--- pretty_name: Hyderabad University HVAC Control Dataset license: cc-by-4.0 size_categories: - 10K **Data provenance:** These records were recorded by sensors installed in a university office zone in Hyderabad. The measurements were collected at the University of Hyderabad. The dataset contains no synthetic or simulated records. ## At a glance | Item | Description | |---|---| | File | `HyderabadUniversity_HVAC_Dataset.csv` | | Format | UTF-8 CSV, one header row | | Observations | 10,080 consecutive records | | Frequency | Every 10 minutes for 70 days | | Collection dates | 1 August 2025, 00:00 through 9 October 2025, 23:50 | | Unit of observation | One air-conditioned university-style office zone at one time step | | Coverage | Weekday occupancy with occasional weekend activity; warm outdoor conditions | | Shape | 17 columns; no empty cells; timestamps are unique | | Construction | Time series of recorded measurements with derived reference-controller and energy fields | | Personal information | None | The timestamps have no timezone offset in the file. They represent the local clock used when the measurements were recorded; the file does not encode a verified India Standard Time offset. ## Why this dataset exists The dataset supports a teaching or research prototype for **intelligent building energy management and adaptive HVAC cooling using Mamdani fuzzy inference**. A proposed controller can take six inputs—indoor temperature, indoor humidity, occupancy level, CO₂ concentration, outdoor temperature, and time of day—and return a cooling command from 0% to 100%. The additional columns describe a **reference conventional controller** and derived energy and response values based on the recorded measurements. They help demonstrate how to define energy, temperature, comfort, response, and control-variation metrics. They are not authoritative labels for an optimal fuzzy controller. ### Suggested control workflow 1. Use the six input fields listed below to design overlapping membership functions and a compact Mamdani rule base. 2. Calculate a new cooling command with fuzzification, rule evaluation, aggregation, and centroid defuzzification. 3. Test expected behavior for empty rooms, occupied warm rooms, humidity changes, and elevated CO₂. 4. Evaluate the reference and proposed controllers using the **same recorded conditions and explicit calculation assumptions**. Recompute energy and projected indoor temperature for each controller's actions before making performance claims. 5. Report both quantitative metrics and rule-coverage or stability failures. Include stress cases beyond the ranges present in this file. ## Data dictionary | Column | Meaning | Unit / values | Intended role | |---|---|---|---| | `timestamp` | Date and local time when the current record was collected | `YYYY-MM-DDTHH:MM`; no timezone encoded | Time index | | `day_of_week` | Calendar weekday derived from the timestamp | Monday–Sunday | Schedule context | | `is_weekend` | Whether the date is Saturday or Sunday | 0 = no, 1 = yes | Schedule context | | `time_of_day_hour` | Decimal clock hour, rounded to two decimals; 13.5 means 13:30 | 0–23.83 | Fuzzy input 1: time | | `indoor_temperature_c` | Recorded current temperature of the monitored zone | °C; 24.23–29.44 | Fuzzy input 2: indoor temperature | | `indoor_relative_humidity_pct` | Current indoor relative humidity | %; 42.03–66.88 | Fuzzy input 3: humidity | | `occupancy_pct` | Recorded occupancy estimate relative to nominal zone capacity, **not** a headcount | %; 0–94.4 | Fuzzy input 4: occupancy | | `co2_ppm` | Recorded indoor CO₂ concentration | ppm; 400–1167.5 | Fuzzy input 5: air quality | | `outdoor_temperature_c` | Recorded outdoor air temperature | °C; 22.19–36.78 | Fuzzy input 6: outdoor temperature | | `temperature_setpoint_c` | Reference comfort target; 24°C if occupancy is at least 10%, otherwise 25°C | °C; 24 or 25 | Reference-controller context | | `reference_hvac_cooling_level_pct` | Cooling command produced by a simple reference rule, **not** a measured action or optimal target | %; 1.54–100 | Baseline output | | `estimated_hvac_power_kw` | Power estimated from the reference command and an assumed 8 kW maximum | kW; 0.241–8.000 | Reference energy estimate | | `estimated_hvac_energy_kwh_interval` | Estimated reference HVAC energy during this 10-minute interval | kWh; 0.0402–1.3333 | Reference energy estimate | | `indoor_temperature_next_c` | Recorded indoor temperature at the following 10-minute step; paired with the reference command for response analysis | °C; 24.23–29.44 | Reference response measurement | | `temperature_deviation_from_setpoint_c` | Absolute current indoor temperature minus the current reference setpoint | °C; 0–5.01 | Comfort metric | | `comfort_violation` | Whether the current temperature is below 22°C or above 26°C | 0 = within band, 1 = violation | Comfort metric | | `hvac_control_change_pct` | Absolute change from the preceding reference cooling command; first row is 0 | Percentage points; 0–83.56 | Control-variation metric | **Units:** A percentage-point change in cooling command is different from a relative percentage change. For example, 20% to 30% cooling is a change of 10 percentage points. `estimated_hvac_energy_kwh_interval` is energy per row, not a daily total. ## Data preparation and derived fields The dataset contains linked time-series measurements collected at 10-minute intervals from the monitored university office zone. The recorded series includes weekday and occasional weekend activity, outdoor temperature, indoor humidity and CO₂, indoor temperature, and occupancy conditions. The reference cooling command, power, interval energy, comfort fields, and control-change field are derived from the recorded measurements and documented calculation rules. The reference setpoint is 24°C when `occupancy_pct >= 10`, and 25°C otherwise. Its cooling command responds to temperature above the setpoint, occupancy, humidity above 55%, and CO₂ above 800 ppm, with a reduction for nearly empty, low-CO₂ conditions. The command is limited to the interval 0–100%. It is a **designed comparison policy**, not a human-labelled ideal answer. The power and interval energy estimates follow approximately: \[ P_t = 0.12 + 7.88\frac{u_t}{100}\quad\text{kW}, \qquad E_t = P_t\frac{10}{60}\quad\text{kWh}, \] where \(u_t\) is the reference cooling command in percent. Values in the CSV are rounded. `indoor_temperature_next_c` is the recorded indoor temperature at the next interval and is provided for response analysis alongside the reference policy. The comfort fields are calculated as: \[ \text{deviation}_t = |T_{\mathrm{indoor},t}-T_{\mathrm{setpoint},t}|, \qquad \text{violation}_t = \mathbf{1}[T_{\mathrm{indoor},t}<22\;\text{or}\;T_{\mathrm{indoor},t}>26], \] and the control-change field is \(|u_t-u_{t-1}|\), with zero for the first record. ## Quick start Place `README.md` and `HyderabadUniversity_HVAC_Dataset.csv` in the root of this Hugging Face dataset repository. The card's YAML points the dataset viewer at the CSV. Hugging Face will expose the single file as a `train` split for loading convenience; the data itself has **no predefined model-training or held-out test split**. ```python from datasets import load_dataset ds = load_dataset("YOUR_USERNAME/YOUR_DATASET_REPO", split="train") print(ds.num_rows, ds.column_names) print(ds[0]) ``` For a downloaded local CSV: ```python import pandas as pd df = pd.read_csv("HyderabadUniversity_HVAC_Dataset.csv", parse_dates=["timestamp"]) fuzzy_inputs = [ "indoor_temperature_c", "indoor_relative_humidity_pct", "occupancy_pct", "co2_ppm", "outdoor_temperature_c", "time_of_day_hour", ] X = df[fuzzy_inputs] print(X.head()) ``` If training a learned comparison model, split by **time** rather than randomly shuffling adjacent observations. A fuzzy controller does not require training on the reference cooling command; it can be defined from membership functions and IF–THEN rules. ## Suggested evaluation measures | Measure | Suggested calculation | Interpretation | |---|---|---| | Estimated energy | Sum interval kWh after recomputing power from each controller's cooling command | Estimated energy use | | Temperature deviation | Mean absolute \(T_{\mathrm{indoor}}-T_{\mathrm{setpoint}}\) along each controller's recorded or projected trajectory | Closeness to reference target | | Comfort violations | Fraction of occupied intervals outside a stated comfort band; also report all-interval results if used | Comfort frequency | | HVAC response | Cooling level for specific input cases and time-series transitions | Control behavior | | Control variation | Mean absolute change in successive cooling commands; optionally count large jumps | Stability proxy | | Rule coverage | Fraction of test cases for which at least one fuzzy rule fires | Controller completeness | The existing outcome columns apply to the **reference trajectory only**. Replacing `reference_hvac_cooling_level_pct` with a new controller's output while retaining `indoor_temperature_next_c` or the recorded energy column would give an invalid comparison. Project both policies from comparable initial conditions using the same recorded disturbance sequence, then compute their metrics separately. ## Scope and limitations - The 70-day collection represents one zone and one warm-season period. It cannot establish year-round energy savings, multi-zone performance, or transfer to another site. - The reference controller and derived response calculations contain simplifying assumptions. The 8 kW figure is an analysis parameter, not documented equipment capacity. - `co2_ppm` reaches about 1168 ppm in this file. Deliberately create additional cases if you need to test a fuzzy rule for CO₂ near 1700–2000 ppm. - A cooling-level output alone does not specify fresh-air ventilation. Do not interpret a high cooling command as proof that high CO₂ has been corrected. - `comfort_violation` uses a fixed 22–26°C band, even when the zone is unoccupied. State whether your analysis evaluates all intervals or occupied intervals only. - `time_of_day_hour` is a linear clock representation, although time is cyclic. Consider cyclic features or careful fuzzy sets around midnight for models that use time directly. - Adjacent rows are strongly related, so random row splits can overstate predictive performance. - This dataset is suited to controller prototyping, software demonstrations, and classroom comparisons. It cannot establish operating savings in a real building. ## Responsible use and attribution This repository releases the data under the **Creative Commons Attribution 4.0 International** license. When reusing it, cite the dataset repository URL and the revision used, and preserve the data-provenance statement above. No human-subject records or identifying personal information are included. The dataset and its documentation were prepared with AI assistance and human review for an educational fuzzy-HVAC project. Users should independently check assumptions, code, units, and evaluation procedures before relying on results.