References
https://www.hindawi.com/journals/mpe/2010/684742/
https://www.sciencedirect.com/science/article/abs/pii/S0925231201007020
http://www.caiso.com/market/Pages/ReportsBulletins/RenewablesReporting.aspx
https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html
WHY US?
AI/ML in Wind Power Forecasting
Power from any renewable sources(e.g. solar,wind,etc) is always unpredictable so predictive models are required for capacity, storage and grid-hop planning.
Microgrid operators need to know how much electricity will be generated in the next hour vs next 24 hours vs next month which requires more accurate prediction of power generation. And the only way to achieve this accuracy is through predictive AI models which need data points like weather, plant uptime/downtime, irradiation, station level, etc.
“Clean” and “Curated” Data that we can compliment in your modeling : Our curated data contains various weather, turbine and rotor features. Data has been recorded from January 2018 till March 2020 at every 10-minute interval.
Datasets
Wind Turbine Scada Dataset
For Wind Turbines, scada systems measure and save data points like wind speed, wind direction, generated power etc. for every 10 minutes intervals. The data points in the file are:
- Date/Time (for 10 minutes intervals)
- LV ActivePower (kW): The power generated by the turbine for that moment
- Wind Speed (m/s): The wind speed at the hub height of the turbine (the wind speed that turbine use for electricity generation)
- Theoretical Power Curve (KWh): The theoretical power values that the turbine generates with that wind speed which is given by the turbine manufacturer
- Wind Direction (°): The wind direction at the hub height of the turbine (wind turbines turn to this direction automatically)
Wind Power Forecasting
Here’s data of a certain windmill.It contains various weather, turbine and rotor features. Data has been recorded from January 2018 till March 2020. Readings have been recorded at a 10-minute interval. The aim is to predict the wind power that could be generated from the windmill for the next 15 days using data points like
- ActivePower
- Ambient Temperature
- BearingShaftTemperature
- Blade1PitchAngle
- Blade2PitchAngle
- Blade3PitchAngle
- ControlBoxTemperature
- GearboxBearingTemperature
- GearboxOilTemperature
- GeneratorRPM
- GeneratorWinding1Temperature
- GeneratorWinding2Temperature
- HubTemperature
- MainBoxTemperature
- NacellePosition
- ReactivePower
- RotorRPM
- TurbineStatus
- WTG
- WindDirection
Wind Power Generation Forecast
| Wind Power Generation Forecast by Coupling Numerical Weather Prediction Model and Gradient Boosting Machines in Yahyalı Wind Power Plant |
Wind hourly generation time series
| This dataset is from Sotavento wind farm located in Galicia, Spain It consists of 24 wind turbines with an installed capacity of 17.56 MW. The historical wind speed, wind direction and total power output of 24 wind turbines with 10-min resolution of this wind farm in 2016 are listed. |
Data-Driven Wind Power Forecast
| This dataset corresponds to the results of the paper titled:”Data-Driven Wind Power Forecast for Very Short Horizons”4th International Conference on Smart Energy Systems and Technologies (SEST) – 2021 |
Meteomatics Wind forecasts
| This dataset is provided by Meteomatics |
Wind Power Forecasting Competition Data
| This dataset comprises of data from the Wind Power Forecasting Competition run in conjunction with the 14th International Conference on the European Energy Market, as well as the code (in R) and entries generated by the winning team.Competition Data includes 1-year of training data (wind power generation and weather data) provided by the competition organisers, and competition period input data. |
Data Cleaning And Data Prep Tools Used By Our Team
The Project team will use the appropriate tools based on volume, location and nature of the client data and deliver the cleaned/formatted data with visualization or ingested in a database-whatever way a client will demand. They can also run initial POC with client suggested tools/machines to do initial QC of the data and feasibility of algorithmic solution.
Data Cleaning and Data Prep Tools used by our team
