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Smart buildings — BMS & energy

The problem. A commercial building's management system runs hundreds of points — zone temperatures, CO₂, air-handler status, sub-metered energy. Facilities teams want two things at once: a live comfort view that flags a stuffy or overheating zone before the complaints come in, and an energy picture that shows where the kilowatt- hours actually go so they can cut waste.

This scenario pairs live comfort monitoring with energy aggregation on the same building model.

Set up demo data​

New workspace? Run this once (Python) to create a zone's CO₂ series, create the subscription before we listen (with a stuffy spell), and an energy series for the roll-up. Safe to re-run.

import intellistream_datahub_sdk, numpy as np, pandas as pd

client = intellistream_datahub_sdk.DataHubClient.from_env()

client.timeseries.create([intellistream_datahub_sdk.TimeSeries(external_id="zone_l8_co2_ppm", name="L8 open-plan CO2", unit="ppm", value_type="float")])
client.subscriptions.create([intellistream_datahub_sdk.Subscription(
    external_id="tower_a_comfort", name="Tower A comfort", timeseries=["zone_l8_co2_ppm"])])
idx = pd.date_range(end=pd.Timestamp.now(tz="UTC"), periods=60, freq="1min")
co2 = np.full(60, 600.0); co2[-10:] = 1180.0          # out of comfort band
client.timeseries.insert_from_lists(timestamps=idx, values=co2, ts="zone_l8_co2_ppm")

client.timeseries.create([intellistream_datahub_sdk.TimeSeries(external_id="tower_a_l8_energy_kwh", name="Tower A L8 energy", unit="kwh", value_type="float")])
idx2 = pd.date_range(end=pd.Timestamp.now(tz="UTC"), periods=30, freq="1d")
client.timeseries.insert_from_lists(timestamps=idx2, values=np.random.uniform(200, 400, 30), ts="tower_a_l8_energy_kwh")

1. Watch comfort live​

Subscribe to the zone comfort series and react as readings land — raise a comfort_alert when a zone drifts out of band. See Consume live data.

import ai.intellistream.datahub.sdk.subscriptions.SubscriptionMessage;

try (var stream = client.subscriptions().listen(List.of("tower_a_comfort"))
        .stream((SubscriptionMessage msg) -> {   // auto-acks after each message
            if (outsideComfort(msg.payload())) {       // temp or CO2 out of band
                EventModel alert = new EventModel();
                alert.setExternalId("comfort_alert_l8_" + System.currentTimeMillis());
                alert.setType("comfort_alert");
                alert.setStatus("open");
                alert.setMetadata(Map.of("zone", "zone_l8_open_plan", "co2_ppm", "1180"));
                alert.setEventTime(ZonedDateTime.now());
                client.events().create(List.of(alert));
            }
        })) {
    awaitShutdown();   // your app lifecycle; closing the stream ends delivery
}

2. See where the energy goes​

Sub-meters report per floor and system. Roll consumption up to daily totals per meter to rank the biggest users and spot overnight waste. See Query & aggregate.

var filter = new RetrieveFilter();
filter.setExternalId("tower_a_l8_energy_kwh");
filter.setStart(ZonedDateTime.now().minusDays(30));
filter.setEnd(ZonedDateTime.now());
filter.setAggregates(List.of("sum"));
filter.setGranularity("1d");

var request = new DataRetriever<RetrieveFilter>();
request.setItems(List.of(filter));

client.timeseries().retrieve(request).getItems().get(0).getDatapoints()
    .forEach(p -> chartDailyKwh(p.getTimestamp(), p.getValue()));
Correlate comfort and equipment

When several zones go uncomfortable together, walk the graph from each to the air handler they share — the same alarm correlation trick finds the one unit behind the complaints.

See the result​

The stuffy zone trips the loop before the complaints come in:

comfort_alert_l8_…  → open   (CO₂ 1180 ppm, above the comfort band)

See also​