Expense Analyser

An international company operating in the PropTech sector, delivering solutions that support efficient building management, cost optimisation, and tenant safety.

By integrating energy-efficient technologies into its services, the company also contributes to reducing environmental impact across commercial and industrial facilities.

Energy consumption data was available but difficult to translate into actionable decisions.

Increasing pressure related to climate change and energy efficiency made it critical to better understand how energy and water are used across buildings and installations.

Although data from meters was available, it was fragmented and difficult to interpret. This limited the ability to identify inefficiencies, prioritize optimization efforts, and make informed decisions about modernization or maintenance.

As a result, organizations lacked clear visibility into where energy was being lost and how to effectively reduce operational costs.

To improve energy efficiency and reduce costs, the company needed a solution that could:

• collect and process data directly from utility meters
• operate independently across facilities with different technical setups
• translate raw data into clear insights and optimisation opportunities
• support both local analysis and cross-facility comparisons

The solution had to work in real-world environments, without relying on complex infrastructure or centralized systems.

An autonomous monitoring device and data analytics platform were developed to collect, process, and analyze energy and water consumption data across managed facilities. The system combines local data acquisition with analytics capabilities to help operators identify inefficiencies and make informed resource management decisions.

The system collects, records, and processes data locally from machines, installations, and switching stations. It operates independently, enabling data acquisition, storage, and analysis without relying on constant external connectivity.

Users access dashboards that highlight areas of energy loss and indicate where optimisation efforts can deliver the greatest impact.

In addition, a higher-level analytics layer compares data across multiple facilities, supporting investment prioritization and enabling predictive insights based on historical performance.

By transforming utility consumption data into dashboards, performance comparisons, and actionable insights, the platform also demonstrates practical applications of business intelligence. These capabilities help organizations evaluate resource usage, identify cost-saving opportunities, and prioritize investments based on operational data.

The solution combines embedded systems, data processing, and analytics technologies:

• C++ for low-level data acquisition from devices
• .NET for backend services and web interface
• Python for data processing and machine learning models
• InfluxDB for time-series data storage
• Kapacitor for real-time data processing and alerts
• Grafana for data visualisation and dashboards
• 3D printing for custom hardware enclosure design

The device gathers real-time data from utility meters and processes it locally using embedded software.

Collected data is stored and analysed using time-series databases and analytics tools, allowing for continuous monitoring of consumption patterns.

The system identifies deviations, inefficiencies, and trends, presenting them through visual dashboards that support operational decision-making.

Cross-site analytics enable benchmarking and comparison between facilities, helping identify underperforming assets and optimisation opportunities.

The system provides continuous visibility into energy and water consumption, enabling early identification of inefficiencies and abnormal usage patterns.

By analysing data locally and in real time, it allows teams to react quickly and address issues before they escalate into higher costs or equipment problems.

The ability to compare data across facilities improves decision-making and supports more effective resource management.

The solution delivered measurable improvements across key areas:

• Reduced energy and water consumption costs, by identifying inefficiencies
• Improved visibility into resource usage, enabling better control
• More effective prioritisation of optimisation investments
• Early detection of anomalies, supporting preventive maintenance
• Data-driven decision-making, replacing assumptions with measurable insights

Can an energy monitoring device in commercial buildings work without constant internet connectivity?

Yes. An autonomous device can acquire, store and analyze meter data locally, so it does not depend on constant external connectivity. In this project, InTechHouse built a device that collects data from machines, installations and switching stations, processes it with embedded C++ software, and operates across facilities with different technical setups without complex infrastructure or centralized systems.

How can energy and water consumption be compared across multiple PropTech facilities?

With a higher-level analytics layer that compares data from each site. In this project, InTechHouse stored meter readings in InfluxDB, used Kapacitor for real-time processing and alerts, and presented results in Grafana dashboards. Cross-site analytics help identify underperforming assets, prioritize optimization investments and provide predictive insights based on historical performance, with Python used for data processing and machine learning models.

Can meter data analytics detect abnormal energy use before it causes equipment problems?

Yes, through continuous monitoring of consumption patterns with real-time alerts. In this project, InTechHouse designed the Expense Analyser to identify deviations, inefficiencies and trends in energy and water use, so building teams can react quickly before issues escalate into higher costs or equipment problems. Early detection of anomalies also supports preventive maintenance in commercial and industrial facilities.