Mathematical and Statistical Model of Load Recognition in Microgrids
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The paper establishes a mathematical and statistical model of load recognition in microgrids to enhance automated demand response in energy-constrained systems. Efficient allocation of energy, load allocation, and stability of the system all require proper identification of electrical appliances. In contrast to other studies that are available, the current study concentrates on statistical validation, robustness, and reliability rather than accuracy. The energy usage of appliances is modelled as a stochastic process to represent the variability of the real-world usage. Deterministic and probabilistic models, as well as energy-based and event-based feature extraction methods, are compared. Both parametric and non-parametric tests are used to test performance in uncertain conditions. The findings indicate that there is no significant accuracy difference between deterministic and probabilistic models. However, energy-based features are significantly better than event-based features. The variability of appliance usage has statistical differences, but in terms of practical implications on performance in the models, it is not very significant, thus being stable. In general, the article offers a valid and evidence-based model that can help make effective and sustainable decisions regarding the management of microgrid energy.
Keywords:
Demand response, Hypothesis testing, Load recognition, Microgrids, Probabilistic modellingReferences
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