Abriliam Consulting — Industrial Energy Management
Before building any models, we need to understand the shape and character of the data. This notebook examines the chiller plant dataset through summary statistics, time-series trends, and correlation analysis to surface anomalies and guide the diagnostic investigation.
Missing values in each column:
oat_C 0
wb_C 0
occ 0
tons 0
chw_sup_C 0
chw_ret_C 0
chw_dT_C 0
chw_flow_m3h 0
cw_sup_C 0
cw_ret_C 0
cw_dT_C 0
cw_flow_m3h 0
approach_C 0
dp_kpa 0
chiller_kw 0
tower_fan_kw 0
chw_pump_kw 0
cw_pump_kw 0
plant_kw 0
kw_per_ton 0
plant_kw_per_ton 0
tower_fan_kw_per_ton 0
pumping_kw_per_ton 0
dtype: int64
Summary statistics:
oat_C wb_C occ tons chw_sup_C \
count 1344.000000 1344.000000 1344.000000 1344.000000 1344.000000
mean 21.971768 18.469857 0.437281 202.310180 6.497118
std 5.537674 5.223145 0.236582 72.427236 0.199888
min 9.528023 7.674085 0.003374 40.000000 5.814391
25% 17.243689 13.835159 0.298208 147.201633 6.359207
50% 22.019321 18.588505 0.368380 214.134704 6.508027
75% 26.550146 23.203806 0.601501 257.748715 6.637654
max 34.455590 29.209971 0.985438 360.631196 7.078945
chw_ret_C chw_dT_C chw_flow_m3h cw_sup_C cw_ret_C ... \
count 1344.000000 1344.000000 1344.000000 1344.000000 1344.000000 ...
mean 11.698814 5.201697 123.286156 23.223926 27.521415 ...
std 0.977320 0.958199 53.350509 5.367137 5.622375 ...
min 9.626562 3.216000 18.801814 10.437609 14.091599 ...
25% 10.794124 4.256318 82.148731 18.504226 22.558544 ...
50% 11.957734 5.557457 122.019542 23.301704 27.724563 ...
75% 12.561173 6.052536 158.086490 27.887926 32.345008 ...
max 13.841881 7.081364 312.989726 35.000361 40.469019 ...
dp_kpa chiller_kw tower_fan_kw chw_pump_kw cw_pump_kw \
count 1344.000000 1344.000000 1344.000000 1344.000000 1344.000000
mean 175.668842 645.271362 25.011770 43.988172 8.599194
std 19.673575 190.714987 7.080368 21.491271 2.602534
min 134.258563 202.598553 8.978128 6.000000 5.000000
25% 158.402368 488.184334 19.638425 27.582625 6.388679
50% 171.764386 676.740724 23.809398 41.524940 8.381974
75% 193.622700 807.096917 30.098654 59.688591 10.689970
max 219.801919 900.000000 46.654823 85.000000 16.127471
plant_kw kw_per_ton plant_kw_per_ton tower_fan_kw_per_ton \
count 1344.000000 1344.000000 1344.000000 1344.000000
mean 722.870498 3.331991 3.740465 0.147641
std 211.832906 0.534258 0.623603 0.087723
min 228.816352 2.495624 2.775560 0.032155
25% 549.172073 3.046748 3.399471 0.090503
50% 757.822436 3.191399 3.583410 0.121378
75% 899.344803 3.405071 3.836126 0.170387
max 1041.126945 6.857364 7.590985 0.633392
pumping_kw_per_ton
count 1344.000000
mean 0.260833
std 0.064297
min 0.143180
25% 0.203011
50% 0.242365
75% 0.317086
max 0.560377
[8 rows x 23 columns]
No missing values across all 23 columns — the dataset is complete. Key observations from the summary statistics:
The triple-axis time-series plot reveals several important patterns:
The 240-hour (10-day) simple moving average of kW/ton shows a clear upward drift starting in early July. This long-term trend confirms that something changed in plant operations — the plant is becoming less efficient even after smoothing out weather and load variability. Weekend periods (shaded) consistently show higher kW/ton due to the part-load penalty.
Condenser water flow and chiller efficiency are correlated — higher CW flow corresponds to higher loads and generally better kW/ton. The spline smoothing helps visualize the underlying trend without hourly noise. Both metrics show seasonal variation driven by outdoor conditions.
| oat_C | wb_C | occ | tons | chw_sup_C | chw_ret_C | chw_dT_C | chw_flow_m3h | cw_sup_C | cw_ret_C | ... | cw_pump_kw | plant_kw | kw_per_ton | plant_kw_per_ton | tower_fan_kw_per_ton | pumping_kw_per_ton | kw_per_ton_15_sma | kw_per_ton_5_sma | kw_per_ton_24_sma | kw_per_ton_240_sma | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1344.000000 | 1344.000000 | 1344.000000 | 1344.000000 | 1344.000000 | 1344.000000 | 1344.000000 | 1344.000000 | 1344.000000 | 1344.000000 | ... | 1344.000000 | 1344.000000 | 1344.000000 | 1344.000000 | 1344.000000 | 1344.000000 | 1330.000000 | 1340.000000 | 1344.000000 | 1344.000000 |
| mean | 21.971768 | 18.469857 | 0.437281 | 202.310180 | 6.497118 | 11.698814 | 5.201697 | 123.286156 | 23.223926 | 27.521415 | ... | 8.599194 | 722.870498 | 3.331991 | 3.740465 | 0.147641 | 0.260833 | 3.325178 | 3.329938 | 3.332719 | 3.367520 |
| std | 5.537674 | 5.223145 | 0.236582 | 72.427236 | 0.199888 | 0.977320 | 0.958199 | 53.350509 | 5.367137 | 5.622375 | ... | 2.602534 | 211.832906 | 0.534258 | 0.623603 | 0.087723 | 0.064297 | 0.351797 | 0.436612 | 0.325486 | 0.173593 |
| min | 9.528023 | 7.674085 | 0.003374 | 40.000000 | 5.814391 | 9.626562 | 3.216000 | 18.801814 | 10.437609 | 14.091599 | ... | 5.000000 | 228.816352 | 2.495624 | 2.775560 | 0.032155 | 0.143180 | 2.936343 | 2.760202 | 3.037449 | 3.125798 |
| 25% | 17.243689 | 13.835159 | 0.298208 | 147.201633 | 6.359207 | 10.794124 | 4.256318 | 82.148731 | 18.504226 | 22.558544 | ... | 6.388679 | 549.172073 | 3.046748 | 3.399471 | 0.090503 | 0.203011 | 3.106304 | 3.074437 | 3.105068 | 3.246344 |
| 50% | 22.019321 | 18.588505 | 0.368380 | 214.134704 | 6.508027 | 11.957734 | 5.557457 | 122.019542 | 23.301704 | 27.724563 | ... | 8.381974 | 757.822436 | 3.191399 | 3.583410 | 0.121378 | 0.242365 | 3.195772 | 3.199272 | 3.197062 | 3.385068 |
| 75% | 26.550146 | 23.203806 | 0.601501 | 257.748715 | 6.637654 | 12.561173 | 6.052536 | 158.086490 | 27.887926 | 32.345008 | ... | 10.689970 | 899.344803 | 3.405071 | 3.836126 | 0.170387 | 0.317086 | 3.375653 | 3.407882 | 3.449104 | 3.413820 |
| max | 34.455590 | 29.209971 | 0.985438 | 360.631196 | 7.078945 | 13.841881 | 7.081364 | 312.989726 | 35.000361 | 40.469019 | ... | 16.127471 | 1041.126945 | 6.857364 | 7.590985 | 0.633392 | 0.560377 | 4.729038 | 5.944731 | 4.878574 | 4.878574 |
8 rows × 27 columns