•China faces challenges to supply clean building heating services to its citizens.•All building heating solutions have a great potential.•The choice of building heating solutions in China is affected ...by spatial parameters.•It is essential to use spatial analysis to find proper building heating solutions.
With continuing of urbanization, improving of life quality as well as combating against air pollution, China is facing comprehensive challenges to supply modern clean heating to a majority of its citizens. For space heating solutions, currently in urban areas of north China, coal based district heating is prevalent. In urban areas of south China, distributed heating solutions are used. In rural areas, de-centralized coal stoves and biomass stoves are still commonly used. As renewable building heating solution, ground source heat pumps are installed for large scale applications. Building floor areas heated by ground source heat pumps increased tremendously during past ten years. Air source heat pump is being promoted in north Chinese rural areas as part of coal to clean heating project. Solar water heater and electric water heater for domestic hot water supply is widely used in north China and gas water boiler is widely used in south China. A series of policies have encouraged clean fossil fuel district heating in north China. National development plans are also supporting and subsidizing renewable heating technology such as heat pumps. Different building heating technologies have their own advantages and disadvantages from techno-economic and environmental perspectives. The choice of building heating solutions for different geolocations of China is strongly affected by spatial parameters such as local climate condition, population distribution, natural resource availability etc. Therefore, a spatial data analysis method is essential to help stakeholders decide proper building heating solutions in different parts of China by key performance indicators reflecting lower primary energy use, economic affordability and lower environmental impact.
Accurate load forecasting of district heating systems (DHSs) is an essential guide to guaranteeing effective energy production, distribution, and rational utilization. Artificial neural networks have ...been extensively applied to heating energy prediction in DHS. Recently, a new time series prediction model namely Informer was proposed. This study proposes an Informer-based framework for DHS heating load forecasting. To explore the performance of Informer in heating load forecasting tasks, four forecasting models namely Autoregressive Integrated Moving Average model, Multilayer Perceptron, Recurrent Neural Network and Long Short-Term Memory network are established for comparison. The historical heating load, outdoor temperature, relative humidity, wind speed and air quality index of a DHS in Tianjin are used as the input characteristics to comprehensively assess the performance of these five forecasting strategies. The prediction results of the models are evaluated and visualized. The experimental results show that the Informer-based forecasting model can achieve the most accurate and stable predictions. Furthermore, a relative position encoding algorithm is introduced to enhance its generalization and robustness. Overall, the Informer-based framework can report satisfactory testing results. The prediction curve is fitted to the trend of temperature change which can play an excellent guiding role in heating dispatching.
•A new framework based on Informer is proposed for heating load forecasting of a DHS in Tianjin, China.•Informer is compared with other four popular prediction models namely ARIMA, MLP, RNN and LSTM.•The performance of Informer in heating load forecasting has been verified.•A relative position coding is introduced to improve the prediction ability of Informer.
District heating systems are appropriate infrastructures to supply space heating and domestic hot water from a number of potential sources in future energy systems based on renewable energy. On the ...other hand, district heating is also facing competition from heating technologies in individual buildings. Lowering the district heating temperatures will improve efficiency and competitiveness of district heating; however, the production of domestic hot water is an impediment to this. This article uses energyPRO to analyse three low-temperature district heating schemes. District heating is supplied from a heat pump using air, seawater or groundwater as heat sources. Domestic hot water is supplied by booster heat pumps using district heating as heat source or through a heat exchanger. The economic benefit of adding booster heat pumps to cover the domestic hot water demands is assessed by modelling the time-varying operation against the electricity market as well as the time-varying coefficient of performance of heat pumps and grid losses. Based on this operation cost reduction assessment, the energy system value of booster heat pumps ranges from 2500 to 6800€ per house depending on the choice of low-temperature heat source for the district heating heat pump as well as economic time-horizon and discount rate.
•Energy systems analyses of heat pump-based district heating systems.•Analysing air, ground and seawater as heat source for district heating heat pumps.•Assessment of the value of booster heat pumps for domestic hot water preparation.
In order to reach targeted 4th generation district heating temperatures around 55 °C supply and 25 °C return, it is necessary to ensure that heating installations inside buildings are designed and ...operated properly. In this study we investigated the best-case of current design and operation of building installations with the aim of identifying whether there is a gap between current best-case examples and future temperature targets. The study included 7 single-family dwellings and 3 apartment buildings, that were selected based on their low district heating return temperature. Data from the building substations showed that single-family dwellings obtained return temperatures in the range from 25 to 30 °C while the apartment buildings had return temperatures in the range of 30–40 °C. This indicates that there is a gap between the best functioning heating installations in apartment buildings today, and the targeted district heating return temperatures of 25–30 °C in future 4th generation district heating networks. District heating return temperatures in the range of 30–40 °C could however be the initial ambition for the existing buildings all around Europe that are expected to be connected to new district heating systems in the near future.
•Single-family dwellings can obtain return temperatures in the range 25-30 °C.•In these, short service pipes and no circulation of DHW were typical.•Well-functioning apartment buildings had return temperatures in the range 30-40 °C.•Radiator systems were equipped with balancing valves, pre-settings, and thermostats.•There is a gap between current standards and future 4GDH requirements.
Denmark is aiming for a fossil-free heating sector for buildings by 2035. Judging by the national heating plan, this will be achieved mainly by a further spread of DH (district heating) based on the ...renewable heat sources. To make the most cost-effective use of these sources, the DH supply temperature should be as low as possible. We used IDA–ICE software to simulate a typical Danish single-family house from the 1970s connected to DH at three different stages of envelope and space heating system refurbishment. We wanted to investigate how low the DH supply temperature can be without reducing the current high level of thermal comfort for occupants or the good efficiency of the DH network. Our results show that, for a typical single-family house from the 1970s, even a small refurbishment measure such as replacing the windows allows the reduction of the maximum DH supply temperature from 78 to 67 °C and, for 98% of the year, to below 60 °C. However for the temperatures below 60 °C a low-temperature DH substation is required for DHW (domestic hot water) heating. This research shows that renewable sources of heat can be integrated into the DH system without problems and contribute to the fossil-free heating sector already today.
•Denmark is aiming for a 100% fossil-free heating sector for buildings by 2035.•District heating with a supply temperature of 55–50 °C is central to the solution.•We model a typical 70s house to find the lowest possible supply temperature.•The supply temperature should not be >55 °C for more than 2% of time over a year.•Existing buildings can be integrated into low-temperature district heating.
Combined harnessing of electrical and thermal energies could leverage their complementary nature, inspiring the integration of power grids and centralized heating systems in future smart cities. This ...paper considers interconnected power distribution network (PDN) and district heating network (DHN) infrastructures through combined heat and power units and heat pumps. In the envisioned market framework, the DHN operator solves an optimal thermal flow problem given the nodal electricity prices and determines the best heat production strategy. Variate coefficients of performance of heat pumps with respect to different load levels are considered and modeled in a disciplined convex optimization format. A two-step hydraulic-thermal decomposition method is suggested to approximately solve the optimal thermal flow problem via a second-order cone program. Simultaneously, the PDN operator clears the distribution power market via an optimal power flow problem given the demands from the DHN. Electricity prices are revealed by dual variables at the optimal solution. The whole problem gives rise to a Nash-type game between the two systems. A best-response decentralized algorithm is proposed to identify the optimal operation schedule of the coupled infrastructure, which interprets a market equilibrium as neither system has an incentive to alter their strategies. Numeric results demonstrate the potential benefits of the proposed framework in terms of reducing wind curtailment and system operation cost.
An accurate heating load prediction algorithm can play an important role in smart district heating systems (SDHS), which is helpful for realizing on-demand heating and fine control. However, most of ...the traditional heating load prediction algorithms neglect the indoor temperature feedback from the household and cannot form closed-loop control. This paper designs an intelligent sensor based on the Narrow band Internet of Thing (NB-IoT) to collect the indoor temperature of a typical household and proposes an algorithm based on attention long short term memory (ALSTM) to predict the heating load for an integrated "heat exchange station - heat user". The attention mechanism is designed to obtain more accurate nonlinear prediction models between the heating load and influencing factors, such as indoor temperature, outdoor temperature, and historical heat consumption. A performance comparison with other state-of-the-art algorithms shows that the proposed ALSTM algorithm has the best performance, achieving an accuracy of 97.9%. Besides, a Kalman filter is introduced to identify and remove outliers while reducing the random error of the measurement.
•The NB-IoT sensors are designed to acquire the indoor temperatures of users.•The ALSTM-based prediction model has excellent nonlinear representation ability.•The attention mechanism makes the ALSTM automatically focus on the core factors.•The Kalman filter is integrated to detect the abnormal value in the original data.
Abstract
The text in subsection 4.3.2 of the published article contains confusing text and some algebraic errors in equations. The corrected text is listed below; it should replace the subsection.
To reach the goal of decarbonizing energy systems, newly constructed buildings must adhere to higher efficiency standards. In new residential developments, this results in a higher share and ...significance of the energy demand for domestic hot water preparation. In order to generate realistic load profiles for domestic hot water preparation, which can help with dimensioning district heating systems, four substation types were modeled in TRNSYS. Two instantaneous and two storage systems were considered for single family and multi family houses. 11 fictitious buildings were defined, for which the systems were dimensioned. Yearly simulations were conducted, using DHWcalc draw-off profiles as input. The impact of domestic hot water preparation system design on energy balances, return temperatures and simultaneity factors was investigated. Distribution heat losses amount to approximately the same energy demand as the useful energy demand for domestic hot water. In single family houses, storage heat losses also account for a significant energy demand, but in larger multi family houses the storage heat losses are negligible. The yearly weighted average return temperatures of the investigated buildings vary from 25 to 54 °C. Instantaneous DHW preparation result in 8–9 K lower return temperatures for the district heating system than storage systems. An important influencing factor on the return temperature is the relation of energy used by the circulation system to the useful energy demand, resulting in lower return temperatures in more densely inhabited buildings. Load duration curves for superposed profiles were calculated and compared to literature values. The results show, that DHWcalc draw-off profiles provide a suitable basis with realistic simultaneity for district heating load profiles. It is shown, that the time interval for which simultaneity factors are calculated must always be considered. Also, the importance of the underlying probability distributions for draw-off profiles was shown by comparing different approaches.
•Dynamic simulations of different DHW preparation systems in TRNSYS.•Generation of DH load profiles for DHW for instantaneous and storage systems.•Instantaneous DHW systems yield lower return temperatures than storage systems.•Simultaneity factors were calculated for different systems and time scales.•Time scale and probability distributions for DHW draw-offs influence peak loads.
