The development of smart grids is expected to shift the role of buildings in power networks from passive consumers to active players that trade on power markets in real-time and participate in the operation of networks. Although there are several studies that report on consumer views on buildings with smart grid features, there is a gap in the literature about the views of the energy and buildings sectors, two important sectors for the development. This study fills this gap by presenting the views of key stakeholders from the Swedish energy and buildings sectors on the active building concept with the help of interviews and a web survey. The findings indicate that the active building concept is associated more with energy use flexibility than self-generation of electricity. The barriers to development were identified to be primarily financial due to the combination of the current low electricity prices and the high costs of technologies. Business models that reduce the financial burdens and risks related to investments can contribute to the development of smart grid technologies in buildings, which, according to the majority of respondents from the energy and buildings sectors, are to be financed by housing companies and building owners.
Buildings are expected to play a key role in the development and operation of future smart energy systems through real-time energy trade, energy demand flexibility, self-generation of electricity, and energy storage capabilities. Shifting the role of buildings from passive consumers to active players in the energy networks, however, may require closer cooperation between the energy and buildings sectors than there is today. Based on 23 semi-structured interviews and a web survey answered by key stakeholders, this study presents the views of the energy and buildings sectors on the current energy challenges in a comparative approach. Despite conflicting viewpoints on some of the issues, the energy and buildings sectors have similar perspectives on many of the current energy challenges. Reducing CO2 emissions is a shared concern between the energy and buildings sectors that can serve as a departure point for inter-sectoral cooperation for carbon-reducing developments, including the deployment of smart energy systems. The prominent energy challenges were identified to be related to low flexibilities in energy supply and use, which limit mutually beneficial cases, and hence cooperation, between the energy and buildings sectors today.
Mobilized thermal energy storage (M-TES) is a promising technology to transport heat without the lim-itation of pipelines, therefore suitable for collecting distributed renewable or recovered resources. In par-ticular, the M-TES can be flexibly used for the emergency heating in the COVID-19 era. Though the M-TES has been commercializing in China, there is not any specific regulation or standard for M-TES systems. Therefore, this paper summarizes and discusses the existing regulations and policies concerning M-TES in the aspects of facility manufacture and operation, road transportation, and financial support and guidance. Furthermore, the suggestions were presented including necessary consensus on the devel-opment of M-TES among different departments, consideration of local conditions when drafting or revis-ing regulations and policies, sufficient investment, or subsidy on the R&D of M-TES, and qualification recognition of M-TES companies and staffs.
Latent heat storage technology plays an important role in the effective utilization of clean energy such as solar energy in building heating, but the low thermal conductivity of heat storage medium (phase change material) affects its large-scale application. As a new heat storage enhancement technology, rotation mechanism has a good application prospect. In this paper, the solidification performance of a triplex-tube latent heat thermal energy storage unit at constant speed (0.5 rpm) is studied numerically. Different optimization design methods (Taguchi method and response surface method) are used for deep analysis. The influences of fin position, number, and material on solidification properties are explored by the Taguchi method. Then, the unit structure (fin angle, fin length, and fin width) is optimized by the response surface method. Compared with the original structure, the average heat release rate of 8 copper fins with all outer tubes is increased by 108.93%, and the solidification time is reduced by 52.06%. The optimal structure can further shorten the solidification time by 29.14% and increase the average heat release rate by 40.5%. Additionally, the study of wall temperature shows that increasing temperature difference makes solidification speed faster and heat energy release faster. This effect effectively eliminates the adverse effects of slow solidification during the later stages of the process on the system.
Precise prediction of heat demand is crucial for optimising district heating (DH) systems. Energy consumption patterns (ECPs) represent a key parameter in developing a good mathematical model to predict heat demand. This study quantitatively investigated the impacts of ECPs on heat consumption. Two key factors, namely, time and type of buildings, were used to reflect various ECPs in DH systems, and a Gaussian mixture model (GMM) was developed to examine their impacts on heat consumption. The model was trained and validated using the measured data from a real DH system. Results show that the factor of time does not represent a good reflection of ECP. In contrast, categorising buildings according to their function is an effective way to reflect ECPs. Based on the defined building types, i.e., commercial, apartment and office, the average absolute deviation of the predicted heat load was about 4-8%.
The building sector accounts for a large part of the energy use in Europe and is a sector where the energy efficiency needs to improve in order to reach the EU energy and climate goals. The energy efficiency goal is set in terms of primary energy even though there are different opinions on how to calculate primary energy. When determining the primary energy use in a building several assumptions are made regarding allocation and the value of different energy sources. In order to analyze the difference in primary energy when different methods are used, this study use 16 combinations of different assumptions to calculate the primary energy use for three simulated heating and ventilations systems in a building. The system with the lowest primary energy use differs depending on the method used. Comparing a system with district heating and mechanical exhaust ventilation with a system with district heating, mechanical exhaust ventilation and exhaust air heat pump, the former has a 40% higher primary energy use in one scenario while the other has a 320% higher in another scenario. This illustrates the difficulty in determining which system makes the largest contribution to fulfilling the EU energy and climate goals.
The European Union's directive of the energy performance of buildings makes energy systems with local energy generation interesting. To support local energy generation the government has appointed a commission to investigate the possibility to implement net metering for grid connected PV-systems. In this paper three different systems are simulated and analyzed with regards to economics and energy: a PV-system and a heat pump (alternative 1), a heat pump and a solar thermal system (alternative 2) and a heat pump, a PV-system and a solar thermal system (alternative 3). System alternative 1 is profitable with daily net metering and monthly net metering and unprofitable with instantaneous net metering. The solar electrical fraction of the system is 21.5%, 43.5% and 50%, respectively. System alternative 2 is unprofitable and has a solar electricity fraction of 5.7%. System alternative 3 is unprofitable and has a solar electricity fraction of just below 50. The conclusion is that a PV system in combination with a heat pump is a superior alternative to a solar thermal system in combination with a heat pump.
The energy consumption of buildings accounts for a major share in the modern society. Accurate forecast of building thermal demand is of great significance to both building management systems and heat distribution networks. Machine learning models driven by abundant load data have demonstrated their great capability in predicting real-world consumption patterns and trends. A large number of input features have been considered in the literature for developing data-driven models. However, a thorough analysis regarding their importance is currently lacking. This work first presents a review on the commonly considered features in building thermal demand prediction models, and focuses particularly on their influences. To further facilitate investigating the impacts of various input features, based on a four-year dataset collected from a district heating system with 13 input features, a deep learning model, the long short-term memory (LSTM) network, is employed for a real-world case study. Our results suggest that the past load, outdoor temperature, and hour index have the greatest influence, and should be primarily considered in building thermal demand forecast models. For the studied case, they lead to an RMSE of 12.231 MW and a CV-RMSE of 5.814 %. Additionally involving wind speed and day index is also useful, which improves the RMSE to 11.971 MW and CV-RMSE to 5.691 %. On the contrary, including all available features does not achieve a bettery accuracy, in which RMSE and CV-RMSE are 12.349 MW and 5.871 %.
Time-use data, describing in detail the everyday life of household members as high-resolved activity sequences, have a largely unrealized potential of contributing to domestic energy demand modelling. A model for computation of daily electricity and hot-water demand profiles from time-use data was developed, using simple conversion schemes, mean appliance and water-tap data and general daylight availability distributions. Validation against detailed, end-use specific electricity measurements in a small sample of households reveals that the model for household electricity reproduces hourly load patterns with preservation of important qualitative features. The output from the model, when applied to a large data set of time use in Sweden, also shows correspondence to aggregate profiles for both household electricity and hot water from recent Swedish measurement surveys. Deviations on individual household level are predominantly due to occasionally ill-reported time-use data and on aggregate population level due to slightly non-representative samples. Future uses and developments are identified and it is suggested that modelling energy use from time-use data could be an alternative, or a complement, to energy demand measurements in households.