Energy and Built Environment最新文献

As part of a broad strategy to reach net-zero greenhouse gas emissions and limit global warming, many countries are requiring all new buildings to have net-zero energy use. This requires that on-site energy use not exceed on-site generation of renewable energy (taken here to be solar energy), or equivalently, that the building Energy Use Intensity (EUI, kWh/m²a) not exceed the supply of on-site solar energy (electricity and heat) per m² of floor area per year. On this basis, we find that achieving net-zero energy performance in an archetype 40-story square building in 16 different cities of North America requires EUI of 17–24 kWh/m²a using PV panels, and 19–28 kWh/m²a using PVT collectors. Changing building orientation to a non-square floor shape can improve maximum permitted EUI by up to 50% in PV and 60% in PVT case. Conversely, the best-performing residential and commercial buildings have EUIs of 50–75 kWh/m²a. Only if building heights are limited to 5–10 floors does the available solar energy, and thus the permitted EUI, reach 50–75 kWh/m²a. Therefore, we recommend that policymakers not require high-rise buildings to be net-zero energy, unless they are prepared to limit building heights to 5–10 floors.

Improper development of land uses in the city leads to climate changes, resulting in an increase in GW and the formation of UHIs. These changes have adverse effects on people's level of comfort. This research is supposed to extract the optimal model by proving the relationship between the physical arrangement of the architecture of high-rise buildings in residential complexes and reducing the adverse effects of this. Therefore, in this research, four models of solitary, environmental, combined, and rowly block arrangement were investigated based on numerical calculation methods and CFD simulation. These simulations were done by ENVI-meto software based on air temperature, relative humidity, wind speed, and thermal comfort in the open space. The results showed that the type of physical arrangement of buildings can increase air temperature for GW by up to 3 °C and UHIs by 0.5 °C. The combined pattern is the most optimal in this section due to its more compact structure than the solitary pattern. Regarding the effect of relative humidity on climate changes, the solitary pattern has the lowest percentage of relative humidity compared to other patterns due to more air circulation in its physical structure. Also, the type of physical arrangement of buildings can improve the wind speed for GW by up to 0.2 m/s and for UHIs by up to 0.7 m/s. Based on this, the most optimal model is the environmental pattern, because the physical structures of the buildings are an obstacle in wind circulation. The fluctuation range of the PMV index for the intensity of GW effects on thermal comfort, is 0.7°, and the fluctuation range of this index for the power of UHIs is about 0.2°. The solitary pattern is the most optimal pattern to reduce the severity of adverse effects of GW and UHIs; this is due to the scattered distribution of blocks in this pattern. In general, according to the research findings, it can be concluded that the most optimal pattern to reduce the severity of the adverse effects of GW is solitary and environmental patterns, and to reduce the severity of the negative impact of UHIs, the solitary pattern is used.

Accurate and reliable load forecasting is crucial for ensuring the security and stability of the power grid. This paper proposes a combined prediction method based on Empirical Wavelet Transform (EWT) and Autoformer time series prediction model for the non-stationary and non-linear time series of electric load. The original sequence is first decomposed by EWT to obtain a set of stable subsequences, and then the Autoformer time series prediction model is used to predict each subsequence. Finally, the prediction results of each subsequence are combined to obtain the final prediction results. The proposed EWT-Autoformer prediction model is applied to an electric load example, and the experimental results are compared with the Recurrent Neural Network (RNN) method, Long Short-Term Memory (LSTM) method, and Informer method under the same conditions. The experimental results indicate that compared to LSTM, the method proposed in the paper has an R² improvement of 9–20 percentage points, an improvement of 6–8 percentage points compared to RNN, an improvement of 3–7 percentage points compared to Informer, and an improvement of 2–3 percentage points compared to Autoformer. In addition, the RMSE and MAE are also significantly lower than other models.

Fault detection and diagnosis (FDD) approaches comprise three main pillars: model-based, knowledge-based, and data-driven strategies. Data-driven approaches prioritise operational data and do not necessitate in-depth understanding of the system's background; yet, significant amounts of data is required, which often poses challenges to researchers. Since simulated data is inexpensive and can run numerous faults types with varying severities and time periods, it has been used in data-driven FDD analysis. However, the majority of FDD approaches are implemented at the system level of buildings. However, most buildings have numerous systems with distinct features. Furthermore, using individualised system-level analysis makes it difficult to see system-to-system relationships. Currently, there is a significant underrepresentation of research that investigate the applications of FDD models under whole-building scenarios, so as to identify a wider range of energy consumption related faults in buildings. Furthermore, since data-driven approaches significantly depend on the quantities of training data, it becomes challenging to diagnose faults that have limited features. As a result, this study diagnoses numerous building systems faults, including single and simultaneous faults with limited features. This is implemented within the context of the whole-building energy performance of religious buildings in hot climatic areas, employing data-driven FDD methodologies. Various multi-class classification approaches were investigated to classify both the normal condition and faulty classes. Furthermore, feature extraction methodologies were compared to quantify their potential for improving the diagnosis. In addition to the classification evaluation metrics, one-way ANOVA and Tukey-Kramer tests were implemented to examine the significance of the reported performance differences. RF classifier obtained highest classification accuracy during validation and testing with about 90%, indicating a promising performance in whole-building faults analysis. The adoption of feature extraction techniques did not improve classification performance, thereby emphasising that some classifiers may perform better with high-dimensional datasets.

The building stock is responsible for a large share of global energy consumption and greenhouse gas emissions, therefore, it is critical to promote building retrofit to achieve the proposed carbon and energy neutrality goals. One of the policies implemented in recent years was the Energy Performance Certificate (EPC) policy, which proposes building stock benchmarking to identify buildings that require rehabilitation. However, research shows that these mechanisms fail to engage stakeholders in the retrofit process because it is widely seen as a mandatory and complex bureaucracy. This study makes use of an EPC database to integrate machine learning techniques with multi-objective optimization and develop an interface capable of (1) predicting a building’s, or household’s, energy needs; and (2) providing the user with optimum retrofit solutions, costs, and return on investment. The goal is to provide an open-source, easy-to-use interface that guides the user in the building retrofit process. The energy and EPC prediction models show a coefficient of determination (R${}^2$) of 0.84 and 0.79, and the optimization results for one case study EPC with a 2000€ budget limit in Évora, Portugal, show decreases of up to 60% in energy needs and return on investments of up to 7 in 3 years.

Building temperature setpoints affect both HVAC energy consumption and occupant comfort. To reduce HVAC energy usage, researchers often investigate how system operations can be optimized under weather and occupancy variability subject to a fixed setpoint that minimizes any possible discomfort. While previous research has explored the selection of dynamic setpoints to minimize HVAC energy consumption based on outdoor temperature, they have often neglected the impact of varying occupancy rates on the setpoints. This paper aims to demystify energy savings derived from fixed and dynamic temperature setpoints under weather and occupancy variability and explores the additional energy savings that can be achieved through dynamic temperature setpoints. An exhaustive HVAC zone temperature setpoint optimizer was developed to determine dynamic setpoints with respect to weather and occupancy (i.e., setpoints that minimize HVAC energy consumption at different occupancy rates based on outdoor weather). U.S. DOE reference building energy models for small, medium, and large office buildings were simulated at 17 climate zones, 4 occupancy rates (25%, 50%, 75%, 100%) and 7 setpoints (19.5°C to 25.5°C at 1°C interval). It was found that, both fixed and dynamic setpoints benefit from the energy reduction of approximately 2-4% from the lower heat generated by the occupants at lower occupancy rates. However, at outdoor temperatures between 5°C and 32°C where occupant heat loads can swing the building between heating, free-running, and cooling modes, dynamic setpoints yield additional 2-10% energy savings, compared to fixed setpoints.

In this study, we will model a light-weight building made of phase change materials (PCMs) to analyze the impact of the building volume, window orientation, and air infiltration on the PCM performance. This is done by calculating the energy savings attained by the use of PCM across all of Morocco. We'll use the commercial Rubitherm panels with RT28HC as a phase change material in this work. Typically, the EnergyPlus simulation engine is chosen to perform the modeling. The impact of building volumes is also evaluated on the PCM activation for light-weight square buildings with different side lengths of 10 m, 9 m, 8 m, and 7 m. Also, we looked at how well the PCM performed in terms of energy savings and thermal regulation at different window orientation placements (south, north, west, and east) and various air infiltration rates (0.5, 1, 1.5, and 3 ACH). This paper's primary objective is to determine the energy savings for the PCM-enhanced building in Morocco, as well as the effect of building volume, window orientation, and infiltration on the PCM capabilities for stabilizing the indoor room temperature. The results show good indoor temperature stabilization during the summer, for the light-weight square building with a south-facing window and no air infiltration. This configuration was able to achieve a total fluctuation reduction of 1303.3 °C for the 10 m building in a semi-arid environment. Besides, a high energy savings percentage of 69.56% was achieved for the PCM-enhanced building with the south-oriented window and air infiltration of 0.5 air change per hour.

Thermal comfort is critical for ensuring the health and productivity of occupants and knowing their thermal demand could help creat a satisfying environment with the least energy waste. Theory of thermal comfort is built based on experimental studies in uniform and stable environments, while there is still a gap when applying in evaluating human thermal comfort in non-uniform thermal environments. Therefore, an insight into human thermal comfort in non-uniform thermal environments is taken. The fundamental studies of thermal comfort in non-uniform thermal environments are explicated, including thermal comfort theory applied in non-uniform thermal environments, types of non-uniform thermal environments and corresponding studies, physiological and psychological thermal responses in the environments. Besides, the evaluation indices of non-uniform thermal environments are classified according to their definitions and the comfort models are reviewed. Finally, future works in this research field are discussed. In general, the overall thermal comfort and local thermal comfort should be both taken into account in non-uniform thermal environments where skin temperatures and psychological thermal responses of occupants are different among local body segments. Moreover, as studies of local thermal comfort are mainly conducted in thermally neutral condition, the limits considering the state deviating thermal neutrality (slightly warm or cool), which is frequently found in reality, are encouraged to be studied. Finally, a well-defined comprehensive index should be proposed considering heat exchange of human body with their microenvironments and thus the comfort range of the index could be provided for designing of non-uniform thermal environments.

The study is focused on the use of nanofluids in a micro-open tall cavity, which is a type of micro heat exchanger (MHE). The cavity is heated from the bottom sidewall in a sinusoidal pattern, and the effects of four input parameters (Ra, Ha, Kn, and Vf) on heat transfer and irreversibility are investigated using numerical simulations based on Lattice Boltzmann Method (LBM). The findings of the study suggest that the local heat transfer on the bottom sidewall is strongly influenced by Ra and Ha, while the surface distribution of entropy generation is mainly dependent on Kn. The study also shows that the optimization of the magnitude and wavelength of the sinusoidal temperature can improve both local heat transfer and surface distribution of entropy generation. The results of the study provide valuable insights into the design of micro heat exchangers and suggest that the optimization of micro-porous geometries using DOE could lead to increased energy efficiency. The study contributes to our understanding of the complex interactions between input parameters in micro heat exchangers and highlights the importance of considering multiple parameters in the design process.

Thermal environment may affect sleep quality and sleeping thermal comfort at the same time. Up to now, there is no direct evidence or physiological theory can prove the idea that thermal comfort equal to good sleep quality, but how to balance the relationship between them is often ignored when designing thermal environment. This review took body temperature distribution as a bridge for the connection of sleep quality and sleeping thermal comfort. At first, we reviewed the quantitative relationships among thermal environment, body temperature, sleep quality and sleeping thermal comfort, and then based on the idea of reverse derivation, a recommended design process for bedroom thermal environment was proposed. Each step of the process corresponded to the impact mechanism described above. The review results showed that, body temperature distribution was closely related to both sleep quality and thermal comfort, and thus it was suitable to be used as a bridge between them; body temperatures fluctuated dynamically during sleep, and thus heat transfer and thermal regulation models should be used to describe them; most current sleep researches regarded skin temperature as the representative value of body temperature distribution, yet core body temperature needs to be paid more attention on in the future because it was more closely related to sleep rhythm; furthermore, in addition to young people, subjects of more age groups should be tested to enrich China's sleep research database. In general, it's theoretically possible to guarantee both sleep quality and sleeping thermal comfort, and this review can be used as a reliable reference for bedroom thermal environment design.