By a News Reporter-Staff News Editor at Energy Weekly News -- Investigators publish new report on Algorithms. According to news originating from Santa Monica, California, by VerticalNews correspondents, research stated, "Scenario discovery offers a new means to characterize and communicate the information in computer simulation models under conditions of deep uncertainty. The approach first defines scenarios as the future states of world where a proposed policy fails to meet its goals and then uses statistical algorithms to find concise descriptions of such regions in large databases of simulation model results."
Our news journalists obtained a quote from the research from Pardee RAND Graduate School, "Current scenario discovery applications rely on the Patient Rule Induction Method (PRIM), a user-interactive bump-hunting algorithm that identifies hyper-rectangular regions in the input space of the simulation model. While often successful, scenario discovery applications have been limited because in general a policy's vulnerabilities are not well described by the PRIM's hyper-rectangular regions. This study proposes and evaluates improved scenario discovery algorithms that address this challenge with a Principal Component Analysis (PCA)-based preprocessing step that transforms the original model input parameters so that PRIM can then identify high quality hyper-rectangular scenarios in the new rotated coordination system. We explore two versions. PCA-PRIM allows rotations among all uncertain model input parameters and CPCA-PRIM (for constrained PCA-PRIM) only allows rotations among parameters within user-specified domains. The latter may provide more useful information to users, who may find scenario axes described by linear combinations of related domain parameters more interpretable than combinations of dissimilar parameters. We run two sets of tests on the PCA-PRIM and CPCA-PRIM algorithms, the first using simulated test date and the second results from a model used in a previous RAND study of the cost-effectiveness of renewable energy portfolio standards. We find that the new algorithms produce higher quality scenarios than PRIM alone as evaluated by several important measures of merit. In the test data, PCA-PRIM produces improvements averaging 37 percent, and CPCA-PRIM averaging 14 percent, over PRIM alone."
According to the news editors, the research concluded: "In the renewable energy policy case study, PCA-PRIM and CPCA-PRIM exhibit similar improvements of about 16 percent over PRIM, and CPCA-PRIM generates scenarios interpretable by, and that provide useful information to, decision makers."
For more information on this research see: Improving scenario discovery using orthogonal rotations. Environmental Modelling & Software, 2013;48():49-64. Environmental Modelling & Software can be contacted at: Elsevier Sci Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, Oxon, England. (Elsevier - www.elsevier.com; Environmental Modelling & Software - www.elsevier.com/wps/product/cws_home/422921)
The news correspondents report that additional information may be obtained from S. Dalal, Pardee RAND Grad Sch, Santa Monica, CA 90405, United States. Additional authors for this research include B. Han, R. Lempert, A. Jaycocks and A. Hackbarth.
Keywords for this news article include: Oil & Gas, California, Algorithms, Santa Monica, United States, Renewable Energy, North and Central America
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