In the current climate of soaring energy prices, the company wants to offer a new service by which local clients that are near a source of renewable energy will have free energy when there is a surplus of energy in that area. In this system can reliably predict, at least 24 hours in advance, whether there will be a surplus of either wind or solar energy and it will send out an alert to the customers in the area to allow them to opt-in to the slot.
install requirements.txt in a Python>=3.11.0 environment
pip install -r requirements.txt # install
Exploration_preprocessing.ipynb contains the data exploration code for wether data in colchester and Brighton. Run the notebook and it will extract data form 'weatherdata_for_students' dataset. By changing filetype you can extract 'brighton' data or 'Colchester' data
filetype='brighton'
for file in file_list:
filenames = file.split('\\')[1].split('_')[0]
Modeling.ipynb notebook contains the Model, trainging and prediction. After running the all cells you can get system Recommendations base in our assumptions
training
Prediction
Recommendation
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Wind Energy Calculations Wind Energy
Power (W) = 1/2 x ρ x A x v3 Power = Watts
- ρ (rho, a Greek letter) = density of the air in kg/m3 -The standard(link is external) density of air is 1.225 kg/m3
- A = cross-sectional area of the wind in m2 - The turbine has a 24 m diameter, which means the radius is 12 m. Thus, the swept area of the turbine is: (pi)r2 = 3.14159(122) = 452.4 m2
- v = velocity of the wind in m/s
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Solar Energy Calculation - using 4 panale and convert megaW to kiloWatt
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Number of houses in brighton-b5 area. houses - 3,067 houses
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average electricity usage per month in UK households is about 350kWh electricity Usage
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Daily usage 350/31 = 11.29 Kwh