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Building Archetypes (BAs) are often used in modeling urban-scale building energy consumption profiles to represent the energy consumption behavior of typical buildings within the modeled urban environment. One current approach to the development of BAs involves the creation of Building Energy Models (BEMs) using surveyed data coupled with codes, standards, or engineering experience. Another approach is the modification of existing standardized BEMs using available data to produce a suitable BA for the representation of the building stock. The adoption of either of these methods has been treated as an axiom or a step that did not need much consideration. However, the results produced by BEMs created according to different methodologies present several differences. The discrepancy in the produced results is shown not only in the results of the BEMs developed according to different methodologies but also in the degree to which these results vary when changing input values. Limited studies analyzed the sensitivity of these models to uncertain inputs, even though it is essential for the development of BA libraries. This paper comparatively investigates the Region-Specific (RS) and Department of Energy (DOE)-based BEM creation methods for BA library development. The models are developed by following the two methodologies, and their predictions are compared to the metered data from a high-rise residential building in the Marina district of Lusail City, Qatar. A Global Sensitivity Analysis (GSA) method is used to identify the most influential parameters contributing to the energy consumption variation of these models. Further, a parametric simulation campaign is conducted by varying the inputs determined earlier to quantify the BEMs' output variability. According to the results, the baseline RS BEM and DOE BEM yield an Energy Use Intensity (EUI) of 211.03 kWh/m2 and 137.9 kWh/m2, respectively. The deviation of these values from the metered EUI of 185,37 kWh/m2 indicates that the RS BEM development methodology is preferable to the DOE BEM for the BA development. Based on the GSA results, regardless of the BEM development methodology used, the most important parameters contributing to output variation are the Cooling Setpoint, the Outdoor Air Requirements, the Equipment Power Density, and the Lighting Power Density. Further, the RS BEM's parametric simulations produce a mean cooling EUI of 324.83 kWh/m2, closer to the literature benchmarks than the mean cooling EUI of DOE BEM, 341.62 kWh/m2. The results obtained from the baseline model and the uncertainty analysis support the conclusion that a Region-Specific Model developed from the ground up serves to better represent high-rise residential buildings in regions with extreme climate conditions. The impact this work achieves is providing decision-making support for urban scale building energy modelers for future building stock representation efforts in such areas while also providing an expected uncertainty range for the developed high-rise residential archetypes. [ABSTRACT FROM AUTHOR]

• Meter-level anomaly detection and diagnosis is performed through CMP algorithm. • CMP parameters are automatically set using supervised and unsupervised methods. • A severity score, based on 4 statistical methods, is used to recognize anomalies. • 55 anomalous energy consumption patterns are discovered in a one-year timeseries. • Anomalies at meter-level are diagnosed by exploiting information at sub-load level. Recently, the spread of IoT technologies has led to an unprecedented acquisition of energy-related data providing accessible knowledge on the actual performance of buildings during their operation. A proper analysis of such data supports energy and facility managers in spotting valuable energy saving opportunities. In this context, anomaly detection and diagnosis (ADD) tools allow a prompt and automatic recognition of abnormal and non-optimal energy performance patterns enabling a better decision-making to reduce energy wastes and system inefficiencies. To this aim, this paper introduces a novel meter-level ADD process capable to identify energy consumption anomalies at meter-level and perform diagnosis by exploiting information at sub-load level. The process leverages supervised and unsupervised analytics techniques coupled with the distance-based contextual matrix profile (CMP) algorithm to discover infrequent subsequences in energy consumption timeseries considering specific boundary conditions. The proposed process has self-tuning capabilities and can rank anomalies at both meter and sub-load level by means of robust severity score. The methodology is tested on one-year energy consumption timeseries of a medium/low voltage transformation cabin of the university campus of Politecnico di Torino leading to the detection of 55 anomalous subsequences that are diagnosed by analysing a group of 8 different sub-loads. [ABSTRACT FROM AUTHOR]

[Display omitted] • The proposed method can be applied to all window types and does not require the manual adjustment of threshold values according to different weather conditions. • The prediction accuracy of the proposed method in the case study reaches 97.79%. • The features of window opening behavior for a teaching building in severe cold regions are explored. • Occupants of teaching buildings in severe cold regions in the transition season prefer a large opening angle (45°∼180°). The building industry plays an important role in the effort to achieve carbon neutrality, and window opening behavior is a major contributor to building energy consumption. In severe cold regions, opening windows during the heating period causes energy waste. If the window opening behavior can be monitored in real time, then the occupants' energy use behavior can be guided to reduce energy waste. This paper proposes a window opening behavior monitoring method using a deep learning-based image recognition model that can identify the window state based on real-time video and was validated in a case study based on outdoor thermal and wind data. A teaching building with casement windows was used as a case study. The results showed that the occupants preferred a large window opening angle (45°∼180°), which is a choice that significantly affects the building energy consumption. The prediction accuracy of the proposed method can reach 97.79%, which also serves to avoid the difficulty of manually calibrating the pixel threshold and realize real-time feedback. This method has a wide range of application scenarios that enable us to obtain window opening behavior to improve the building performance simulation accuracy and guide occupants' energy consumption behavior. [ABSTRACT FROM AUTHOR]

In recent years, a large number of building energy consumption prediction models, with various intended uses, have been proposed. The majority of these models have either taken a data-driven or a physical modeling approach, with each approach having its own strengths and limitations. Towards leveraging the strengths and reducing the limitations of each approach for improved prediction performance, this paper presents a hybrid machine-learning approach for occupant-behavior-sensitive energy consumption prediction. The proposed approach is composed of three constituent models: (1) a machine-learning model that learns the impact of outdoor weather conditions from simulation-generated data, (2) a machine-learning model that learns the impact of occupant behavior from real data, and (3) an ensemble model that predicts cooling energy consumption based on the outputs of the first two models. The simulation-generated data were created through simulating a set of reference buildings in EnergyPlus. The real data were collected from an office building in Pennsylvania. The proposed hybrid model was validated on an unseen real dataset. It achieved 0.73 kWh RMSE and 9.07% CV in hourly cooling energy consumption prediction, which indicates that the proposed approach is promising. [ABSTRACT FROM AUTHOR]

The objective of this work was to evaluate the interactions of users with air conditioning systems, lighting, natural ventilation, internal shading devices, and costs related to automation of a teaching building. Three behavior patterns (proactive, intermediate and careless) were created. The proactive user seeks to use natural light, uses passive solar shading and integration of natural and artificial ventilation. The intermediate user does not seek integration of natural and artificial lighting, they use hybrid ventilation as a ventilation strategy. The careless user does not use any passive strategy, predominantly uses artificial conditioning and adjusts cooling and heating setpoint to values out of the norms. In addition to the users' behaviors, the automated building was modeled. Computer simulations were performed in the EnergyPlus program, in order to measure the influence of behavior patterns on the building's energy consumption. Based on the analysis of the building's energy consumption profile, results indicate users are between the intermediate and the careless user classification, with a tendency to the intermediate user profile. The paper also presents an analysis of the payback time with the building automation in face of the user profiles. [ABSTRACT FROM AUTHOR]

• End-use of electricity consumption in residential buildings in Gulf Cooperation Council countries was presented. • Discrepancies in certain indicators have been observed by the end-use of electricity consumption. • Improving the efficiency of air conditioners and thermal insulation of the walls are the best measures to conserve electricity in residential buildings. • The results can be used in areas with a similar climate, living standard, buildings types, and the inhabitants' behavior. Energy conservation and improvement in energy efficiency yield multiple technical, economic, environmental, and social benefits and realize comfort and sustainability of residential buildings (RBs). This review paper presents the end-use of electricity consumption in RBs and the results of economic analysis. In addition, a description and discussion of studies on energy conservation measures, Net Zero Energy Building, and renewable energy in RBs are discussed. The percentage of end-used energy consumption for different purposes obtained in many studies are often not consistent. Moreover, discrepancies in certain indicators have been observed, necessitating the need to clarify the factors in the consumption of electricity in RBs. Further, a few studies used the term energy consumption in buildings (total or electrical); however, specifying the type of buildings to identify and use the necessary indicators of energy consumption in each type of building are desirable. Furthermore, most studies emphasized improving the efficiency of air conditioners and thermal insulation of the walls as these are the best measures to conserve electricity. [ABSTRACT FROM AUTHOR]

• The degree of influence of residential cluster layout factors on building heating energy consumption levels in cold regions of China. • Provides an idea of residential layout optimization and design based on the energy saving objectives of residential clusters, translating quantitative indicators such as wind environment into residential layout form design problems. • Energy simulation-based residential planning and layout design method is used to quantify the relationship between residential cluster design factors and energy efficiency in Jinan, resulting in optimal design parameters for residential planning factors in Jinan. • The study of building heating energy consumption in a city in a cold region of China, with a methodology that can be used in countries or cities with similar climate characteristics. Residential heating energy consumption in China accounts for about 34% of the total building operation energy consumption in the country. The layout pattern of residential clusters is one of the most critical factors affecting building heating energy consumption, yet few scholars have studied the layout of residential clusters in a quantitative way. This paper takes Jinan, a typical city in cold region IIB of China, as an example, to quantify and analyse the residential layout parameters related to heating energy consumption: group layout, building orientation and building spacing; ECOTECT and HTB2 are used to model and simulate the energy consumption of the three types of layout residential groups selected for the study, and the relationship between residential group layout parameters and residential heating energy consumption is analysed based on multiple linear regression analysis. The relationship between residential group layout parameters and residential heating energy consumption was analysed based on multiple linear regression analysis. The study shows that, on the premise of ensuring the same building density, the group layout has the greatest influence on the building energy consumption, and the courtyard layout has lower heating energy consumption compared to the row and staggered layout; on this basis, the influence of building orientation and building spacing on heating energy consumption is discussed. The study shows that increasing the spacing between buildings to meet the daylight spacing will reduce energy consumption by 4% and that a 15° shift to the west will reduce energy consumption by 5%. This result confirms the importance of cluster layout in terms of heating energy consumption and urban energy efficiency in cities in cold regions. [ABSTRACT FROM AUTHOR]

[Display omitted] • The communal areas can have different levels of energy performance in the 15 types of public residential buildings under study. • Spatial design features can evidently affect the energy consumption in the communal areas. • The ability to harvest daylight serves as a key determinant of energy efficiency. • Both econometric and machine learning techniques are proved to be effective to generate reliable findings. In this paper, we construct a unique database for 1228 residential buildings in Hong Kong to investigate how the spatial features of these residential buildings affect the electricity consumption in the communal area. We choose Hong Kong for this analysis as the city owns a large number of standard-type residential buildings managed by the public institution, which could be affected strongly by environmental policies. Both the machine learning method, based on the Least Absolute Shrinkage and Selection Operator (LASSO), and econometric regressions are adopted to analyse the data. We first utilize the machine learning LASSO technique to identify the most relevant factors for the subsequent econometric analysis. Our results show that the electricity demand for relatively low consumption building types, such as Twin Tower, is 6% lower than that of the high consumption building types. Newly constructed buildings usually belong to the medium consumption types, with the estimated monthly electricity consumption per apartment in communal areas to be around 50.2 kWh on average in 2020. These findings shed light on the nexus between spatial features and energy use for complex buildings, potentially contributing to the better crafting of energy-saving policy and the improvement of residential building programmes. [ABSTRACT FROM AUTHOR]

• The combination of rough set theory and deep learning algorithms is analyzed. • Rough set theory is used to reduce the influencing factors of building energy consumption. • Deep learning is used to extract features of building energy consumption data. • An accuracy comparison of several prediction models based on rough sets and different neural networks is made. The efficient and accurate prediction of building energy consumption can improve the management of power systems. In this paper, the rough set theory was used to reduce the redundant influencing factors of building energy consumption and find the critical factors of building energy consumption. These key factors were then used as the input of a deep neural network with a "deep" architecture and powerful capabilities in extracting features. Building energy consumption is output of the deep neural network. This study collected data from 100 civil public buildings for rough set reduction, and then collected data from a laboratory building of a university in Dalian for nearly a year to train and test deep neural networks. The test included both the short-term and medium-term predictions of building energy consumption. The prediction results of the deep neural network were compared with that of the back propagation neural network, Elman neural network and fuzzy neural network. The results show that the integrated rough set and deep neural network was the most accurate. The method proposed in this study could provide a practical and accurate solution for building energy consumption prediction. [ABSTRACT FROM AUTHOR]