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The thermal energy storage (TES) technology has gained so much popularity in recent years as a practical way to close the energy supply–demand gap. Due to its higher energy storage density and long‐term storage, thermochemical energy storage (TCES), one of the TES methods currently in use, seems to be a promising one. These potential advantages have triggered to undertake a decent amount of research investigations in the past few years. The present review paper summarizes the recent outcomes of TCES systems for building water and space heating applications and demonstrates the different kinds of systems and their configuration arrangements. The recently developed experimental as well as theoretical prototypes are looked over with respect to their arrangement (closed and open loop) and role of relevant operating conditions. Various kinds of reactor shapes are also summarized and presented. Critical issues like materials’ achievable heat storage density/capacity, stability/cyclability, charging temperature, and systems’ mass and heat transfer properties are discussed. This work also presents the current challenges and the possible suggestions to address them. This review suggests that additional research is necessary to determine the role of influencing parameters in the design and establishment of TCES prototypes for building’s water and space heating applications.

The residential sector in Ireland is a large user of solid fuels for space heating purposes. Solid fuels are commonly used to supplement other forms of heating rather than as the primary source. Using a survey data set of Irish households and a multinomial logit approach, differences between the household characteristics of primary and supplementary solid fuel users are identified, including for levels of education, age of dwelling, location and pro-environmental attitudes. Evidence also shows that increases in income lead to a transition away from primary solid fuel use but not supplementary consumption, suggesting that an energy stacking model explains the household’s choice of heating fuels in Ireland. Given the established effects that solid fuels have on air quality and the scale of supplementary solid fuel use, policies to promote a transition to cleaner fuels need to account for the clear differences in the features of the two user groups.

Around 60% of households in Texas currently rely on electricity for space heating. As decarbonization efforts increase, non‐electrified households could adopt electric heat pumps, significantly increasing peak (highest) electricity demand in winter. Simultaneously, anthropogenic climate change is expected to increase temperatures, the potential for summer heat waves, and associated electricity demand for cooling. Uncertainty regarding the timing and magnitude of these concurrent changes raises questions about how they will jointly affect the seasonality of peak demand, firm capacity requirements, and grid reliability. This study investigates the net effects of residential space heating electrification and climate change on long‐term demand patterns and load shedding potential, using climate change projections, a predictive load model, and a direct current optimal power flow (DCOPF) model of the Texas grid. Results show that full electrification of residential space heating by replacing existing fossil fuel use with higher efficiency heat pumps could significantly improve reliability under hotter futures. Less efficient heat pumps may result in more severe winter peaking events and increased reliability risks. As heating electrification intensifies, system planners will need to balance the potential for greater resource adequacy risk caused by shifts in seasonal peaking behavior alongside the benefits (improved efficiency and reductions in emissions). Plain Language Summary Electric heat pump adoption could help abate the impacts of climate change on overall power system reliability by reducing summer cooling demand in a warmer world because heat pumps are a more efficient alternative to standard air conditioning. It would also carry greenhouse gas emissions benefits in a decarbonized power grid. Yet widespread adoption of heat pumps (especially low‐efficiency heat pumps) could cause more extreme winter peaking events. During these extreme cold events, higher peak demand could result in an increased probability of outages. Higher efficiency heat pumps would help avoid these winter reliability impacts, but these more efficient heat pumps are also currently more expensive. From a grid planning perspective, planners need to account for a range of future peak demand dynamics across different heat pump adoption scenarios and climate futures. Key Points Widespread electric heat pump adoption and climate change will increase uncertainty in seasonal peaking patterns and grid reliability Future peaking behavior depends on the frequency and severity of extreme heat and cold events and the type of heat pumps widely adopted Shifts toward winter peaking could carry large resource adequacy risks, depending on pump efficiency and climate scenario

Phase change materials (PCMs) designate materials able to store latent heat. PCMs change state from solid to liquid over a defined temperature range. This process is reversible and can be used for thermo-technical purposes. The present paper aims to study the thermal performance of an inorganic eutectic PCM integrated into the rooftop slab of a test room and analyze its potential for building thermal management. The experiment is conducted in two test rooms in Antofagasta (Chile) during summer, fall, and winter. The PCM is integrated into the rooftop of the first test room, while the roof panel of the second room is a sealed air cavity. The work introduces a numerical model, which is built using the finite difference method and used to simulate the rooms’ thermal behavior. Several thermal simulations of the PCM room are performed for other Chilean locations to evaluate and compare the capability of the PCM panel to store latent heat thermal energy in different climates. Results show that the indoor temperature of the PCM room in Antofagasta varies only 21.1°C±10.6°C, while the one of the air-panel room varies 28.3°C±18.5°C. Under the experiment’s conditions, the PCM room’s indoor temperature observes smoother diurnal fluctuations, with lower maximum and higher minimum indoor temperatures than that of the air-panel room. Thermal simulations in other cities show that the PCM panel has a better thermal performance during winter, as it helps to maintain or increase the room temperature by some degrees to reach comfort temperatures. This demonstrates that the implementation of such PCM in the building envelope can effectively reduce space heating and cooling needs, and improve indoor thermal comfort in different climates of Chile.

Space heating consumption prediction is critical for energy management and efficiency, directly impacting sustainability and efforts to reduce greenhouse gas emissions. Accurate models enable better demand forecasting, promote the use of green energy, and support decarbonization goals. However, existing models often lack precision due to limited feature sets, suboptimal algorithm choices, and limited access to weather data, which reduces generalizability. This study addresses these gaps by evaluating various Machine Learning and Deep Learning models, including K-Nearest Neighbors, Support Vector Regression, Decision Trees, Linear Regression, XGBoost, Random Forest, Gradient Boosting, AdaBoost, Long Short-Term Memory, and Gated Recurrent Units. We utilized space heating consumption data from the European Central Bank Headquarters office as a case study. We employed a methodology that involved splitting the features into three categories based on the correlation and evaluating model performance using Mean Squared Error, Mean Absolute Error, Root Mean Squared Error, and R-squared metrics. Results indicate that XGBoost consistently outperformed other models, particularly when utilizing all available features, achieving an R2 value of 0.966 using the weather data from the building weather station. This model’s superior performance underscores the importance of comprehensive feature sets for accurate predictions. The significance of this study lies in its contribution to sustainable energy management practices. By improving the accuracy of space heating consumption forecasts, our approach supports the efficient use of green energy resources, aiding in the global efforts towards decarbonization and reducing carbon footprints in urban environments.

3D city models are frequently used to acquire and store energy-related information of buildings for energy applications. In this context, CityGML is the most common data model, and the Energy ADE, one of its most complex extensions, provides a systematic way of storing detailed energy-related data in XML format. Contrarily, even though CityGML’s JSON-based encoding, CityJSON, has an extension mechanism, an energy-related CityJSON Extension is missing. This paper, therefore, presents the first results of the development of a CityJSON Energy Extension and space heating demand calculation is utilized as the use case. The simplified version of the Energy ADE, called the Energy ADE KIT profile, is used to create a semi-direct translation to the CityJSON Energy Extension. This Extension is then validated through the official validator of CityJSON and the use case, and improvements are made considering the validation results. The space heating demand is calculated according to the Dutch standard NTA 8800 for a subset of Rijssen-Holten in the Netherlands although the solar gains calculation requires further review. The results show that the final CityJSON Energy Extension provides full support for space heating demand calculations based on the NTA 8800 and eliminates the deep hierarchical structure of the Energy ADE. A comparison on CityJSON file sizes shows a 25.2 MB increase after the required input data is stored in a CityJSON + Energy Extension file, which is not significant considering the high amount of data stored in the file. Overall, this paper shows that the CityJSON Energy Extension could provide an easy-to-use alternative to the CityGML Energy ADE.

In homes in the UK, it is very common to operate space heating intermittently; the heating is usually switched off when the occupants are asleep at night and when they are out during the day. The strong association between heating operation and household routines leads to a morning peak in demand which, if it persists following electrification of heating, will require significant reinforcement of electricity supply networks.This paper examines factors that underpin how heating is used in the UK. A unique dataset of heating controller settings from 337 UK allows investigation of how patterns of heating operation in individual homes contribute to daily patterns of space heating energy consumption at the group level. A mixed method approach is followed, combining quantitative analysis of data with interviews with householders.The concept of thermal routines is introduced, bringing a time dimension to the consideration of domestic thermal comfort and recognising that demand for space heating is linked to patterns of practices in the home, which are themselves linked to social routines, e.g. timing of work and school. The results from this study suggest that household thermal routines around 07:00 in the morning are a particularly important consideration for a transition to future energy systems with a high proportion of low carbon heat. Factors that currently limit flexibility of heating demand in the UK are identified, and the implications for a transition to low carbon heating sources are discussed.

This paper is part of broader research aimed at determining the relationship between energy performance and energy costs as a part of the operational and life cycle costs in school buildings in the Federation of Bosnia and Herzegovina (FBiH), as exceptionally important social and public buildings. The research was conducted by statistical analysis of data collected from documents of detailed energy audits (DEA) for 185 school buildings in FBiH in relation to construction periods. The paper analyzes the characteristics of buildings such as construction period, building envelope characteristics, climatic conditions, efficiency of installed space heating system, number of users and heating mode. The aim of this research was to determine the energy performance for the existing state and to compare them with the allowable values in accordance with the applicable legal regulations. There is a performance gap between predicted (calculated) and measured (actual) delivered energy for space heating. This research shows poor energy performance and provides a basis for developing strategies and plans to improve energy efficiency. The results of the energy performance of school buildings in the FBiH are the first step towards the development of a model for predicting energy costs.

PurposeThe purpose of this paper is to present a methodology for estimating scheduled and manual override heating events and heating settings from indoor air temperature and gas use measurements in UK homes.Design/methodology/approachLiving room air temperature and gas use data were measured in ten UK homes built to low energy standards. The temperature measurements are used to establish whether the central heating system is turned on or off and to estimate the heating setpoint used. The estimated heating periods are verified using the homes' average daily gas consumption profiles.FindingsUsing this method, the average number of heating periods per day was 2.2 (SD = 0.8) on weekdays and 2.7 (SD = 0.5) on weekends. The weekday mean heating duration was 8.8 h and for weekends, it was 9.8 h. Manual overrides of the settings occurred in all the dwellings and added an average of 2.4 h and 1.5 h to the heating duration on weekdays and weekends respectively. The mean estimated setpoint temperatures were 21.2 and 21.4°C on weekdays and weekends respectively.Research limitations/implicationsManual overrides of heating behaviours have only previously been assessed by questionnaire survey. This paper demonstrates an alternative method to identifying these manual override events and responds to a key gap in the current body of research that little is currently reported on the frequency and duration of manual heating overrides in UK homes.Practical implicationsThe results could be used to better inform the assumptions of space heating behaviour used in energy models in order to more accurately predict the space heating energy demands of dwellings.Originality/valueManual overrides of heating behaviours have only previously been assessed by questionnaire survey. This paper demonstrates an alternative method to identifying these manual override events and responds to a key gap in the current body of research that little is currently reported on the frequency and duration of manual heating overrides in UK homes.

Heat pump systems and radiant floor heating systems are extensively employed to adjust indoor temperatures. Both types of system can reduce energy consumption and increase the coefficient of performance, with some limitations, to further improve energy conservation and environmental protection. For this reason, the development of an environmentally friendly and energy-saving system that is suitable for future energy demands is necessary. A new space heating system coupled with an underground energy storage system, without the use of heat pumps, is proposed herein. To validate the practicality and feasibility of the methodology established in this study, many simulations were performed, and sensitivity analyses of possible influencing factors were conducted. The modeling results proved that human indoor space heating demands can be satisfied with almost zero carbon emissions using the system proposed in this study.