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The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction.

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    • 2015-12-09
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    Naganathan, H., Chong, W. K., & Ye, N. (2015). Learning Energy Consumption and Demand Models through Data Mining for Reverse Engineering. Procedia Engineering, 118, 1319-1324. doi:10.1016/j.proeng.2015.11.392

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