Replication Data for: A gradient boosting approach for optimal selection of bidding strategies: Simple model - Original variables (doi:10.18710/WNKSVX)

View:

Part 1: Document Description
Part 2: Study Description
Part 3: Data Files Description
Part 4: Variable Description
Part 5: Other Study-Related Materials
Entire Codebook

Document Description

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

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]

Study Description

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

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

Sources Statement

Data Access

Other Study Description Materials

Related Publications

Citation

Title:

submitted for review

Bibliographic Citation:

submitted for review

File Description--f18953

File: simple model original variables.tab

  • Number of cases: 1045

  • No. of variables per record: 15

  • Type of File: text/tab-separated-values

Notes:

UNF:6:gXehgeAeqs6FWVHODiWuAQ==

Variable Description

List of Variables:

Variables

id

f18953 Location:

Summary Statistics: Max. 1072.0; Min. 0.0; Valid 1045.0; StDev 308.5419094521152; Mean 533.0889952153133

Variable Format: numeric

Notes: UNF:6:SkWz/q58eu38hJvqHzigUA==

issue_date

f18953 Location:

Variable Format: character

Notes: UNF:6:KjsIwoncK/8eKf5jI/iMOQ==

value_date

f18953 Location:

Variable Format: character

Notes: UNF:6:lXuef8uyBsOb9PFAqCG63g==

DET_1

f18953 Location:

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==

STOCH_0

f18953 Location:

Summary Statistics: Valid 1045.0; Max. 2595.7244037039927; Min. 0.9372676899656653; Mean 131.79205058698605; StDev 164.67088785740788;

Variable Format: numeric

Notes: UNF:6:iDJ8Cgq4jFz9p5izfvHNKg==

DELTA

f18953 Location:

Summary Statistics: Valid 1045.0; StDev 129.3999850518011; Min. -835.2433812799864; Mean -29.206385667235487; Max. 543.1424180299509

Variable Format: numeric

Notes: UNF:6:3Ku+gOfhCGdaDdjGPk2EKA==

BEST

f18953 Location:

Summary Statistics: Max. 1.0; StDev 0.4957943345970733; Valid 1045.0; Min. 0.0; Mean 0.4334928229665076

Variable Format: numeric

Notes: UNF:6:4gfbuV0gUgMtzO8KykvyqA==

inflow_deviation

f18953 Location:

Summary Statistics: Min. -5.72833333333333; Max. 31.8758333333333; StDev 3.1943247285867504; Valid 1045.0; Mean 0.09452230342902737

Variable Format: numeric

Notes: UNF:6:aiQvt/RV1whOzLE+blKA9w==

reservoir_filling_1

f18953 Location:

Summary Statistics: Min. 0.194841981132073; Valid 1045.0; StDev 0.19756646381156925; Max. 1.0222869257969; Mean 0.7191990186761885

Variable Format: numeric

Notes: UNF:6:CLgO9ezwQRcEfSakIWbOOg==

reservoir_filling_2

f18953 Location:

Summary Statistics: StDev 0.17062213302058676; Mean 0.8513437250695532; Min. 0.170804121817984; Valid 1045.0; Max. 1.10756254610908

Variable Format: numeric

Notes: UNF:6:XBZwRhEpqXtPO/g/sw7uww==

vol_p

f18953 Location:

Summary Statistics: Max. 1.95103977154963; Min. 0.0126441056154704; StDev 0.2512141809351983; Valid 1045.0; Mean 0.24778741826768863

Variable Format: numeric

Notes: UNF:6:qFXjnOom3uHPNaQ9SXGomQ==

vol_prog

f18953 Location:

Summary Statistics: Min. 0.00370908416530143; StDev 0.15884814522651844; Mean 0.1897966076122498; Valid 1045.0; Max. 1.18475424562604;

Variable Format: numeric

Notes: UNF:6:37y1tNs9HhBT6sJrO7PWtg==

water_value

f18953 Location:

Summary Statistics: StDev 11.455056337152207; Valid 1045.0; Max. 47.5204081632653; Mean 19.97824089444386; Min. 0.0

Variable Format: numeric

Notes: UNF:6:C66X83VPXkxHVFTlD9lcTA==

average_p

f18953 Location:

Summary Statistics: Mean 32.34073684210522; Valid 1045.0; Min. 14.81375; Max. 66.1604166666666; StDev 9.879162096000973;

Variable Format: numeric

Notes: UNF:6:TElfbW07vTYpj26s1pqlxw==

average_prog

f18953 Location:

Summary Statistics: Max. 72.7333333333333; Mean 32.30271969696965; StDev 9.868419240058609; Min. 14.5466666666666; Valid 1045.0

Variable Format: numeric

Notes: UNF:6:JCmIaQPqhbu8cGl+3a10XQ==

Other Study-Related Materials

Label:

00_ReadMe.txt

Notes:

text/plain