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[Display omitted] • A split-degree days index is proposed for building energy use/climate classification. • Split-degree days index is shown to better estimate solar radiation and humidity data. • The estimation of building energy end-uses is improved using the split-degree days. • Split-degree days also showed improved results for high thermal mass and 24-hr models. • Split-degree days index maintains a high accuracy for varying base temperatures. Degree days have been widely used in different applications in buildings, such as estimating building energy use and climate classification for building energy standards. However, there are limitations with the use of conventional degree days that result in inaccuracies in estimating building energy consumption using degree day-based methods. This paper proposes a new method, the split-degree day method, that shows substantially improved results in the accuracy of the building energy use estimation compared to the conventional degree day methods. The analysis in 801 locations in the U.S. using regression models showed that the new, proposed split-degree day method in this paper, compared to the conventional degree day method, better accounts for the weather parameters and more accurately estimates end-uses. The split-degree days showed an improvement of over 5% in the accuracy of the total annual energy use prediction, 8% for predicting the heating energy use, 0.3% for predicting the cooling energy use, and 33% for predicting the fan energy use. Additionally, the analysis showed improved results for the model with higher thermal mass and the model with a 24-hour operating schedule. These improvements were due to the fact that the split-degree day method includes more information related to the weather characteristics of a location, which is missing in a conventional degree day calculation procedure that relies on aggregated data at daily intervals. Furthermore, as opposed to the conventional degree days, the accuracy of the estimations using the split-degree days are shown to maintain their high accuracy when the base temperature deviates from the optimal base temperature. The proposed method can be used as a more accurate alternative to the conventional degree days in different applications, including but not limited to building climate classification, building energy estimation, and weather normalization for building energy savings calculation. [ABSTRACT FROM AUTHOR]

Facing the challage of energy conservation and emission reduction in the building sector, most energy saving strategies of central heating only focus on the performance improvement of building envelope, ignoring the impact of energy use behavior. This paper proposed a building energy conservation strategy based on energy use behavior analysis, which provides a perspective for in-depth exploration of building energy conservation. After investigating a typical university in the northern China, the energy use behaviors were characterized and two different DeST energy consumption models before or after considering energy use behavior were established. By comparing the results calculated by these two models, the impact of energy use behavior analysis on building energy consumption was quantitatively analyzed. The results show that the heating energy consumption and carbon emission can be reduced by 23.28% and 23% respectively through the proposed strategy based on energy use behavior. Therefore, the analysis of energy use behavior can provide a theoretical basis for heating operators to make decisions on energy supply regulation. With central cooling and heating further popularizing in the future, the research methodology of this paper has more extensive reference value for energy conservation and emission reduction strategies in the building sector. [ABSTRACT FROM AUTHOR]

• Obtaining the optimal interpolation method for adjacent sites. • Proposing the method for revising meteorological data at city scale. • Verifying the accuracy of the method by energy consumption simulation. In the development process of building energy conservation design, the correct provision of local Typical Meteorological Year (TMY) data is a prerequisite and basic work to evaluate building energy consumption, which directly affects the fine use of outdoor data for building energy conservation. Based on the recorded and observed data of meteorological stations in China, the acquisition and correction methods of TMY were studied by mathematical statistics and target searching, and the building energy consumption simulation of urban office buildings was conducted to verify the accuracy of the method, including:(1) Reviewed the generation method for TMY and the acquisition of data.(2) Based on the existing TMY data, the best spatial interpolation method of adjacent stations is studied by using the standard deviation method, and the selection method of the best number of stations on a large regional scale is given.(3) An improved method of urban environment TMY is proposed. The dry bulb temperature and relative humidity of the building site are corrected to generate TMY under Urban Heat Island (UHI) effect. The research in this paper improves the coverage and accuracy of urban TMY data and supplies basic research support for building energy-saving designs. [ABSTRACT FROM AUTHOR]

Building energy consumption forecasting is crucial for energy managers and policymakers. However, acutely predicting the energy consumption of any building is more important in energy efficiency benchmarking of buildings. Energy benchmarking is a precise technique for measuring, tracking, and reducing end-use energy usage of buildings by employing comparative scenarios. Bottom-Up energy consumption assessment has been used in several studies to evaluate a collection of buildings' energy performance to a standard. Traditionally, researchers have used a single approach to predict the energy use of buildings and to conduct benchmarking. However, it has been found that doing so frequently compromises the accuracy of the predicted energy use of buildings. It also has an adverse effect on the buildings' energy benchmarking framework results. This paper will provide an energy benchmarking framework for residential buildings based on the blending techniques of multiple approaches. Over 3000 datasets were gathered from the Indian city of Jaipur. In order to analyse the trends in energy consumption, the data were divided into categories based on economic levels. The data is then subjected to some fundamental statistical analysis, along with several approaches are blended to forecast the energy consumption of the buildings with the highest accuracy. These benchmark levels set guidelines for assessing and recognising strong performance while also identifying underperforming buildings and giving them more priority for energy efficiency improvement. The technique may be used for different metropolitan levels and climatic conditions. [ABSTRACT FROM AUTHOR]

• This paper determines the relationship between urban form and building energy use. • The surface-to-volume ratio and obstruction angle of buildings are important. • Consideration of the heating energy type and building use can yield reliable result. • The urban heat island effect exerts an impact on the electricity use in some months. • Correlation variation is caused by the building permit date. In the climate change era, urban geometry serves to ensure sustainability for urban planners since it notably affects building energy consumption. However, previous studies have mainly conducted experimental investigations under hypothetical conditions. Without empirical evidence, it is difficult to implement these findings under real conditions. This paper examined empirical data to determine the urban geometry effect on building energy consumption in a large, complicated urban area. Urban geometric parameters, including the surface-to-volume ratio, obstruction angle, and building orientation, were measured across Seoul, and their correlations with building energy use were analyzed. The results show that the surface-to-volume ratio and obstruction angle are important, despite the empirical data complexity. Additional data analysis indicates that the heating energy type and building use consideration yields more reliable correlation coefficient trends. The potential impacts of natural ventilation and the urban heat island (UHI) effect on building energy consumption were examined. The UHI effect, rather than shadows on buildings, predominantly impacts the building electricity use in August and September. The correlation variation caused by the building permit date was investigated. The results in this paper could help urban planners and designers develop blueprints to control urban geometry and establish relevant design guidelines. [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]

• Miscellaneous loads are a major opportunity to save electricity in vacant buildings. • A method for inferring vacancy was developed. • The model generates a continuously updated likelihood of vacancy. • The vacancy inference model requires less ground truth data. • The sensor fusion approach is robust and enables flexibility. Recent events have forced building managers to examine energy use during vacant periods and revealed miscellaneous electrical loads (MELs) as an opportunity for savings. This paper addresses a key step in unlocking these savings, specifically the reliable identification of when a building is vacant. A Vacancy Inference Engine (VIE), using sensor fusion, was developed to identify vacant periods based on outputs from common sensors, historical building vacancy patterns, and expert knowledge. The VIE calculates the confidence that a building is vacant, allowing building managers to balance the capture of energy savings with the possibility of complaints due to powering down MELs. The VIE has the advantage over logistic regression and other models in that it does not require a full set of ground truth for the training process. The VIE successfully predicted vacancy in an office building using input data streams of instantaneous electricity demand, indoor carbon dioxide concentrations, and the number of active Wi-Fi connections. The VIE's ability to predict vacancy was compared to that of logistic regression using a metric based on the Complaint Opportunity Rate and found to be nearly identical (0.94 versus 0.95, respectively). [ABSTRACT FROM AUTHOR]

China is currently experiencing rapid urbanization. The building sector plays an important role in China's energy conservation and emission reduction. An increasing amount of energy will be steadily consumed in the building sector as the urbanization process accelerates. China's government attaches great importance to building energy efficiency (BEE). How does the urbanization process affect building energy consumption (BEC)? How much energy can be saved in the building sector through implementation of energy polices? These questions are critical for government development strategies and planning. Thus, in this paper, an econometric model (co-integration technique) is developed to analyze how China's BEC is affected by macroeconomic variables in the context of urbanization, and also forecast the BEC and BEE potential until 2020 under different scenarios. The research result shows that occupants’ lifestyles, residents’ living standards, and energy price have significant influences on BEC. The building sector is likely to contribute approximately 20% to China's energy conservation through electricity pricing reform. [ABSTRACT FROM AUTHOR]

Abstract: The current energy distribution infrastructure in many urban areas either cannot support anticipated future energy use or would require significant rehabilitation even if current use were maintained. Understanding the dynamics of local energy use is an important precondition of understanding how to remedy this situation. This paper builds a model to estimate the building sector energy end-use intensity (kwh/m2 floor area) for space heating, domestic hot water, electricity for space cooling and electricity for non-space cooling applications in New York City. The model assumes that such end use is primarily dependent on building function, whether residential, educational or office for example, and not on construction type or the age of the building. The modeled intensities are calibrated using ZIP code level electricity and fuel use data reported by the New York City Mayor''s Office of Long-Term Planning and Sustainability. The end-use ratios were derived from the Residential and Commercial Building Energy Consumption Survey''s Public Use Microdata. The results provide the ability to estimate the end-use energy consumption of each tax lot in New York City. The resulting spatially explicit energy consumption can be a valuable tool for determining cost-effectiveness and policies for implementing energy efficiency and renewable energy programs. [Copyright &y& Elsevier]

• Optimizing the integration of renewable energy in existing buildings. • Minimizing required cost to satisfy specified reduction in building energy consumption. • Optimal compliance with EISA2007 energy requirements for federal buildings. • Novel methodology for efficient segmentation of complex buildings. • Complying with building functional requirements and constructability constraints. This paper presents the development of a novel optimization model for optimizing the use of RE measures in existing buildings to satisfy an owner-specified target reduction in energy consumption while minimizing the required building upgrade costs. The model performance was evaluated using a real-life case study of an educational building with a total area of 20,717 m2. The optimization model was used to identify an optimal combination of RE technologies that minimize the upgrade cost for this case study while satisfying its specified 80% energy reduction goal. The model minimized the building upgrade cost by making a number of optimal selections, such as locating the majority of solar RE technologies in building segments facing south due to their higher exposure to the sun radiations. The results of this analysis highlight the original contributions of the model in identifying an optimal set of RE measures that minimize the building upgrade cost while satisfying all owner-specified requirements, providing a detailed description of the optimal building integration plan of RE technologies, and complying with practical functional requirements, and constructability constraints. These capabilities are expected to support building owners and decision-makers in their efforts to integrate cost-effective RE measures in existing buildings. [ABSTRACT FROM AUTHOR]