Increasing a Block’s ΔT by Approximately 20 °C
Excellent Stakeholder Collaboration
The Value of Remote Acceptance Testing
Background
FairHeat was engaged by a private developer for Quality Assurance on a residential development in North Acton. The development consists of 376 dwellings across three blocks. The heating and hot water systems are supplied by a low-temperature hot water (LTHW) heat network connected to twin-plate Heat Interface Units (HIUs) in each dwelling. Space heating is delivered via underfloor heating designed for a flow temperature of 40- °C and a return of 30 °C.
Primary heat generation for the development is provided by air-source heat pumps (ASHPs) located in a rooftop plant room, supplemented by gas boilers in the basement plant room. Performance monitoring for the energy centre, block, and dwelling heat meters is facilitated by Guru Pinpoint, enabling data-driven insights into system performance. Guru Pinpoint is a web-based software that displays heat network performance data through easy-to-understand charts and dashboards.
FairHeat was engaged to conduct acceptance testing across all dwellings, comprising 76 on-site tests and 300 tests carried out remotely using Guru Pinpoint.
Challenge
The purpose of the acceptance testing was to ensure system performance met design specifications, guaranteeing both resident comfort and network efficiency. The following key issues were identified during Remote Acceptance Testing (RAT):
- Low Delta T (ΔT): One block was operating with a ΔT of 5 °C compared to the design ΔT of 30 °C, significantly impacting network efficiency.
- Heating Instability: HIU demand in one dwelling cycled on and off continuously, leading to reduced comfort and higher energy bills.
- High Return Temperatures: Some dwellings displayed elevated return temperatures during standby and DHW operation, reducing overall network efficiency.
- Incomplete Data: Certain dwellings lacked discernible DHW or space heating events in Guru Pinpoint data, complicating performance analysis.
FairHeat Solution
To address these issues, FairHeat’s Quality Assurance team implemented the following solutions:
Flushing Bypass Identification and Resolution:
Guru Pinpoint analysis revealed one block was operating with a ΔT of 5 °C instead of the design ΔT of 30 °C, indicating an open bypass on the system.
The location of the bypass was determined using Guru Pinpoint, finding a dwelling where the HIU had a ΔT of c. 20 °C during standby operation compared to a typical ΔT of c. 8 °C, and a flow rate of 0 m3/h. This indicated that the flushing bypass above the HIU was open, meaning that the HIU return temperature sensor was close to ambient temperature due to no flow through the HIU, while the HIU flow temperature sensor was still reading a high temperature due to conduction through the pipework. This was leading to high flow rates and return temperatures to the Energy Centre, adversely impacting network efficiency.
This was flagged to the contractor, and the site team were able to promptly arrange access to the dwelling and close the bypass above the HIU, restoring network performance without extensive diagnostic efforts. The impact of closing this flushing bypass on the dwelling and block heat meters respectively is shown in Figure 1 and Figure 2 below.
Faulty Component Replacement:
HIU demand cycling on and off in one dwelling was traced to a faulty DHW control valve, as can be seen in figure 3. This was replaced promptly, stabilising the heating system and improving resident comfort.
Recommissioning for High Return Temperatures:
Dwellings with elevated return temperatures were identified, and recommissioning was coordinated with the contractor to optimise HIU performance. Retesting confirmed satisfactory performance.
Data Event Coordination:
For dwellings without DHW or space heating events in Guru Pinpoint, FairHeat collaborated with the contractor to coordinate the creation of these events to test performance remotely.
Results
FairHeat’s approach exemplifies the value of innovative Quality Assurance practices, combining advanced data analysis with collaborative problem-solving to optimise heat network performance and enhance end-user satisfaction.
FairHeat’s Remote Acceptance Testing resulted in significant improvements, including:
- Enhanced Delta T (ΔT): One block’s ΔT increased from 5 °C to 26 °C.
- Flow Rate Reduction: One block’s bypass flow rate decreased from 1.2 m³/h to 0.025 m³/h.
- Heat Loss Reduction: One Block’s heat losses dropped from 90 W/dwelling to 76 W/dwelling.
- Resident Comfort: Improved HIU performance and reduced heat losses led to lower bills and enhanced comfort.
- Efficiency Demonstration: The effectiveness of Remote Acceptance Testing was proven, showcasing its potential for diagnosing on-site issues without physical access.
Highlights
Through Guru Pinpoint’s advanced data analysis, FairHeat identified an open bypass in one dwelling that caused the block’s ΔT to drop to 5 °C, far below the design target of 30 °C. By collaborating with the site team, the bypass was closed, leading to an increase in ΔT to 26 °C. This intervention significantly improved the heat network’s efficiency, reduced energy wastage, and demonstrated the critical value of precise data monitoring.
FairHeat maintained an open and effective partnership with the contractor, ensuring seamless communication and quick resolution of identified issues. For instance, troubleshooting and replacing a faulty DHW control valve resolved heating instability in a specific dwelling, showcasing the team’s ability to efficiently address both network-wide and unit-specific challenges.
Remote Acceptance Testing is currently limited to heat meter data, so common issues such as long DHW delivery times and low radiator/UFH flow temperatures can only be identified by on-site acceptance testing. According to Heat Networks: Code of Practice for the UK (CP1 2020), best practice is to carry out on-site acceptance testing on 100% of dwellings.
However, where on-site testing in 100% of dwellings is not feasible, Remote Acceptance Testing is a valid alternative which can identify key inefficiencies, such as high return temperatures and open bypasses, without requiring physical site access. This approach not only saves time and resources but also underscores its potential to enhance operational workflows and maintain high standards of quality assurance across developments.
