Abstract
This paper assesses the performance of a technique for estimating the power consumption of individual devices based on aggregate consumption. The new semi-Markov technique, outperforms pure hidden Markov models on the REDD dataset.
The technique also exploits information from transients to eliminate a substantial fraction of the observed errors.
- A. Cole and A. Albicki. Algorithm for nonintrusive identification of residential appliances. In Proc. IEEE ISCAS, volume 3, pages 338--341. IEEE, 1998.Google ScholarCross Ref
- D. Egarter, V. P. Bhuvana, and W. Elmenreich. PALDi: Online load disaggregation via particle filtering. IEEE Transactions on Instrumentation and Measurement, 64(2):467--477, 2015.Google ScholarCross Ref
- G. D. Forney. The Viterbi algorithm. Proceedings of the IEEE, 61(3):268--278, 1973.Google ScholarCross Ref
- Z. Guo, Z. J. Wang, and A. Kashani. Home appliance load modeling from aggregated smart meter data. IEEE Trans. Power Systems, 30(1):254--262, 2015.Google ScholarCross Ref
- G. Hart. Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12):1870--1891, 1992.Google ScholarCross Ref
- M. J. Johnson and A. S. Willsky. Bayesian nonparametric hidden Semi-Markov models. J. Machine Learning Research, 14:673--701, Feb. 2013. Google ScholarDigital Library
- H. Kim, M. Arlitt, and G. Lyon. Unsupervised disaggregation of low frequency power measurements. In SIAM Int. Conf. Data Mining, pages 747--758, 2011.Google ScholarCross Ref
- J. Z. Kolter and T. Jaakkola. Approximate inference in additive factorial HMMs with application to energy disaggregation. In 2012 International Conference on Artificial Intelligence and Statistics, pages 1472--1482, 2012.Google Scholar
- J. Z. Kolter and M. J. Johnson. REDD: A public data set for energy disaggregation research. In SustKDD workshop on Data Mining Applications in Sustainability, number 1, pages 1--6, 2011.Google Scholar
- J. Z. Kolter and M. J. Johnson. The Reference Energy Disaggregation Data Set. http://redd.csail.mit.edu/, 2011.Google Scholar
- V. Krishnamurthy and J. B. Moore. Signal processing of semi-markov models with exponentially decaying states. In Proc. IEEE Conf. Decision and Control, pages 2744--2749. IEEE, 1991.Google ScholarCross Ref
- H. Lange and M. Berg´es. Efficient Inference in Dual-Emission FHMM for Energy Disaggregation. In AAAI: AI for Smart Grids and Smart Buildings, 2016.Google Scholar
- S. Makonin, F. Popowich, I. V. Bajic, B. Gill, and L. Bartram. Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring. IEEE Transactions on Smart Grid, 7(6):2575--2585, 2016.Google ScholarCross Ref
- A. Marchiori, D. Hakkarinen, Q. Han, and L. Earle. Circuit-Level Load Monitoring for Household Energy Management. IEEE Pervasive Computing, 10(1):40--48, Jan. 2011. Google ScholarDigital Library
- O. Parson, S. Ghosh, M.Weal, and A. Rogers. Non-intrusive load monitoring using prior models of general appliance types. In AAAI, 2012. Google ScholarDigital Library
- S. V. Vaseghi. Hidden Markov Models with Duration-Dependent State Transition Probabilities. Electronics Letters, 27(8):625, 1991.Google ScholarCross Ref
- A. R.Webb. Gamma mixture models for target recognition. Pattern Recognition, 33(12):2045--2054, 2000.Google ScholarCross Ref
- Y. F.Wong, Y. A. S¸ekercio glu, and T. Drummond. Real-time Load Disaggregation Algorithm using Particle-Based Distribution Truncation with State Occupancy Model. Electronics Letters, 50(9):697--699, 2014.Google ScholarCross Ref
- T. Zia, D. Bruckner, and A. Zaidi. A Hidden Markov Model Based Procedure for Identifying Household Electric Loads. In IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society, pages 3218--3223. IEEE, Nov. 2011.Google ScholarCross Ref
Recommendations
Hidden semi-Markov models
As an extension to the popular hidden Markov model (HMM), a hidden semi-Markov model (HSMM) allows the underlying stochastic process to be a semi-Markov chain. Each state has variable duration and a number of observations being produced while in the ...
The evaluation problem in discrete semi-hidden Markov models
This paper is devoted to discrete semi-hidden Markov models (SHMM), which are related to the well-known hidden Markov models (HMM). In particular, the HMM associated to an SHMM is defined, and the forward algorithm for solving the evaluation problem in ...
Coding with partially hidden Markov models
DCC '95: Proceedings of the Conference on Data CompressionPartially hidden Markov models (PHMM) are introduced. They are a variation of the hidden Markov models (HMM) combining the power of explicit conditioning on past observations and the power of using hidden states. (P)HMM may be combined with arithmetic ...
Comments