Arooj Arif
The Energy Crisis is costing economies worldwide and depleting natural resources at an alarming rate. Conservation of energy has become a point of concern particularly in large consumption sectors. Load monitoring is an effective solution to conserve electrical energy at a large-scale, cost-effectively. While many Non-intrusive Load Disaggregation techniques are in practice, some techniques have been known to outperform others. This paper discusses the most optimum unsupervised learning technique for load disaggregation in Non-intrusive Load Monitoring for Type-I and Type-II loads; catering to factors such as residential requirements, practicality in real time, ease of integration, scalability, feature extraction, reduced training and increased accuracy to meet the need of the time. A review of various implemented models of ‘Super-State Hidden Markov Models’ in NILM has been provided, to conclude that this methodology is the most efficient approach to load disaggregation in the provided case scenario that prevails on a large scale. A comparison with recent state-of-the-art work has also been provided to show that this approach out performs conventional approaches in terms of the above-mentioned factors (catered to by super-state HMMs).
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