Uma proposta de desagregação de energia baseada no Modelo Oculto de Markov
Resumo
A desagregação de energia nos permite separar o consumo agregado de energia de uma residência em seus aparelhos contribuintes. Nesse trabalho, usamos um método de desagregação de energia baseado na construção de Modelos Ocultos de Markov que descrevem a demanda de energia de aparelhos de uso doméstico e propusemos o uso do algoritmo de Baum-Welch para a estimativa dos parâmetros do modelo do refrigerador e da lavadora. Cada modelo foi obtido e avaliado com os datasets Tracebase e REDD. Os resultados apresentam um modelo geral para cada aparelho, os quais foram construídos usando até 7 instâncias de treinamento, de modo que a métrica apresenta uma melhor convergência. Por fim, cada modelo foi ajustado para desagregar a energia.
Palavras-chave:
Aprendizagem de Máquina, Inteligência Artificial, Matemática Computacional
Referências
Hart, G. W. (1992). Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12):1870–1891.
Kim, H., Marwah, M., Arlitt, M., Lyon, G., and Han, J. (2011). Unsupervised disaggregation of low frequency power measurements. In Proceedings of the 2011 SIAM international conference on data mining, pages 747–758. SIAM.
Kolter, J. Z. and Johnson, M. J. (2011). Redd: A public data set for energy disaggregation research. In Workshop on data mining applications in sustainability (SIGKDD), San Diego, CA, volume 25, pages 59–62.
Kong, W., Dong, Z. Y., Ma, J., Hill, D. J., Zhao, J., and Luo, F. (2016). An extensible approach for non-intrusive load disaggregation with smart meter data. IEEE Transactions on Smart Grid, 9(4):3362–3372.
Kwak, Y., Hwang, J., and Lee, T. (2018). Load disaggregation via pattern recognition: A feasibility study of a novel method in residential building. Energies, 11(4):1008.
Lam, H. Y., Fung, G., and Lee, W. (2007). A novel method to construct taxonomy electrical appliances based on load signaturesof. IEEE Transactions on Consumer Electronics, 53(2):653–660.
Parson, O., Ghosh, S., Weal, M., and Rogers, A. (2012). Non-intrusive load monitoring using prior models of general appliance types. In Twenty-Sixth AAAI Conference on Articial Intelligence.
Parson, O., Ghosh, S., Weal, M., and Rogers, A. (2014). An unsupervised training method for non-intrusive appliance load monitoring. Articial Intelligence, 217:1–19.
Reinhardt, A., Baumann, P., Burgstahler, D., Hollick, M., Chonov, H., Werner, M., and Steinmetz, R. (2012). On the accuracy of appliance identication based on distributed load metering data. In 2012 Sustainable Internet and ICT for Sustainability (SustainIT), pages 1–9. IEEE.
Sammut, C. and Webb, G. I. (2011). Encyclopedia of machine learning. Springer Science & Business Media.
Schreiber, J. (2017). Pomegranate: fast and exible probabilistic modeling in python. The Journal of Machine Learning Research, 18(1):5992–5997.
Stamp, M. (2017). Introduction to machine learning with applications in information security. Chapman and Hall/CRC.
Kim, H., Marwah, M., Arlitt, M., Lyon, G., and Han, J. (2011). Unsupervised disaggregation of low frequency power measurements. In Proceedings of the 2011 SIAM international conference on data mining, pages 747–758. SIAM.
Kolter, J. Z. and Johnson, M. J. (2011). Redd: A public data set for energy disaggregation research. In Workshop on data mining applications in sustainability (SIGKDD), San Diego, CA, volume 25, pages 59–62.
Kong, W., Dong, Z. Y., Ma, J., Hill, D. J., Zhao, J., and Luo, F. (2016). An extensible approach for non-intrusive load disaggregation with smart meter data. IEEE Transactions on Smart Grid, 9(4):3362–3372.
Kwak, Y., Hwang, J., and Lee, T. (2018). Load disaggregation via pattern recognition: A feasibility study of a novel method in residential building. Energies, 11(4):1008.
Lam, H. Y., Fung, G., and Lee, W. (2007). A novel method to construct taxonomy electrical appliances based on load signaturesof. IEEE Transactions on Consumer Electronics, 53(2):653–660.
Parson, O., Ghosh, S., Weal, M., and Rogers, A. (2012). Non-intrusive load monitoring using prior models of general appliance types. In Twenty-Sixth AAAI Conference on Articial Intelligence.
Parson, O., Ghosh, S., Weal, M., and Rogers, A. (2014). An unsupervised training method for non-intrusive appliance load monitoring. Articial Intelligence, 217:1–19.
Reinhardt, A., Baumann, P., Burgstahler, D., Hollick, M., Chonov, H., Werner, M., and Steinmetz, R. (2012). On the accuracy of appliance identication based on distributed load metering data. In 2012 Sustainable Internet and ICT for Sustainability (SustainIT), pages 1–9. IEEE.
Sammut, C. and Webb, G. I. (2011). Encyclopedia of machine learning. Springer Science & Business Media.
Schreiber, J. (2017). Pomegranate: fast and exible probabilistic modeling in python. The Journal of Machine Learning Research, 18(1):5992–5997.
Stamp, M. (2017). Introduction to machine learning with applications in information security. Chapman and Hall/CRC.
Publicado
26/10/2020
Como Citar
FONTES, Igor; FREITAS, Cristopher; DOS SANTOS, Eduardo; AQUINO, Andre.
Uma proposta de desagregação de energia baseada no Modelo Oculto de Markov. In: ESCOLA REGIONAL DE COMPUTAÇÃO BAHIA, ALAGOAS E SERGIPE (ERBASE), 20. , 2020, Arapiraca-AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
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p. 21-30.
