Spaces:
Sleeping
Sleeping
File size: 1,653 Bytes
d0f2767 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 |
from energy_prediction.EnergyPredictionModel import EnergyPredictionModel
from energy_prediction.EnergyPredictionPipeline import EnergyPredictionPipeline
import paho.mqtt.client as mqtt
import json
broker_address = "localhost"
broker_port = 1883
topic = "sensor_data"
client = mqtt.Client(mqtt.CallbackAPIVersion.VERSION2)
def main():
prediction_data_pipeline_north = EnergyPredictionPipeline(scaler_path="src\energy_prediction\models\scalerNorth.pkl", wing='north')
prediction_data_pipeline_south = EnergyPredictionPipeline(scaler_path="src\energy_prediction\models\scalerSouth.pkl", wing='south')
# Energy Prediction North wing
energy_prediction_north = EnergyPredictionModel(
model_path="src/energy_prediction/models/lstm_energy_north_01.keras"
)
# Energy Prediction South wing
energy_prediction_south = EnergyPredictionModel(
model_path="src/energy_prediction/models/lstm_energy_south_01.keras"
)
def on_message(client, userdata, message):
dfN = prediction_data_pipeline_north.fit(message)
dfS = prediction_data_pipeline_south.fit(message)
if not(dfN is None and dfS is None):
outN = energy_prediction_north.pipeline(dfN, prediction_data_pipeline_north.scaler)
outS = energy_prediction_south.pipeline(dfS, prediction_data_pipeline_south.scaler)
return outN, outS
else:
return None
print("Connecting to broker")
client.on_message = on_message
client.connect(broker_address, broker_port)
client.subscribe(topic)
client.loop_forever()
if __name__ == "__main__":
main()
|