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[TASK] Technoeconomic HERON Use-case Upload (#31)
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* Added new folder with necessary files for use cases

* LMB Publication
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@sobinc1
sobinc1 authored Oct 8, 2024
1 parent 0b353a5 commit e555bc0
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31 changes: 31 additions & 0 deletions use_cases/2024_09_microreactor_LMB_system/ARMA/ERC/ARMA_pointer_fouryearrepeat.csv
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scaling,Year,filename
1,2018,ERCOT_WEST_RT_hr_2018.csv
1,2019,ERCOT_WEST_RT_hr_2019.csv
1,2020,ERCOT_WEST_RT_hr_2020.csv
1,2021,ERCOT_WEST_RT_hr_2021.csv
1,2022,ERCOT_WEST_RT_hr_2018.csv
1,2023,ERCOT_WEST_RT_hr_2019.csv
1,2024,ERCOT_WEST_RT_hr_2020.csv
1,2025,ERCOT_WEST_RT_hr_2021.csv
1,2026,ERCOT_WEST_RT_hr_2018.csv
1,2027,ERCOT_WEST_RT_hr_2019.csv
1,2028,ERCOT_WEST_RT_hr_2020.csv
1,2029,ERCOT_WEST_RT_hr_2021.csv
1,2030,ERCOT_WEST_RT_hr_2018.csv
1,2031,ERCOT_WEST_RT_hr_2019.csv
1,2032,ERCOT_WEST_RT_hr_2020.csv
1,2033,ERCOT_WEST_RT_hr_2021.csv
1,2034,ERCOT_WEST_RT_hr_2018.csv
1,2035,ERCOT_WEST_RT_hr_2019.csv
1,2036,ERCOT_WEST_RT_hr_2020.csv
1,2037,ERCOT_WEST_RT_hr_2021.csv
1,2038,ERCOT_WEST_RT_hr_2018.csv
1,2039,ERCOT_WEST_RT_hr_2019.csv
1,2040,ERCOT_WEST_RT_hr_2020.csv
1,2041,ERCOT_WEST_RT_hr_2021.csv
1,2042,ERCOT_WEST_RT_hr_2018.csv
1,2043,ERCOT_WEST_RT_hr_2019.csv
1,2044,ERCOT_WEST_RT_hr_2020.csv
1,2045,ERCOT_WEST_RT_hr_2021.csv
1,2046,ERCOT_WEST_RT_hr_2018.csv
1,2047,ERCOT_WEST_RT_hr_2019.csv
131 changes: 131 additions & 0 deletions use_cases/2024_09_microreactor_LMB_system/ARMA/ERC/ARMA_train_fouryearrepeat_pvt120.xml
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<?xml version="1.0" ?>
<Simulation verbosity="debug">
<RunInfo>
<WorkingDir>Output_2018_2021_presvInpCDF_0_1_F1095_F168_fouryearrepeat_pvt120</WorkingDir>
<Sequence>load, train, meta, serialize, sample, plotting</Sequence>
</RunInfo>

<Files>
<Input name="input">../ARMA_pointer_fouryearrepeat.csv</Input>
<Input name="pk">arma.pk</Input>
</Files>

<Steps>
<IOStep name="load">
<Input class="Files" type="">input</Input>
<Output class="DataObjects" type="HistorySet">input</Output>
<Output class="OutStreams" type="Print">test1</Output>
</IOStep>
<RomTrainer name="train">
<Input class="DataObjects" type="HistorySet">input</Input>
<Output class="Models" type="ROM">arma</Output>
</RomTrainer>
<IOStep name="meta">
<Input class="Models" type="ROM">arma</Input>
<Output class="DataObjects" type="DataSet">meta</Output>
<Output class="OutStreams" type="Print">romMeta</Output>
</IOStep>
<IOStep name="serialize">
<Input class="Models" type="ROM">arma</Input>
<Output class="Files" type="">pk</Output>
</IOStep>
<MultiRun name="sample">
<Input class="DataObjects" type="PointSet">placeholder</Input>
<Model class="Models" type="ROM">arma</Model>
<Sampler class="Samplers" type="MonteCarlo">mc</Sampler>
<Output class="OutStreams" type="Print">test2</Output>
<Output class="DataObjects" type="DataSet">synthetic</Output>
<Output class="OutStreams" type="Print">synthetic</Output>
</MultiRun>
<IOStep name="plotting">
<Input class="DataObjects" type="HistorySet">input</Input>
<Input class="DataObjects" type="HistorySet">synthetic</Input>
<Output class="OutStreams" type="Plot">plotters</Output>
</IOStep>
</Steps>

<DataObjects>
<PointSet name="placeholder">
<Input>scaling</Input>
<Output>OutputPlaceHolder</Output>
</PointSet>
<HistorySet name="input">
<Input>scaling,Year</Input>
<Output>price,hour</Output>
<options>
<pivotParameter>hour</pivotParameter>
</options>
</HistorySet>
<DataSet name="synthetic">
<Input>scaling</Input>
<Output>price</Output>
<Index var="hour">price</Index>
<Index var="Year">price</Index>
</DataSet>
<DataSet name="meta"/>
</DataObjects>

<Models>
<ROM name="arma" subType="ARMA">
<pivotParameter>hour</pivotParameter>
<Features>scaling</Features>
<Target>price,hour</Target>
<P>0</P>
<Q>1</Q>
<Fourier>8760,4380,2190,1095,168,24,12</Fourier>
<Segment grouping="interpolate">
<macroParameter>Year</macroParameter>
<subspace pivotLength="120" shift="zero">hour</subspace>
<evalMode>full</evalMode>
<Classifier class="Models" type="PostProcessor">classifier</Classifier>
</Segment>
<reseedCopies>True</reseedCopies>
<preserveInputCDF>True</preserveInputCDF>
<seed>42</seed>
</ROM>
<PostProcessor name="classifier" subType="DataMining">
<KDD labelFeature="labels" lib="SciKitLearn">
<Features>price</Features>
<SKLtype>cluster|KMeans</SKLtype>
<n_clusters>73</n_clusters>
</KDD>
</PostProcessor>
</Models>

<OutStreams>
<Print name="romMeta">
<type>csv</type>
<source>meta</source>
</Print>
<Print name="test1">
<type>csv</type>
<source>input</source>
</Print>
<Print name="test2">
<type>csv</type>
<source>placeholder</source>
</Print>
<Print name="synthetic">
<type>csv</type>
<source>synthetic</source>
</Print>
<Plot name="plotters" subType="SyntheticCloud">
<training>input</training>
<samples>synthetic</samples>
<macroParam>Year</macroParam>
<microParam>hour</microParam>
<variables>price</variables>
</Plot>
</OutStreams>

<Samplers>
<MonteCarlo name="mc">
<samplerInit>
<limit>1</limit>
<initialSeed>42</initialSeed>
</samplerInit>
<constant name="scaling">1</constant>
</MonteCarlo>
</Samplers>

</Simulation>
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