1109/TPWRS.2019.2928276" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/TPWRS.2019.2928276</a></dgdoi:pub-id>
- Siffer, A., Fouque, P. A., Termier, A., & Largouet, C. (2017). Anomaly detection in streams with extreme value theory. In <em>Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining</em>. <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1145/3097983.3098144" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1145/3097983.3098144</a>">https://doi.org/10.1145/3097983.3098144</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1145/3097983.3098144" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1145/3097983.3098144</a></dgdoi:pub-id>
- Spirić, J. V., Dočić, M. B., & Stanković, S. S. (2015). Fraud detection in registered electricity time series. <em>International Journal of Electrical Power and Energy Systems</em>. <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1016/j.ijepes.2015.02.037" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.ijepes.2015.02.037</a>">https://doi.org/10.1016/j.ijepes.2015.02.037</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.ijepes.2015.02.037" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.ijepes.2015.02.037</a></dgdoi:pub-id>
- Yip, S. C., Tan, W. N., Tan, C. K., Gan, M. T., & Wong, K. S. (2018). An anomaly detection framework for identifying energy theft and defective meters in smart grids. <em>International Journal of Electrical Power and Energy Systems</em>. <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1016/j.ijepes.2018.03.025" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.ijepes.2018.03.025</a>">https://doi.org/10.1016/j.ijepes.2018.03.025</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.ijepes.2018.03.025" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.ijepes.2018.03.025</a></dgdoi:pub-id>
- Zanetti, M., Jamhour, E., Pellenz, M., Penna, M., Zambenedetti, V., & Chueiri, I. (2019). A Tunable Fraud Detection System for Advanced Metering Infrastructure Using Short-Lived Patterns. <em>IEEE Transactions on Smart Grid</em>. <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1109/TSG.2017.2753738" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1109/TSG.2017.2753738</a>">https://doi.org/10.1109/TSG.2017.2753738</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1109/TSG.2017.2753738" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/TSG.2017.2753738</a></dgdoi:pub-id>
DOI: https://doi.org/10.2478/picbe-2021-0049 | Journal eISSN: 2558-9652
Language: English
Page range: 543 - 551
Published on: Dec 31, 2021
Published by: The Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
Keywords:
Related subjects:
© 2021 Simona-Vasilica Oprea, Adela Bâra, Niculae Oprea, published by The Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution 4.0 License.