rocco-haro/renewable-energy-prediction

Forecast environmental variables to forecast power production from renewable sources of energy.

Versions

• Tensorflow v1.3
• http://tflearn.org/installation/

Power-Prediction

Produce a temporal prediction of overall substation power generation & consumption.

Requirements

• Perform literature review
• Be able to predict the next hour's consumption/generation with >90% accuracy
• Be able to predict up to 12 hours from current period

Input

• Missing Data? Use a polynomial fit of other points to fill it in
• Submodels for discrete power sources. (e.g. unique submodel for three different types of solar panels)

Output

Report for each substation:

• Generation (single number in KW/h)
• Consumption (single number in KW/h)
• Accuracy
• Error
• Deviation and other statistics that we may find useful for validation

Data

• Freq of 10 m, for one year
• wind direction & amplitude (No direct measurement from wind turbines)
• diesel generator output can be considered the demand, little buffer
• temperature of environment (affects power generation from solar and power consumption from populace)

Consider:

• school days (consumes more power than usual)
• build dynamically so that more sources of energy can be added to model

Tips

• Pull any additional data we may need
• Angle of the sun
• Utilize momentum in calculations (SMA for the previous hours leading up to the estimation time)

Credits

• For lstm model: https://github.com/RobRomijnders/LSTM_tsc