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emand response management systems often involve the use of pricing schemes to motivate the efficient use of electrical power. Achieving this efficiency requires the detection of electrical power patterns. The detection of these patterns normally involves use of non-linear, quasi-non-linear, and at times linear data pattern detection models. The behavioural disparities of these models and specifically when used for a specific set of data make it hard to select the most efficient model. The contribution of this study is devising an empirical benchmark (reference) ( perfect ) control pricing (PCP) model through which various models are compared in order to select the most efficient model. In this study, the authors elect neural networks, sliding window–multiple linear regression, and a proportional controller models to be representative of non-linear, quasi-non-linear, and linear models, respectively, in order to demonstrate the effectiveness of PCP. The dataset used for demonstrating both the operation of PCP and the elected models for comparisons is collected from Green Button project and Pacific Gas and Electric.


This is an open access article published by The IET, and distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. CC BY 3.0

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The Journal of Engineering