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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: A gradient boosting approach for optimal selection of bidding strategies: Simple model - Original variables |
Identification Number: |
doi:10.18710/WNKSVX |
Distributor: |
DataverseNO |
Date of Distribution: |
2020-04-20 |
Version: |
1 |
Bibliographic Citation: |
Riddervold, Hans Ole, 2020, "Replication Data for: A gradient boosting approach for optimal selection of bidding strategies: Simple model - Original variables", https://doi.org/10.18710/WNKSVX, DataverseNO, V1, UNF:6:gXehgeAeqs6FWVHODiWuAQ== [fileUNF] |
Citation |
|
Title: |
Replication Data for: A gradient boosting approach for optimal selection of bidding strategies: Simple model - Original variables |
Identification Number: |
doi:10.18710/WNKSVX |
Authoring Entity: |
Riddervold, Hans Ole (NTNU – Norwegian University of Science and Technology) |
Other identifications and acknowledgements: |
Riemer-Sørensen, Signe |
Other identifications and acknowledgements: |
Szederjesi, Peter |
Producer: |
NTNU – Norwegian University of Science and Technology |
Distributor: |
DataverseNO |
Distributor: |
NTNU – Norwegian University of Science and Technology |
Access Authority: |
Riddervold, Hans Ole |
Depositor: |
Riddervold, Hans Ole |
Date of Deposit: |
2020-04-16 |
Holdings Information: |
https://doi.org/10.18710/WNKSVX |
Study Scope |
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Keywords: |
Computer and Information Science, Engineering, reservoir hydro, bidding strategies, hydropower, gradient boosting, neural network |
Abstract: |
Access to an increasing amount of data opens for the application of machine learning models to predict the best combination of models and strategies for bidding of hydro power in a de-regulated market for any given day. This data-set describe the historical performance-gap of two given bidding strategies over several years (2016-2018). Data from two different bidding strategies are presented in the data-set. The first is bidding the expected volume. The expected volumes are found by deterministic optimization against forecasted price and inflow using the SHOP software, and are submitted as fixed hourly bids to the Nord Pool power exchange. The second strategy is stochastic bidding. The stochastic model is based on the deterministic method, but allows for a stochastic representation of inflow to the reservoir and day-ahead market prices. SHOP is a software tool for optimal short-term hydropower scheduling developed by SINTEF Energy Research, used by many hydropower producers in the Nordic market. The total performance-gap for the two strategies in the data-set are calculated as the difference between the optimum value for the relevant bidding date and the value of the investigated strategy. A high number for indicate poor performance. In addition, a set of of relevant variables accessible prior to bidding have been collected and are published in the data-set. Realized- and prognosed prices in the data-set are prices for the NO2 area in Nordpool. The reservoir and watervalue in the data-set are associated with a river system located in south-western Norway |
Time Period: |
2016-01-01-2018-12-31 |
Country: |
Norway |
Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
submitted for review |
Bibliographic Citation: |
submitted for review |
File Description--f18953 |
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File: simple model original variables.tab |
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Notes: |
UNF:6:gXehgeAeqs6FWVHODiWuAQ== |
List of Variables: |
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Variables |
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f18953 Location: |
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f18953 Location: |
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Summary Statistics: Valid 1045.0; StDev 207.8402226798028; Mean 160.99843625422176; Min. 1.3650447097606957; Max. 2745.9187442910916 Variable Format: numeric Notes: UNF:6:0WVPgqBRXm4K/YY/y7UtzA== |
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f18953 Location: |
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Label: |
00_ReadMe.txt |
Notes: |
text/plain |