Energy Management Solutions

AFDD Analytics

AFDD fault detector uses proactive diagnostic rules/processes/algorithms to constantly monitor any faults or deviations to report, maintain optimal performance.

Along with a standard set of rules, users can create custom rules to detect any anomalies that affect system performance or SOPs, or business policies.

Machine learning is employed to predict anomalies and to simulate values on the failure of any inputs. Inputs thus simulated shall ensure no downtime in the absence of the failed inputs.

MBCx (Monitoring based commissioning)

As with every system, performance deteriorates over time and general practice is to periodically tune and reset the system to attain desired performance.

More like many systems becoming online, our MBCx monitors real-time to re-tune the system based on vital parameters. Anomalies are reported for proactive decision-making.

M&V (Measurement & Verification)

Measurement & Verification is a stream to measure /collect, analyse, and report energy saving. This is automated in M&V in compliant with popular standards.

Simulated Baseline results or calculated, measured, or collected results can be ingested adjusted, and compared against real-time data feeds from ear-marked energy consumers.

Machine learning is used for forecasting based on real-time and historical data.

Reports can be designed to update stakeholders on results achieved.

Energy Modeling and Simulations

Energy simulation results form a vital part of a digital twin which can be compared against real-time data.

Our baseline and real-time data are seamlessly integrated to perform different comparisons to initiate various actions to react or proactively correct energy consumption overshoots.

Parametric Analysis allows simulated runs of various adjustments, positive results of which can be used to adjust real-time systems.