73 lines
1.4 KiB
Python
73 lines
1.4 KiB
Python
from pathlib import Path
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from shutil import copyfile
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import pickle
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import numpy as np
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import pandas as pd
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from sklearn.preprocessing import MinMaxScaler, RobustScaler
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from sklearn.exceptions import NotFittedError
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import gpflow
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import tensorflow as tf
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import matplotlib.pyplot as plt
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from gpflow.utilities import print_summary
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import casadi as cs
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import callbacks
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from helpers import *
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from controllers import *
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from time import sleep
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####################
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# Define dict_cols #
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####################
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# dict_cols is used to compute the autoregressive lags that are passed to the GP
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# model at training/evaluation.
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t_cols = []
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w_cols = ['SolRad', 'OutsideTemp']
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u_cols = ['SimulatedHeat']
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y_cols = ['SimulatedTemp']
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t_lags = 0
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w_lags = 1
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u_lags = 2
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y_lags = 3
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dict_cols = {
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't': (t_lags, t_cols),
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'w': (w_lags, w_cols),
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'u': (u_lags, u_cols),
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'y': (y_lags, y_cols)
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}
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N_horizon = 10
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controller = SVGP_MPCcontroller(dict_cols = dict_cols, N_horizon = N_horizon)
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idx = 0
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while True:
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u = controller.get_control_input()
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# Measure disturbance
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w = np.random.rand(2)
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controller.add_disturbance_measurement(w)
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w_forecast = np.random.rand(N_horizon, 2)
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controller.set_weather_forecast(w_forecast)
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# Measure output
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y = np.random.rand(1)
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controller.add_output_measurement(y)
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# Update model
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controller.update_model()
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idx += 1
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pass
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