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Heating, ventilation, and air-conditioning (HVAC) equipment faults and operational errors result in comfort issues and waste of energy in buildings. An Automatic Fault Detection and Diagnosis (AFDD) tool could help facility managers fix comfort and energy issues more efficiently, by identifying the most probable root causes. Existing AFDD methods mostly focus on equipment-level fault detection and diagnostics; almost no attention is given to building level fault diagnosis, considering inter-dependency between equipment through the energy distribution chain. In this work we propose a methodology to automatically derive a Bayesian network from HVAC system topology description such as Haystack. This Bayesian network models and estimates the state of all elements in the system, helping users to identify the most probable root fault. As it is able to ingest evidence from any source (field data, operators, or other models) and is capable of updating its estimates when new evidence is delivered, such a tool could have a great potential to be used interactively on the field. We applied the proposed methodology on simulated and real-world buildings and present in this paper one specific case. • Transfer building system topology and expert knowledge to a Bayesian network. • Transform equipment-level fault detection into building-wide fault diagnosis. • Experiments on simulated and real-world buildings. [ABSTRACT FROM AUTHOR]

• This paper quantifies model errors in fan pressurization tests in windy anisothermal conditions. • Maximum error is characterized with fan-induced, weather-induced, and reference pressures. • Both the power-law and the quadratic law can yield significant errors at low pressures. • Possibilities for testing in windy anisothermal conditions are extended compared to ISO 9972. Building pressurization test methods assume that the flow through building envelopes follows a power law or a quadratic law with a specific correction to account for wind and stack effects. This paper investigates the model error stemming from these assumptions using a numerical approach. The model developed calculates the air flow rate through a building envelope assuming it can be viewed as an array of parallel cracks following a quadratic law. On the one hand, the model gives the ideal leakage air flow rate at a given reference pressure across all leaks; on the other hand, it calculates the leakage air flow rate at the same reference pressure that would be obtained with a pressurization test. In calm isothermal conditions, sensitivity analyses show the error in the leakage air flow rate is in the range of –32% to 0% and −15% to 25% at 4 Pa for the quadratic and the power laws, respectively; it is in the range of 0% to 1.5% and −1% to 0.7% at 50 Pa. In windy anisothermal conditions, the range of the relative error strongly depends on the constraints put on the induced pressures. In our analyses, ISO 9972 constraints disqualify a significant number of tests without reducing the maximum error. The results obtained on a very large number of tests enabled us to derive a simple relationship between the maximum error on the leakage air flow rate, the fan-induced pressure, the weather-induced pressure, and the reference pressure. This relationship could be used for estimating uncertainties in pressurization tests for a given building in given weather conditions without invalidating tests a priori based on criteria such as the zero-flow pressure or the wind speed. The discussion details the model limitations. Note in particular that it applies only to steady weather conditions. Further research is necessary to consider unsteady conditions which are common in the field. [ABSTRACT FROM AUTHOR]

[Display omitted] • Organic PCM shows a appropriate phase change point considering the ambient temperature. • Shape stabilisation is the most prevailing incorporation because friendly and cheap operation. • Physical properties of PCM composites should get attention as equivalent to thermal properties. • The performance of PCM relies heavily on the ambient condition of construction. Building construction deserves many attentions due to its huge energy consumption, while Phase Change Materials (PCMs) provide positive solutions for improving energy efficiency and enhancing the thermal properties of construction materials. However, PCMs also present some negative impacts, such as weakening mechanical properties and increasing costs, chemical instability and so on. In this paper, the main characteristics of PCMs, design and incorporating methods, effects on energy consumption and construction reliability are comprehensively reviewed and discussed. Although many materials have the capacity of phase change, some organic PCMs are more suitable due to the higher latent heat and favourable phase change point in buildings, when eutectic PCMs present greater potential to become the optimal one but much effort is required for investigations. Current design methods and application in construction materials can meet the essential requirements, but the effectiveness is inadequate, including low efficiency of phase changing, leading to low energy storage. Subsequently, some promising research direction and critical areas for optimization are also proposed accordingly in this paper. Future development of PCMs, including novel PCM and efficient incorporation, real applications and functions in buildings are proposed. Additionally, multifunctional construction materials combining PCM deserve much attention and possess promising prospect for energy saving in sustainable and energy efficient building construction. [ABSTRACT FROM AUTHOR]

This paper investigates factors associated with variation in daily total (electricity and gas) energy consumption in domestic buildings using linked pre-COVID-19 smart meter, weather, building thermal characteristics, and socio-technical survey data covering appliance ownership, demographics, behaviours, and attitudes for two nested sub-samples of 1418 and 682 British households selected from the Smart Energy Research Laboratory (SERL) Observatory panel. Linear mixed effects modelling resulted in adjusted R2 between 63% and 80% depending on sample size and combinations of contextual data used. Increased daily energy consumption was significantly associated (p -value < 0.05, VIF < 5) with: households living in buildings with more rooms and bedrooms, that are older, more detached, have air-conditioning, and experience colder (more heating degree days) or less sunny weather; households with more adult occupants, more children, older adult occupants, higher heating temperature setpoints, and that do not try to save energy. The results demonstrate the value of smart meter data linked with contextual data for improving understanding of energy demand in British housing. Accredited UK researchers are invited to apply to access the data, which has recently been updated to include over 13,000 households from across Great Britain. This paper provides guidance on appropriate methods to use when analysing the data. [ABSTRACT FROM AUTHOR]

Heating and cooling systems based on hydrogen sorption in metallic alloys have attracted researchers due to their advantages such as environmental friendliness, ability to utilize low-grade waste or renewable energy, wide operating temperature ranges, and availability of a plethora of alloys to choose from. These heat-operated systems offer a viable alternative to conventional electrically driven HVAC systems. In this paper, a metal hydride-based heating and cooling system (MHHCS) which can cater to the year-round comfort air-conditioning of locations with extreme climatic conditions is proposed. The paper describes the cyclic operation of the MHHCS for application in buildings through the different seasons of a year. The performance of the system is studied using a matched alloy pair of Zr 0.82 Mm 0.09 Ti 0.09 Fe 1.4 Cr 0.6 – LaNi 4.6 Al 0.4 chosen based on thermodynamics and reaction kinetics. This paper discusses the effects of system parameters and operating conditions on the year-round performance of the MHHCS. [ABSTRACT FROM AUTHOR]

• Self-consumption in residential sector is studied under Spanish regulatory framework. • An average-day model versus a whole-year model is benchmarked. • Scheduling household appliances is analysed in a residential building. • Ex-ante and ex-post renewable energy sharing is investigated. Studies on self-consumption in residential use are constantly evolving, and society claims new solutions in response to changes to new paradigms. This paper aims to optimise the sizing of a self-consumption installation for collective self-consumption in a residential building under the Spanish regulatory framework. The study considers the current price volatility in the retail electricity Spanish market and focuses on the techno-economic analysis of self-consumption in a building consisting of 12 dwellings using photovoltaic energy and the possible support of a storage system. The three aspects analysed are the sizing of the self-consumption facility, the use of smart appliances to shift their consumption to more cost-effective times and the sharing of the renewable energy generated by the facility among the consumers participating in the collective self-consumption. This study employs two models, an average-day model and a whole-year model. The techno-economic analysis yields that between the two self-consumption modalities for residential consumption, the modality with surplus simplified compensation is more profitable than the one without surpluses. Finally, the study focuses on sharing the total renewable energy generated among the participants. The results indicate that both ex-ante and ex-post dynamic sharing brings few benefits. [ABSTRACT FROM AUTHOR]

[Display omitted] • Trees reduce the thermal radiation absorbed by the west wall. • Short-wave radiation disturbance peak values are 2–3 times that of long-wave. • Planting is recommended at a min distance ≤4 m from the wall canopy. • Planting center distance should be less than 1/2 the width of the wall. Many studies have shown that planting trees near the west wall of a building is conducive to saving energy for cooling in summer. However, there is a lack of research quantifying the thermal radiation disturbance caused by the location of trees near the west wall. This paper studies the influence of long-wave and short-wave thermal radiation disturbance of trees at different locations around the west wall in summer through field experiments and ENVI-met simulations. It was found that the thermal radiation disturbance (T RD) of trees on the west wall of the building is mainly caused by short-waves which produce 2–3 times greater disturbance than that of long-waves. According to the fitting relationship between the change rate of daily thermal radiation disturbance, the view factor and the tree projection area, the functional formula between them is obtained. These findings provide a theoretical foundation for scientifically evaluating the influence of tree location and practical applications for guiding the planning and landscaping of vegetation around building walls. [ABSTRACT FROM AUTHOR]

• Develop a new strategy to evaluate GAN accuracy for building power demand prediction. • Identify and evaluate five GANs for predicting large-scale building power demand. • Recommend Original GAN and cGAN for large-scale building power demand prediction. As an unsupervised-learning data-driven model, Generative Adversarial Networks (GANs) have recently attracted a lot of attention for various applications. There is potential to apply GANs for large-scale building power demand prediction, which is needed for power grid operation. However, there are many GAN variations and it is unclear which GAN is suitable for this application. To answer this question, this paper identifies five promising GANs (Original GAN, cGAN, SGAN, InfoGAN, and ACGAN) and evaluates their performance for predicting building power demand at a large scale. Physics-based building energy models are developed to generate training and reference data. A new evaluation indicator that combines accuracy and reproducibility is proposed to evaluate the performance of different GANs in predicting building power demand. The results show that SGAN and InfoGAN are not suitable because they cannot control the number of generated building samples for different building types. The prediction performance among the Original GAN, cGAN, and ACGAN can vary depending on training sample sizes and number of building types. If the training sample size is sufficiently large, Original GAN and cGAN can predict building power demand more accurately than ACGAN with the same number of samples. If training samples are limited, Original GAN provides better accuracy than cGAN and ACGAN. When the number of building types increase, the prediction accuracy increases for cGAN, decreases for ACGAN, and remains the same for Original GAN. As a result, cGAN and Original GAN are recommended for large-scale building power demand prediction. [ABSTRACT FROM AUTHOR]

A large part of energy consumption in homes, offices and commercial spaces is related to Heating, Ventilation and Air-conditioning (HVAC) devices. The effective parameter on the consumption of HVAC systems is internal heat gains that arise from occupants, electric equipment and lighting. In order to reduce the energy consumption of these systems, internal heat gains should be predicted accurately. Since there are few investigations performed on the prediction of internal heat gains, in this paper, three predictive models, namely multiple regression model, Levenberg–Marquardt back-propagation (LM-BP) model and similar days method based on combined weights, have been deployed. By assessing the influential factors on internal heat gains, fundamental theories, structures, equations and parameters of these models are thoroughly proposed. To examine the prediction techniques, an office building in China was considered. It was found that all the proposed models have high accuracy; however, the LM-BP neural network showed the most precision among other models with RMSE = 15.59, MAE = 10.16 and MAPE = 6.35. This model had a higher agreement with the actual internal heat gains compared to the predetermined working programs in the ASHRAE standard 90.1. The proposed models used in this study can lead to providing a theoretical base for scholars and engineers to improve the predictive control of HVAC systems, which plays an important role in enhancing thermal comfort, saving energy of residential buildings. [ABSTRACT FROM AUTHOR]

• A novel energy-water ratio is developed for assessment of energy-water nexus. • A thermal model is developed to energy-water nexus. • The thermal model can be used to evaluate the value of energy-water nexus. • Different interdependence between energy-water at the building level are found. • The improved energy efficiency of boiler can save not only energy but also water. Energy and water are two critical resources, and their security has been becoming a regional, national and global priority. The paper investigates the energy-water nexus at the building level through developing a novel energy-water ratio and a thermal model, to provide insight about the interaction mechanism between energy and water at the building level. The developed energy-water ratio has clear physical meaning. The thermal model developed can be used to explore the dynamic interdependences and feedback mechanism between energy and water, and to evaluate the value of interdependence and the real value of infrastructure through linking to business model. The dynamic of energy-water nexus for three kinds of buildings in London is then examined under present and future climate scenarios. Results show that the interdependences of energy-water for these three kinds of buildings vary over time and are different each other. The different interdependences are shaped by the combined effect of both the configurations of infrastructures for building and the environmental condition. The improved energy efficiency of boiler can save not only energy but also water, indicating the positive interaction between energy and water. Results have informative implications for sustainable building and investment in household infrastructures, and suggest an integrated way for co-managing both energy and water effectively. [ABSTRACT FROM AUTHOR]