master-thesis
Science Score: 44.0%
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Low similarity (3.9%) to scientific vocabulary
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Repository
Basic Info
- Host: GitHub
- Owner: hbombDTU
- Language: Python
- Default Branch: main
- Size: 15.6 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Created about 3 years ago
· Last pushed almost 3 years ago
Metadata Files
Readme
Citation
README.md
Master-Thesis
Descripton:
This repository contains codes that have been used in my Master Thesis: Using Curtailed Power Measurements to Improve Probabilistic Forecasts
Code:
data_pre.py Preprares the data that is to be used for training the model. The script preprocesses and scales the input matrix needed for the regression and GMM models.
Tobit.py Models the data using Tobit regression.
JFST.py Models the data using JFST regression.
GMM.py Prepares the data to be used for the Gaussain Mixture Models (GMM). It also computes the baseline given in the paper.
Test:
test_clustering.py Tests the GMM.py script
test_regression.py Tests the Tobit.py and JFST.py script
Owner
- Login: hbombDTU
- Kind: user
- Repositories: 1
- Profile: https://github.com/hbombDTU
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Kwon" given-names: "Hyung-Jo" title: "Using Curtailed Power Measurements to Improve Probabilistic Forecasts" version: 1.0.0 doi: 10.5281/zenodo.1234 date-released: 2023-02-27 url: "https://github.com/hbombDTU/Master-Thesis"