buzeraandClaude Opus 5.5
3afb6688a5
There was no evidence of how accurate the thermal model is: the only number was an in-sample 5-minute error. At every poll, record each room's reading, the outdoor temperature, the radiator-lag estimate and the model parameters. 15, 30, 60 and 120 minutes later, replay the room from that poll with the parameters it had then (out of sample), the outdoor temperature held constant and the radiator lag that actually followed from the relay's real history, and compare with the reading now. A no-change forecast is scored alongside. Only spans with regular polls and a well-known lag are scored. 7 days of per-room statistics are saved with the model (optional "evaluation" payload key; about 18 KB at most). Four new sensors, Heating MPC Forecast Error 15/30/60/120 min, show the mean absolute error with bias, persistence error, skill and per-room detail. On the synthetic lagged plant the 60-minute error is 0.03 °C against 0.39 °C for persistence. Room forecast entities gain forecast_15min and plan_15min attributes and are labelled counterfactual. Volatile attributes are unrecorded. The model is now saved after scoring, so saved statistics are current. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
171 lines
8.4 KiB
Python
171 lines
8.4 KiB
Python
"""Offline tests: run python -m unittest discover -s tests -v."""
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import importlib.util
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import unittest
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from pathlib import Path
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path = Path(__file__).resolve().parents[1] / 'custom_components' / 'heating_mpc' / 'physics.py'
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spec = importlib.util.spec_from_file_location('heating_mpc_physics', path)
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m = importlib.util.module_from_spec(spec)
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import sys
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sys.modules[spec.name] = m
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spec.loader.exec_module(m)
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class ModelTests(unittest.TestCase):
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def test_fitting_and_restore(self):
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learn = m.Learner()
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t = 20.0
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lag = 0.0
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for step in range(1000):
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heater = int(step % 96 < 35)
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lag = lag + m.HEAT_LAG * (heater - lag)
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value = t + 0.003 * (8 - t) + 0.055 * lag + 0.001
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learn.learn(t, value, 8, lag)
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t = value
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self.assertGreaterEqual(learn.count, 950)
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self.assertAlmostEqual(learn.theta[0], 0.003, delta=0.003)
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self.assertAlmostEqual(learn.theta[1], 0.055, delta=0.03)
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restored = m.Learner.from_dict(learn.as_dict())
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self.assertEqual(restored.count, learn.count)
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self.assertEqual(restored.theta, learn.theta)
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def test_optimize_warm_room_prefers_off(self):
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zones = {'living': 23.5, 'bedroom': 23.4}
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learners = {'living': m.Learner(), 'bedroom': m.Learner()}
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out = m.optimize(zones, 12, 0, 22, {'living': 2, 'bedroom': 1},
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learners, False, 3600, horizon_minutes=60)
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self.assertFalse(out['proposal'])
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self.assertEqual(len(out['plan_15min']), 4)
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def test_optimize_cold_room_prefers_heat(self):
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zones = {'living': 19.5, 'bedroom': 19.9}
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learners = {'living': m.Learner(), 'bedroom': m.Learner()}
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out = m.optimize(zones, 4, 0, 22, {'living': 2, 'bedroom': 1},
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learners, False, 3600, horizon_minutes=60)
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self.assertTrue(out['proposal'])
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def test_minimum_duration_on_and_off(self):
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learners = {'r': m.Learner()}
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# Could not switch OFF during first 15 minutes if just turned ON.
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out = m.optimize({'r': 24}, 12, 1, 22, {'r': 1}, learners, True, 120,
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minimum_on_minutes=15, horizon_minutes=60)
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self.assertTrue(out['proposal'])
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# Could not switch ON during first 10 minutes if just turned OFF.
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out = m.optimize({'r': 18}, 0, 0, 22, {'r': 1}, learners, False, 120,
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minimum_off_minutes=10, horizon_minutes=60)
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self.assertFalse(out['proposal'])
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def test_invalid_room_weights_rejected(self):
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with self.assertRaises(ValueError):
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m.optimize({'r': 20}, 10, 0, 22, {'r': 0}, {'r': m.Learner()},
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False, 1000, horizon_minutes=60)
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def test_short_horizon_does_not_claim_60_minute_prediction(self):
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out = m.optimize({'r': 20}, 5, 0, 22, {'r': 1}, {'r': m.Learner()},
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False, 3600, horizon_minutes=30)
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self.assertEqual(out['predicted_60m'], {})
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self.assertIn('r', out['predicted_end'])
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def test_prediction_matches_replay_at_60_minutes(self):
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learners = {'r': m.Learner()}
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out = m.optimize({'r': 20}, 5, .4, 22, {'r': 1}, learners, False, 3600,
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horizon_minutes=90)
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temps, lag = {'r': 20}, .4
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for index, bit in enumerate(out['plan_15min']):
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for _ in range(3):
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temps, lag = m.simulate_step(temps, 5, lag, bit, learners)
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if index == 3:
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self.assertEqual(out['predicted_60m']['r'], round(temps['r'], 3))
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self.assertEqual(out['trajectory_15min']['r'][index], round(temps['r'], 3))
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self.assertEqual(out['predicted_end']['r'], round(temps['r'], 3))
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self.assertEqual(len(out['trajectory_15min']['r']), 6)
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def test_jitter_normalized_and_rejection_counted(self):
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nominal, delayed = m.Learner(), m.Learner()
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self.assertTrue(nominal.learn(20, 20.1, 5, .5, command=True))
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self.assertTrue(delayed.learn(20, 20.11, 5, .5, 330, True))
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for a, b in zip(nominal.theta, delayed.theta):
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self.assertAlmostEqual(a, b)
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self.assertFalse(delayed.learn(20, 25, 5, .5, command=False))
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self.assertFalse(delayed.learn(20, 20.1, 5, 1.5, command=False))
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self.assertEqual((delayed.count, delayed.on_count, delayed.off_count, delayed.rejected_count),
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(1, 1, 0, 2))
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self.assertEqual(m.Learner.from_dict(delayed.as_dict()).rejected_count, 2)
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def test_corrupt_model_restore_is_atomic(self):
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for changes in ({'theta': [-1, .1, 0]}, {'p': [[-1, 0, 0], [0, 1, 0], [0, 0, 1]]},
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{'count': float('inf')}, {'count': '5'}, {'error_sum': float('nan')},
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{'off_count': 1}, {'theta': [float('nan'), 0, 0]}, {'p': None},
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{'p': [[1, .5, 0], [0, 1, 0], [0, 0, 1]]}, {'rejected_count': -1},
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{'rejected_count': '3'}):
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with self.subTest(changes=changes):
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data = m.Learner().as_dict()
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data.update(changes)
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self.assertEqual(m.Learner.from_dict(data), m.Learner())
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for bad in (None, [], 'broken'):
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self.assertEqual(m.Learner.from_dict(bad), m.Learner())
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def test_nonfinite_optimizer_inputs_rejected(self):
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for key, value in (('weights', {'r': float('nan')}), ('effective_heat', float('nan')),
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('switching_cost', float('inf')), ('seconds_since_change', -1),
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('minimum_on_minutes', -1), ('comfort_band', float('nan'))):
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args = dict(temps={'r': 20}, outside=5, effective_heat=0, target=22,
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weights={'r': 1}, learners={'r': m.Learner()}, is_on=False,
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seconds_since_change=3600, horizon_minutes=60)
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args[key] = value
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with self.subTest(key=key), self.assertRaises(ValueError):
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m.optimize(**args)
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def test_covariance_stays_bounded_without_heat_excitation(self):
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learn = m.Learner()
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t = 22.0
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for _ in range(30000): # about 104 days with the relay off
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value = t + 0.003 * (15 - t)
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self.assertTrue(learn.learn(t, value, 15, 0.0, command=False))
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t = value
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for i in range(3):
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self.assertLessEqual(learn.p[i][i], m.PRIOR_P[i][i])
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for j in range(3):
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self.assertEqual(learn.p[i][j], learn.p[j][i])
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restored = m.Learner.from_dict(learn.as_dict())
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self.assertEqual(restored.count, 30000)
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self.assertEqual(restored.p, learn.p)
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def test_restore_tolerates_round_off_and_bounds_wound_up_covariance(self):
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# Shape of a v0.2.1 model after a summer: huge heat variance, tiny asymmetry.
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data = m.Learner(count=40000, off_count=40000).as_dict()
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data['p'] = [[0.03, 1.0, 1e-3], [1.0 + 1e-7, 8.7e12, 0.0], [1e-3, 0.0, 0.0012]]
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restored = m.Learner.from_dict(data)
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self.assertEqual(restored.count, 40000)
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self.assertEqual(restored.p[1][1], m.PRIOR_P[1][1])
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self.assertEqual(restored.p[0][0], m.PRIOR_P[0][0])
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self.assertEqual(restored.p[0][1], restored.p[1][0])
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def test_lag_helpers(self):
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self.assertAlmostEqual(m.advance_lag(0, True, 300), m.HEAT_LAG)
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self.assertAlmostEqual(m.advance_lag(1, False, 1e9), 0)
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self.assertEqual(m.lag_from_time_in_state(True, 0), (0.5, 0.5))
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lag, half_width = m.lag_from_time_in_state(False, 3600)
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self.assertAlmostEqual(lag, half_width)
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self.assertLess(half_width, 0.03)
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# Any starting lag in [0, 1] ends inside the stated interval.
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for start in (0.0, 0.3, 1.0):
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for command in (False, True):
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lag, half_width = m.lag_from_time_in_state(command, 900)
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self.assertLessEqual(abs(m.advance_lag(start, command, 900) - lag), half_width + 1e-12)
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def test_relay_segments(self):
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seg = m.relay_segments
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self.assertEqual(seg(False, 0, [], 300), [(False, 300)])
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self.assertEqual(seg(False, 0, [(60, 'on'), (200, 'off')], 300),
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[(False, 60), (True, 140), (False, 100)])
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self.assertEqual(seg(True, 0, [(300, 'off')], 300), [(True, 300), (False, 0)])
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for changes in ([(60, 'unavailable')], [(60, 'off')], [(200, 'on'), (60, 'off')],
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[(-1, 'on')], [(301, 'on')]):
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with self.subTest(changes=changes):
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self.assertIsNone(seg(False, 0, changes, 300))
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if __name__ == '__main__':
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unittest.main()
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