Abstract
Due to the rapid growth of energy consumption worldwide, it has become a necessity that the energy waste caused by buildings is explicated by the aid of automated systems that can identify anomalous behaviour. Comprehensible anomaly detection, however, is a challenging task considering the lack of annotated real-world data in addition to the real-world uncertainties such as changing weather conditions and varying building features. Fuzzy Logic enables modelling knowledge-based non-linear systems that can handle these uncertainties, and facilitates modelling human interpretable systems. This paper proposes a new method for annotating anomalies and a novel framework for interpretable anomaly detection in real-world gas consumption data belonging to the educational buildings of the Hogeschool van Amsterdam. The proposed architecture uses the Wang and Mendel rule learning with k-means clustering and does not require prior knowledge of the data, while preserving transparency of the model behaviour. Experiments have shown that the proposed system matches the performance of existing baselines using an artificial neural network while providing additional desired features such as transparency of the model behaviour and interpretability of the detected anomalies.
The code is available at: https://github.com/murielhol/FuzzyEnergy.
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Notes
- 1.
In FL, set theoretic operations (e.g. intersection, union) are defined in terms of their membership functions.
- 2.
sklearn.cluster.KMeans: http://scikit-learn.org/stable/modules/clustering.html#clustering. The default settings were used in this paper.
- 3.
Hogeschool van Amsterdam: http://www.hva.nl/over-de-hva/locaties/locaties.html.
- 4.
Koninklijk Nederlands Meteorologisch Instituut: http://projects.knmi.nl/klimatologie/uurgegevens/selectie.cgi.
References
Ahmad, A.S., Hassan, M.Y., Abdullah, M.P., Rahman, H.A., Hussin, F., Abdullah, H., Saidur, R.: A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renew. Sustainable Energy Rev. 33, 102–109 (2014). doi:10.1016/j.rser.2014.01.069
Alcala, R., Casillas, J., Cord\(\acute{o}\)n, O., Herrera, F., Zwir, S.J.T.: Techniques for learning and tuning fuzzy rule-based systems for linguistic modeling and their application. In: Leondes, C. (ed.) Knowledge Engineering: Systems, Techniques and Applications, pp. 889–941. Academic Press, San Diego (1999)
Casillas, J., Cord\(\acute{o}\)n, O., Herrera, F.: Improving the Wang and Mendel’s fuzzy rule learning method by inducing cooperation among rules. In: Proceedings of the 8th Information Processing and Management of Uncertainty in Knowledge-Based Systems Conference, pp. 1682–1688 (2000)
Chandola, V., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 51, 58 (2009). doi:10.1145/1541880.1541882
Cleveland, R.B., Cleveland, W.S., McRae, J.E., Terpenning, I.: STL: a seasonal-Trend decomposition procedure based on loess. J. Official Statist. 6, 3–73 (1990)
Colmenar-Santos, A., de Lober, L.N.T., Borge-Diez, D., Castro-Gil, M.: Solutions to reduce energy consumption in the management of large buildings. Energy Build. 56, 66–77 (2013). doi:10.1016/j.enbuild.2012.10.004
De Nadai, M., van Someren, M.: Short-term anomaly detection in gas consumption through arima and artificial neural network forecast. In: Proceedings of 2015 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS 2015), pp. 250–255. IEEE, New York (2015). doi:10.1109/EESMS.2015.7175886
Hodge, V.J., Auston, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22, 85–126 (2004). doi:10.1007/s10462-004-4304-y. Kluwer Academic Publishers, Department of Computer Science, University of York
Katipamula, S., Brambley, M.R.: Review article: methods for fault detection, diagnostics, and prognostics for building systems: a review, Part I. HVAC & R. Res. 11, 3–25 (2005)
Lodewegen, J.: Saving energy in buildings using an Artificial Neural Network for outlier detection. B.Sc Thesis. University of Amsterdam (2015). https://esc.fnwi.uva.nl/thesis/centraal/files/f1992667963.pdf
Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Direction. Prentice Hall, Upper Saddle River (2001). ISBN: 978-0130409690
ODYSEE: an analysis based on the ODYSSEE and MURE databases. http://www.odyssee-mure.eu/publications/br/energy-efficiency-trends-policies-buildings.pdf
ODYSEE. Energy efficiency trends and policies in the Netherlands (2015). http://www.odyssee-mure.eu/publications/national-reports/energy-efficiency-netherlands.pdf
Perez-Lombard, L., Ortiz, J., Pout, C.: A review on buildings energy consumption information. Energy Build. 40, 394–398 (2008). doi:10.1016/j.enbuild.2007.03.007. Elsevier Inc
Rocha, A., Papa, J.P., Meira, L.A.A.: How far you can get using machine learning black-boxes. In: Conference on Graphics, Patterns and Images, vol. 16, pp. 1530–01834. IEEE (2010). doi:10.1109/SIBGRAPI.2010.34
Sanz, F., Ramrez, J., Correa, R.: Fuzzy inference systems applied to the analysis of vibrations in electrical machines. Fuzzy Inference Syst. Theory Appl. (2012). doi:10.5772/37448
Singh, H., Gupta, M.M., Meitzler, T., Hou, Z., Garg, K.K., Solo, A.M.G., Zadeh, L.A.: Real-Life Applications of Fuzzy Logic, Advances in Fuzzy Systems. Hindawi Publishing Corporation (2013). doi:10.1155/2013/581879
Suganthi, L., Iniyan, S., Samuel, A.A.: Applications of fuzzy logic in renewable energy systems: a review. Renew. Sustain. Energy Rev. 48, 585–607 (2015). doi:10.1016/j.rser.2015.04.037
Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEE Trans. Syst. Syst. Man Cybern. 22, 1414–1427 (1992)
Wijayasekera, D., Linda, O., Manic, M., Rieger, C.: Mining building energy management system data using fuzzy anomaly detection and linguistic descriptions. IEEE Trans. Industr. Inform. 10, 1829–1839 (2014). doi:10.1109/TII.2014.2328291
Zadeh, L.A.: Fuzzy sets. Inf. Control 9, 338–353 (1965)
Zhao, H., Magoules, F.: A review on the prediction of building energy consumption. Renew. Sustain. Energy Rev. 16, 3586–3592 (2012). doi:10.1016/j.rser.2012.02.049
Acknowledgment
This study is partially supported by the Marie Curie Initial Training Network (ITN) ESSENCE, grant agreement no. 607062.
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Hol, M., Bilgin, A. (2017). Design of a Fuzzy Logic Based Framework for Comprehensive Anomaly Detection in Real-World Energy Consumption Data. In: Bosse, T., Bredeweg, B. (eds) BNAIC 2016: Artificial Intelligence. BNAIC 2016. Communications in Computer and Information Science, vol 765. Springer, Cham. https://doi.org/10.1007/978-3-319-67468-1_9
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