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Urban energy mapping plays a crucial role in benchmarking the energy performance of buildings for many stakeholders. This study examined a set of buildings in the city of Borlänge, Sweden, owned by the municipality. The aim was to present a digital spatial map of both electricity use and district heating demand in the spatialâtemporal dimension. A toolkit for top-down data processing and analysis was considered based on the energy performance database of municipality-owned buildings. The data were initially cleaned, transformed and geocoded using custom scripts and an application program interface (API) for OpenStreetMap and Google Maps. The dataset consisted of 228 and 105 geocoded addresses for, respectively, electricity and district heating monthly consumption for the year 2018. A number of extra parameters were manually incorporated to this data, i.e., the total floor area, the building year of construction and occupancy ratio. The electricity use and heating demand in the building samples were about 24.47 kWh/m2 and 268.78 kWh/m2, respectively, for which great potential for saving heating energy was observed. Compared to the electricity use, the district heating showed a more homogenous pattern following the changes of the seasons. The digital mapping revealed a spatial representation of identifiable hotspots for electricity uses in high-occupancy/density areas and for district heating needs in districts with buildings mostly constructed before 1980. These results provide a comprehensive means of understanding the existing energy distributions for stakeholders and energy advisors. They also facilitate strategy geared towards future energy planning in the city, such as energy benchmarking policies.
Samer Quintana; Pei Huang; Mengjie Han; Xingxing Zhang. A Top-Down Digital Mapping of Spatial-Temporal Energy Use for Municipality-Owned Buildings: A Case Study in Borlänge, Sweden. Buildings 2021, 11, 72 .
AMA StyleSamer Quintana, Pei Huang, Mengjie Han, Xingxing Zhang. A Top-Down Digital Mapping of Spatial-Temporal Energy Use for Municipality-Owned Buildings: A Case Study in Borlänge, Sweden. Buildings. 2021; 11 (2):72.
Chicago/Turabian StyleSamer Quintana; Pei Huang; Mengjie Han; Xingxing Zhang. 2021. "A Top-Down Digital Mapping of Spatial-Temporal Energy Use for Municipality-Owned Buildings: A Case Study in Borlänge, Sweden." Buildings 11, no. 2: 72.
In recent years, a buildingâs energy performance is becoming uncertain because of factors such as climate change, the Covid-19 pandemic, stochastic occupant behavior and inefficient building control systems. Sufficient measurement data is essential to predict and manage a buildingâs performance levels. Assessing energy performance of buildings at an urban scale requires even larger data samples in order to perform an accurate analysis at an aggregated level. However, data are not only expensive, but it can also be a real challenge for communities to acquire large amounts of real energy data. This is despite the fact that inadequate knowledge of a full population will lead to biased learning and the failure to establish a data pipeline. Thus, this paper proposes a Gaussian mixture model (GMM) with an Expectation-Maximization (EM) algorithm that will produce synthetic building energy data. This method is tested on real datasets. The results show that the parameter estimates from the model are stable and close to the true values. The bivariate model gives better performance in classification accuracy. Synthetic data points generated by the models show a consistent representation of the real data. The approach developed here can be useful for building simulations and optimizations with spatio-temporal mapping.
Mengjie Han; Zhenwu Wang; Xingxing Zhang. An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm. Buildings 2021, 11, 30 .
AMA StyleMengjie Han, Zhenwu Wang, Xingxing Zhang. An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm. Buildings. 2021; 11 (1):30.
Chicago/Turabian StyleMengjie Han; Zhenwu Wang; Xingxing Zhang. 2021. "An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm." Buildings 11, no. 1: 30.
The identification of underground geohazards is always a difficult issue in the field of underground public safety. This study proposes an interactive visualization framework for underground geohazard recognition on urban roads, which constructs a whole recognition workflow by incorporating data collection, preprocessing, modeling, rendering and analyzing. In this framework, two proposed sampling point selection methods have been adopted to enhance the interpolated accuracy for the Kriging algorithm based on ground penetrating radar (GPR) technology. An improved Kriging algorithm was put forward, which applies a particle swarm optimization (PSO) algorithm to optimize the Kriging parameters and adopts in parallel the Compute Unified Device Architecture (CUDA) to run the PSO algorithm on the GPU side in order to raise the interpolated efficiency. Furthermore, a layer-constrained triangulated irregular network algorithm was proposed to construct the 3D geohazard bodies and the space geometry method was used to compute their volume information. The study also presents an implementation system to demonstrate the application of the framework and its related algorithms. This system makes a significant contribution to the demonstration and understanding of underground geohazard recognition in a three-dimensional environment.
Zhenwu Wang; Benting Wan; Mengjie Han. A Three-Dimensional Visualization Framework for Underground Geohazard Recognition on Urban Road-Facing GPR Data. ISPRS International Journal of Geo-Information 2020, 9, 668 .
AMA StyleZhenwu Wang, Benting Wan, Mengjie Han. A Three-Dimensional Visualization Framework for Underground Geohazard Recognition on Urban Road-Facing GPR Data. ISPRS International Journal of Geo-Information. 2020; 9 (11):668.
Chicago/Turabian StyleZhenwu Wang; Benting Wan; Mengjie Han. 2020. "A Three-Dimensional Visualization Framework for Underground Geohazard Recognition on Urban Road-Facing GPR Data." ISPRS International Journal of Geo-Information 9, no. 11: 668.
Multi-label classification (MLC) is a supervised learning problem where an object is naturally associated with multiple concepts because it can be described from various dimensions. How to exploit the resulting label correlations is the key issue in MLC problems. The classifier chain (CC) is a well-known MLC approach that can learn complex coupling relationships between labels. CC suffers from two obvious drawbacks: (1) label ordering is decided at random although it usually has a strong effect on predictive performance; (2) all the labels are inserted into the chain, although some of them may carry irrelevant information that discriminates against the others. In this work, we propose a partial classifier chain method with feature selection (PCC-FS) that exploits the label correlation between label and feature spaces and thus solves the two previously mentioned problems simultaneously. In the PCC-FS algorithm, feature selection is performed by learning the covariance between feature set and label set, thus eliminating the irrelevant features that can diminish classification performance. Couplings in the label set are extracted, and the coupled labels of each label are inserted simultaneously into the chain structure to execute the training and prediction activities. The experimental results from five metrics demonstrate that, in comparison to eight state-of-the-art MLC algorithms, the proposed method is a significant improvement on existing multi-label classification.
Zhenwu Wang; Tielin Wang; Benting Wan; Mengjie Han. Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification. Entropy 2020, 22, 1143 .
AMA StyleZhenwu Wang, Tielin Wang, Benting Wan, Mengjie Han. Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification. Entropy. 2020; 22 (10):1143.
Chicago/Turabian StyleZhenwu Wang; Tielin Wang; Benting Wan; Mengjie Han. 2020. "Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification." Entropy 22, no. 10: 1143.
Occupant behavior in buildings has been considered the major source of uncertainty for assessing energy consumption and building performance. Modeling frameworks are usually built to accomplish a certain task, but the stochasticity of the occupant makes it difficult to apply that experience to a similar but distinct environment. For complex and dynamic environments, the development of smart devices and computing power makes intelligent control methods for occupant behaviors more viable. It is expected that they will make a substantial contribution to reducing global energy consumption. Among these control techniques, the reinforcement learning (RL) method seems distinctive and applicable. The success of the reinforcement learning method in many artificial intelligence applications has given an explicit indication of how this method might be used to model and adjust occupant behavior in building control. Fruitful algorithms complement each other and guarantee the quality of the optimization. However, the examination of occupant behavior based on reinforcement learning methodologies is not well established. The way that occupant interacts with the RL agent is still unclear. This study briefly reviews the empirical applications using reinforcement learning, how they have contributed to shaping the modeling paradigms and how they might suggest a future research direction.
Mengjie Han; Jing Zhao; Xingxing Zhang; Jingchun Shen; Yu Li. The reinforcement learning method for occupant behavior in building control: A review. Energy and Built Environment 2020, 2, 137 -148.
AMA StyleMengjie Han, Jing Zhao, Xingxing Zhang, Jingchun Shen, Yu Li. The reinforcement learning method for occupant behavior in building control: A review. Energy and Built Environment. 2020; 2 (2):137-148.
Chicago/Turabian StyleMengjie Han; Jing Zhao; Xingxing Zhang; Jingchun Shen; Yu Li. 2020. "The reinforcement learning method for occupant behavior in building control: A review." Energy and Built Environment 2, no. 2: 137-148.
The Predicted Mean Vote (PMV) model is extensively used by current thermal comfort standards, such as ASHRAE 55 and ISO 7730, despite its discrepancy in predicting Thermal Sensation (TS). The implicit assumption is that PMV can be applied for predicting TS of a large population. Our statistical analysis of a subset of ASHRAE global database of thermal comfort field study shows that occupantsâ expectations towards TS are affected by factors that are not accounted for in the classic PMV model, such as climate, building type, age group, season and gender. The influences of the climate and building type are more determinant. An adapted PMV (PMVa) model and an adaptation table were developed based on the selected samples to reduce this discrepancy. After adaptation, the medians of each category corresponding to the discrepancy are zero or near zero. The results also show that the adapted PMV outperforms the classic PMV in predicting TS, while increasing the overall accuracy from 36% to 39%.
Yu Li; Yacine Rezgui; Annie Guerriero; Xingxing Zhang; Mengjie Han; Sylvain Kubicki; Da Yan. Development of an adaptation table to enhance the accuracy of the predicted mean vote model. Building and Environment 2019, 168, 106504 .
AMA StyleYu Li, Yacine Rezgui, Annie Guerriero, Xingxing Zhang, Mengjie Han, Sylvain Kubicki, Da Yan. Development of an adaptation table to enhance the accuracy of the predicted mean vote model. Building and Environment. 2019; 168 ():106504.
Chicago/Turabian StyleYu Li; Yacine Rezgui; Annie Guerriero; Xingxing Zhang; Mengjie Han; Sylvain Kubicki; Da Yan. 2019. "Development of an adaptation table to enhance the accuracy of the predicted mean vote model." Building and Environment 168, no. : 106504.
Yaxiu Gu; Xingxing Zhang; Jonn Are Myhren; Mengjie Han; Xiangjie Chen; Yanping Yuan. Techno-economic analysis of a solar photovoltaic/thermal (PV/T) concentrator for building application in Sweden using Monte Carlo method. Energy Conversion and Management 2018, 165, 8 -24.
AMA StyleYaxiu Gu, Xingxing Zhang, Jonn Are Myhren, Mengjie Han, Xiangjie Chen, Yanping Yuan. Techno-economic analysis of a solar photovoltaic/thermal (PV/T) concentrator for building application in Sweden using Monte Carlo method. Energy Conversion and Management. 2018; 165 ():8-24.
Chicago/Turabian StyleYaxiu Gu; Xingxing Zhang; Jonn Are Myhren; Mengjie Han; Xiangjie Chen; Yanping Yuan. 2018. "Techno-economic analysis of a solar photovoltaic/thermal (PV/T) concentrator for building application in Sweden using Monte Carlo method." Energy Conversion and Management 165, no. : 8-24.
Song Pan; Yingzi Xiong; Yiye Han; Xingxing Zhang; Liang Xia; Shen Wei; Jinshun Wu; Mengjie Han. A study on influential factors of occupant window-opening behavior in an office building in China. Building and Environment 2018, 133, 41 -50.
AMA StyleSong Pan, Yingzi Xiong, Yiye Han, Xingxing Zhang, Liang Xia, Shen Wei, Jinshun Wu, Mengjie Han. A study on influential factors of occupant window-opening behavior in an office building in China. Building and Environment. 2018; 133 ():41-50.
Chicago/Turabian StyleSong Pan; Yingzi Xiong; Yiye Han; Xingxing Zhang; Liang Xia; Shen Wei; Jinshun Wu; Mengjie Han. 2018. "A study on influential factors of occupant window-opening behavior in an office building in China." Building and Environment 133, no. : 41-50.
Yixuan Wei; Xingxing Zhang; Yong Shi; Liang Xia; Song Pan; Jinshun Wu; Mengjie Han; Xiaoyun Zhao. A review of data-driven approaches for prediction and classification of building energy consumption. Renewable and Sustainable Energy Reviews 2018, 82, 1027 -1047.
AMA StyleYixuan Wei, Xingxing Zhang, Yong Shi, Liang Xia, Song Pan, Jinshun Wu, Mengjie Han, Xiaoyun Zhao. A review of data-driven approaches for prediction and classification of building energy consumption. Renewable and Sustainable Energy Reviews. 2018; 82 ():1027-1047.
Chicago/Turabian StyleYixuan Wei; Xingxing Zhang; Yong Shi; Liang Xia; Song Pan; Jinshun Wu; Mengjie Han; Xiaoyun Zhao. 2018. "A review of data-driven approaches for prediction and classification of building energy consumption." Renewable and Sustainable Energy Reviews 82, no. : 1027-1047.