https://github.com/abbey2017/wind-energy-analytics
Physics-guided data-driven solutions for the wind energy industry
https://github.com/abbey2017/wind-energy-analytics
artificial-intelligence machine-learning wind-energy wind-energy-analytics wind-turbine
Last synced: 3 months ago
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Physics-guided data-driven solutions for the wind energy industry
- Host: GitHub
- URL: https://github.com/abbey2017/wind-energy-analytics
- Owner: abbey2017
- License: bsd-3-clause
- Created: 2020-07-25T21:54:06.000Z (almost 6 years ago)
- Default Branch: main
- Last Pushed: 2025-06-18T22:39:41.000Z (about 1 year ago)
- Last Synced: 2025-09-24T23:55:36.935Z (9 months ago)
- Topics: artificial-intelligence, machine-learning, wind-energy, wind-energy-analytics, wind-turbine
- Language: Python
- Homepage:
- Size: 62.3 MB
- Stars: 24
- Watchers: 2
- Forks: 10
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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README
This library helps lower the entry barrier for domain experts
in the wind energy industry to leverage advanced analytics and methodologies
developed in related scientific research.
Modules are implemented for different routine and non-routine analysis of time series
data collected from operating wind turbines. Practicing wind engineers and analysts
can build on fundamental modules as part of a larger project development.
### Library installation
- Clone the github repo to have access to example notebooks and public data.
- Next, pip install the library
```
# Clone github repo
git clone https://github.com/abbey2017/wind-energy-analytics.git
# Pip install library
pip install scada-data-analysis
```
### Current modules
- Iterative power curve filter (returns normal and abnormal datapoints)
- Expected power estimator (returns expected power based on operational data)
### Usage of power curve filter
```
import pandas as pd
from scada_data_analysis.modules.power_curve_preprocessing import PowerCurveFiltering
# Load turbine scada data
df = pd.read_csv('path\to\data')
# Instantiate power curve filtering class
pc_filter = PowerCurveFiltering(turbine_label='Wind_turbine_name', windspeed_label='Ws_avg',
power_label='P_avg', data=df, cut_in_speed=3, bin_interval=0.5,
z_coeff=2.5, filter_cycle=5, return_fig=True, image_path='..\images')
# Process raw scada data
normal_df, abnormal_df = pc_filter.process()
```
### Usage of expected power estimator
```
import pandas as pd
from scada_data_analysis.modules.expected_power import ExpectedPower
# Load turbine scada data
df = pd.read_csv('path\to\data')
# Instantiate expected power estimator
power_model = ExpectedPower(turbine_label='Wind_turbine_name', windspeed_label='Ws_avg',
power_label='P_avg', method='binning', kind='linear')
# Fit estimator based on training data
power_model = power_model.fit(train_df)
# Estimate expected power based on operation power curve
pred_df = power_model.predict(test_df)
```