Cut energy costs across every site you run.

Demand charges are set by the single highest interval in the billing period, so the saving comes from acting before the peak is locked in, not from reporting it afterwards. ControlCom Connect meters demand continuously through the on-site Edge Server, and an alarm on a demand variable notifies the people who can shed load by SMS, email or push notification while the interval is still open. Operators can then act from the same screen: commands write a new demand limit to the equipment, and clear a stored peak at the end of a billing period. The Energy Management reports close the loop by showing the peak demand actually recorded per utility company and billing period, so the team can see whether the discipline is holding month over month. Facilities that manage load this way have cut peak demand charges by up to 20 percent.

No. The Edge Server speaks Modbus TCP and RTU, OPC-UA, BACnet, EtherNet/IP and MQTT, so existing panel meters, power monitors, BMS controllers and PLCs report as they are, and adding a metered point is a client configuration rather than an electrical project. That is why a site can be metering into the platform in days: the demand and consumption values are already on the wire, and the work is mapping registers to named variables rather than pulling cable. Where a facility has no metering at all on a feeder it cares about, that gap has to be closed with hardware first. But everything already instrumented, including the BMS the buildings run on, stays exactly where it is and keeps its job while the platform reads alongside it. The result is that the metering project starts with configuration on day one, and hardware only where the portfolio genuinely has none.

Yes. Every site reports into one organization, so dashboards, alarms and reports cover the whole portfolio rather than one building at a time. The Map plots each location colored by its live alarm state, which makes the morning check a glance instead of a login per site. For comparison work, the Data Explorer plots the same measurement across sites on one chart, raw or aggregated per time bucket, with minimum, maximum and average available for each series and a CSV export of exactly what the chart shows. That is what turns multi-site metering into decisions: the building that drifts overnight, the feeder whose load never drops on weekends, and the site whose profile changed after a controls modification all show up as diverging lines rather than anecdotes, whether the portfolio is two facilities or fifty.

Yes. The Energy Management tools hold your utility accounts, their billing periods and the bills you upload against them, and report peak demand, energy use and load factor per utility company, so each invoice is checked against your own metered record rather than taken on trust. Billing errors and estimated reads surface as a mismatch between the bill and the platform's numbers for the same period. Load factor, the ratio of average demand to peak demand across the billing period, is the most useful screening number the reports produce: a site with a low load factor is paying demand charges out of proportion to the energy it actually uses, which points at short, sharp peaks that scheduling or equipment sequencing could flatten. Reviewing that one number per site, every billing period, is how a portfolio decides where the next demand-management effort goes.

The Edge Server at the site keeps collecting: it buffers readings locally through the outage and syncs the history when the connection returns, so the metered record stays complete. For energy work specifically, completeness is the point. Bill verification only stands up if the platform's record covers the same billing period as the invoice with no holes in it, and a load factor computed over a gappy month is a guess rather than evidence. Local buffering is what protects that, and it runs on the same gateway doing the metering, so there is no separate data logger to maintain. Remote dashboards do go dark for the affected site until the link returns, but nothing is lost, and the demand story for the outage window backfills as soon as the site reconnects.

Yes, from the same stored readings. An asset data report trends the variables of one asset over a chosen period, with aggregation buckets from 10 minutes to 1 day, grouping by unit or classification, optional weather correlation for the asset's location, and a Data Insights section that flags variability, outliers, trends and data quality concerns. Export to CSV downloads the raw readings flat or pivoted, and Save to PDF produces the document version of the same study. Schedule delivery carries the configuration into a recurring job that emails the PDF hourly, daily, weekly or monthly to any recipient list, with a run history kept per job, and a weekly digest recaps the organization every Monday. Sustainability reporting draws on the same record: consumption and efficiency metrics come from the stored series, over whatever period the reporting framework asks for.