venkatesh19v / solar-power-generation-forecasting-fork

Prediction of Solar Power Generated by a power plant using artificial neural networks

Forecasting the Power Generated by a Solar Plant

Solar power is a free and clean alternative to traditional fossil fuels.

However, nowadays, solar cells' efficiency is not as high as we would like, so selecting the ideal conditions for its installation is critical in obtaining the maximum amount of energy out of it.

We want to predict the power output for a particular array of solar power generators, knowing some environmental conditions.

Solar power forecasting is very usefull in smooth operation and control of solar power plant. Generation of energy by a solar panel or cell depends upon the doping level and design of solar PV array but the main factors are the amount of solar radiation falling on the panel, environmental factors like atmospheric temperature and humidity and dust present on the panels . These factors are naturally variable and hence the output of solar cell directly depend on it. Also, the solar irradiance as well as all the above-mentioned factors are variable throughout the day. Hourly average or average at a particular interval of time of these parameters received is measured for better prediction of output of a PV module and thus a solar power plant. The lower the sampling rate better will be the predication.

About DATA

• distance-to-solar-noon, in radians.
• temperature, daily average temperature, in degrees Celsius.
• wind-direction, daily average wind direction, in degrees (0-360).
• wind-speed, daily average wind speed, in meters per second.
• sky-cover, in a five-step scale, from 0 to 4, being 0 totally clear and 4 completely covered.
• visibility, in kilometers.
• humidity, in percentage.
• average-wind-speed-(period), average wind speed during the 3-hour period de measure was taken in, in meters per second.
• average-pressure-(period), average barometric pressure during the 3-hour period de measure was taken in, in mercury inches.
• power-generated, in jules for each 3-hour period.

Dependencies

• Python 3.8
• Pandas
• Tensorflow 2.2
• Keras
• Matplotlib
• Seaborn
• Numpy

Model Architecture

Results

• lasso regressor feature importance

Evaluation Parameter	Value
R2 Score of Whole Data Frame	0.881888
R2 Score of Training Set	0.921727
R2 Score of Test Set	0.767884
Mean of Test Set	1204.179810
Standard Deviation of Test Set	930.158020
Relative Standard Deviation	0.772441

Refrences

• Neural Designer
• Get Data