Lessons Learnt from Substation Inspection on Low Temperature District Heating Networks

District heating networks are considered to be a key element for the decarbonization of Europe. The RELaTED project seeks to contribute to the decarbonization of these infrastructures with the demonstration of low temperature district heating networks. One of the demonstration sites consists of more than 50 substations within a subsection of a larger network in the city of Tartu (Estonia), where the temperature was lowered by 10 ◦C. To ensure the benefits of this new generation district heating network and the fulfillment of comfort requirements, data have been monitored and analyzed at the substation level in an automatic way to facilitate the inspection of every user.This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 768567. This publication only reflects the authors’ views, and neither the Agency nor the Commission are responsible for any use that may be made of the information contained herein

Data-driven assessment for the supervision of District Heating Networks

There is an ongoing trend towards temperature reduction in District Heating Networks, allowing for the reduction of distribution heat loss and enabling the integration of low exergy heat production systems. There is a clear scientific consensus on the improved sustainability of such systems. However, there is not sufficient knowledge on how to deliver a successful transition to a low temperature District Heating system, while ensuring the operational levels of the existing system. This paper presents the experience on the progressive temperature reduction of a district heating subnetwork over the 2018–2021 period in Tartu, Estonia. Data from heat meters is extensively used to assess the capacity of substations and network branches to deliver the required heat and quality levels. Faulty substations are identified for targeted assessment and improvement works. Several substations have been identified as missing some of the performance criteria. This has led to further analysis, closer supervision and interventions in the operational conditions of the network. This is an ongoing process, expected to remain in the established procedures of the DH network operator. At the end of the process, a temperature reduction of 7 ºC has shown an improvement of 4.8% in network heat loss.This study has been carried out in the context of RELaTED project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 768567

Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters

An accurate characterization and prediction of heat loads in buildings connected to a District Heating (DH) network is crucial for the effective operation of these systems. The high variability of the heat production process of DH networks with low supply temperatures and derived from the incorporation of different heat sources increases the need for heat demand prediction models. This paper presents a novel data-driven model for the characterization and prediction of heating demand in buildings connected to a DH network. This model is built on the so-called Q-algorithm and fed with real data from 42 smart energy meters located in 42 buildings connected to the DH in Tartu (Estonia). These meters deliver heat consumption data with a 1-h frequency. Heat load profiles are analysed, and a model based on supervised clustering methods in combination with multiple variable regression is proposed. The model makes use of four climatic variables, including outdoor ambient temperature, global solar radiation and wind speed and direction, combined with time factors and data from smart meters. The model is designed for deployment over large sets of the building stock, and thus aims to forecast heat load regardless of the construction characteristics or final use of the building. The low computational cost required by this algorithm enables its integration into machines with no special requirements due to the equations governing the model. The data-driven model is evaluated both statistically and from an engineering or energetic point of view. R2 values from 0.70 to 0.99 are obtained for daily data resolution and R2 values up to 0.95 for hourly data resolution. Hourly results are very promising for more than 90% of the buildings under study.European Commission, RELaTED: h2020, GA nº 76856

Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters

[EN] An accurate characterization and prediction of heat loads in buildings connected to a District Heating (DH) network is crucial for the effective operation of these systems. The high variability of the heat production process of DH networks with low supply temperatures and derived from the incorporation of different heat sources increases the need for heat demand prediction models. This paper presents a novel data-driven model for the characterization and prediction of heating demand in buildings connected to a DH network. This model is built on the so-called Q-algorithm and fed with real data from 42 smart energy meters located in 42 buildings connected to the DH in Tartu (Estonia). These meters deliver heat consumption data with a 1-h frequency. Heat load profiles are analysed, and a model based on supervised clustering methods in combination with multiple variable regression is proposed. The model makes use of four climatic variables, including outdoor ambient temperature, global solar radiation and wind speed and direction, combined with time factors and data from smart meters. The model is designed for deployment over large sets of the building stock, and thus aims to forecast heat load regardless of the construction characteristics or final use of the building. The low computational cost required by this algorithm enables its integration into machines with no special requirements due to the equations governing the model. The data-driven model is evaluated both statistically and from an engineering or energetic point of view. R-2 values from 0.70 to 0.99 are obtained for daily data resolution and R-2 values up to 0.95 for hourly data resolution. Hourly results are very promising for more than 90% of the buildings under study. (This study has been carried out in the context of RELaTED project. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 768567. This publication reflects only the authors' views and neither the Agency nor the Commission are responsible for any use that may be made of the information contained therein