Efficient Wireless Temperature Monitoring

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Energy efficiency involves not just consuming less energy but also obtaining the same results with reduced consumption. While saving heat energy matters, it’s crucial to consider indoor climate quality. Regrettably, this is frequently achieved by increasing energy consumption.

Addressing both needs is a significant challenge in Latvia. To enhance energy efficiency and indoor air quality, the Jelgava City Council actively contributes to developing a smart urban environment.

SIA Optiwise, an energy management company, collaborated with Jelgava City Council to pilot an advanced heating control optimization system in a school. This system relies on indoor climate feedback through Aranet IoT solutions.

Object:

Name of the object: Jelgava Secondary school No.6
Area of the building: 9 334 m2
Building Plan: 3 stories and a basement

The building is insulated: all of the windows are replaced with new, plastic ones. The building has a one-pipe heating system with no local room regulation options. Natural ventilation.

Average specific heat energy consumption: 59 kWh/m2

Challenge:

While the school building has insulation, it relies on an outdated one-pipe heating system without individual room control. Uneven temperatures persist, making cost-effective solutions elusive, leaving critical areas to dictate overall temperature regulation.

A third-party supplier manages heating controller parameters in the building but lacks initiative for improvement or energy conservation. Room occupants, thermal inertia, and external factors continuously influence conditions. However, the building manager’s ability to adapt to these changes is restricted.

In the realm of smart urban development, the Jelgava City Council is actively promoting energy-efficient building improvements while guaranteeing superior indoor air quality. Additionally, there are plans to implement indoor climate and air quality monitoring systems within school buildings.

Indoor air quality is a particularly important aspect in teaching institutions, as recent studies show that it has a direct impact on people’s cognitive abilities. Poor quality of air in spaces that lack proper ventilation and high concentration of carbon dioxide can potentially impair human cognitive abilities by up to 50%.

There are two main goals:

Find opportunities to reduce energy consumption using feedback from constantly changing indoor climate and external conditions
Maintain optimal room temperature while the building is being actively used

Solution:

The chosen solution is Leanheat’s automatic heating control system, which relies on continuous feedback between indoor conditions and heating unit parameters.

It employs artificial intelligence to predict, manage, and monitor the building’s heating system. This involves combining data from the heating unit and wireless temperature sensors, which is then processed by artificial intelligence.

This ensures optimal heating control, accounting for current weather conditions, upcoming forecasts, and the building’s usage schedule.

Setup and configuration were done within 2 business days. The system in
the building has been in operation since December 1, 2019.

We installed 65 Aranet wireless temperature and humidity sensors for ongoing monitoring in all active spaces. Additionally, we integrated a new heating controller, ECL 310, equipped with wireless GSM Internet connections, along with the sensor base station.

The main reasons for Optiwise choosing Aranet to implement this project were:

Extensive wireless network coverage – allowing the entire school building to be covered with only one base station
Simple setup – does not require additional resources and time
Easy maintenance – the only requirement is replacing the battery once every seven years
Aranet Cloud service – allowing the system to be easily integrated into Leanheat software

Results:

To evaluate improvements, initial reference points were set for thermal energy consumption and indoor temperature using a 3-year historical average heat consumption.

Climate normalization was employed to assess historical and future climate effects, determining annual energy consumption under consistent climate conditions. Temperature monitoring occurred throughout November, with 4 weeks of measurements during the heating season without the new control system to establish the average reference temperature.

Simultaneously, the system’s machine-learning algorithm adapted to the building’s unique conditions.

The new control system, launched on December 1st, aimed to stabilize room temperatures during active hours. After 3 months, data showed a 10.4% reduction in thermal energy consumption (30.7 MWh) compared to the reference. Considering the average outdoor temperature during this period, absolute energy savings amounted to 26.2 MWh. Notably, the school building’s active-use schedule extended from 7:00-14:00 to 7:00-17:30 starting from the 2nd week of January. Had the previous schedule remained, estimated savings would have been even greater.

During this period, the fundamental approach of utilizing room temperature for heating control achieved an average savings of 48.0% compared to the reference. This marks a substantial improvement over similar buildings using setpoint-based control, which led to compensating for internal heat gain loss.

It’s worth noting that data from the latter half of March and April was excluded from the comparison due to the emergency situation and quarantine, which created different conditions compared to previous years.

Conclusion:

Introducing the Leanheat system met the primary goal of reducing energy consumption and minimizing costs without requiring additional school investments. This optimization improved the existing heating system’s functions.

In addition, the new system ensures:

Energy and overall financial savings
Indoor climate monitoring system at the cost of energy saved during the process
Identification of strategic heating system upgrades needed to improve unevenness of the heat distribution systems (helping to determine the best strategy for heat balancer placement in the future)
Potential for CO2 monitoring in the future.