Implementation of low temperature district heating in existing buildings

Ashfaq, A. ORCID: 0000-0001-5152-5550, 2020. Implementation of low temperature district heating in existing buildings. PhD, Nottingham Trent University.

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District heating (DH) provides a gateway for the integration of low carbon technologies and renewable energy sources to achieve a sustainable carbon neural future. The low temperature district heating (LTDH), in particular, is the latest and most efficient technology which enables the possibility of combining multi-vector heat sources to the network such as, renewable energy sources, heat-pumps and waste heat from the industry. This thesis considers REMOURBAN project to investigate the implementation of low-temperature district heating in existing boiler based buildings in Nottingham. This LTDH (60/30) network intervention is first of it’s kind in the UK and utilises return pipe to heat 94 flats. The study is comprised in three main parts, i.e. thermal performance modelling of buildings, hydraulic modelling of the district heating network and predictive modelling of monitored data.

The results from the first part show that retrofitting increases the energy performance of buildings by almost 50%, and the relation between the building regulations and thermal performance analysis show that with current regulations in the UK, it is unlikely to achieve the target of net-zero emission buildings (NZEB) by the year 2050. The second part of the study investigates the design and operation of an energy efficient LTDH network (from REMOURBEN). The results from the hydraulic modelling suggest that the networks should be designed with variable speed pumping, and supply water temperature should be kept constant from the plant room. This leads to the lowest energy consumption in the network. It is concluded that the energy efficiency and t in REMOURBAN project can be improved by reducing flow-rates both in the network and circulation pump inside the plant room. Moreover, the techno-economic analysis for the de-carbonised district heating network shows that 100% decarbonisation depends on selling excess electricity and heat to the private consumers.

Finally, the predictive modelling suggests classical stochastic SARIMA method is good for short-horizon forecasts while modern machine learning (MLP and SVR neural networks) are best for medium and long-horizon forecasts. The GIS mapping shows that the decentralised LTDH network with multiple energy centres is the optimum strategy owing to the cost of network pipe-works and heat-losses in the network.

The overall conclusion of the study is that the implementation of low temperature district heating in existing building is possible and optimisation as well as control of flow-rates are the key factors in achieving energy efficiency in the network. The novelty of this study is that a live LTDH network intervention has been as a case study which provides a energy efficient solution for the UK. The learning from this study can be replicated to the future LTDH network project anywhere in the UK or elsewhere.

Item Type: Thesis
Description: Abridged version.
Creators: Ashfaq, A.
Date: May 2020
Divisions: Schools > School of Architecture, Design and the Built Environment
Record created by: Linda Sullivan
Date Added: 22 Feb 2021 11:02
Last Modified: 26 Jan 2023 03:00

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