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- W3149270370 abstract "One of the important aspects of energy modeling is the process of demand curve prediction. Existing demand curve prediction methods generally rely on statistical curve fittings which assume a certain functional form such as constant price elasticity. There are a number of disadvantages to this approach. For one, this method makes certain assumptions about the functional form of the price-demand curve that may not be exhibited in practice. In addition, since curve fits rely on only a single function, and not a distribution of functions, they do not capture the uncertainty about price-demand curves. In this work, demand curve prediction is instead treated by assigning a probability measure to the space of all functions that meet the global regularity (non-decreasing conditions). Using this method, a numerical example of Bayesian demand curve prediction is presented." @default.
- W3149270370 created "2021-04-13" @default.
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- W3149270370 date "2009-12-01" @default.
- W3149270370 modified "2023-09-27" @default.
- W3149270370 title "Demand curve prediction via Bayesian probability assignment over a functional space" @default.
- W3149270370 cites W2039206511 @default.
- W3149270370 doi "https://doi.org/10.1109/wsc.2009.5429229" @default.
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