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Data Analytics for Large-Scale Building Energy Modelling and Optimization
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Data Analytics for Large-Scale Building Energy Modelling and Optimization

A topical collection in Buildings (ISSN 2075-5309). This collection belongs to the section "Building Energy, Physics, Environment, and Systems".

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Editors

Dr. Mengjie Han

E-Mail Website
Collection Editor
Microdata Analysis, Dalarna University, 79188 Falun, Sweden
Interests: machine learning; statistical modeling; urban energy; building control
Special Issues, Collections and Topics in MDPI journals
Dr. Pei Huang

E-Mail Website
Collection Editor
Energy Technology, Dalarna University, 79188 Falun, Sweden
Interests: low carbon buildings design and control optimization; smart buildings; energy-sharing communities; building energy systems; electric vehicles; decision making under uncertainty; Bayesian theory
Prof. Dr. Xingxing Zhang

E-Mail Website
Collection Editor
School of Industrial Technology and Business Studies, Dalarna University, Falun Borlänge, Sweden
Interests: positive energy district; solar energy; urban energy system
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

Building energy modeling is transforming from the individual building scale to large-scale district- or urban-level modeling. Traditional modeling methods need to be updated and extended in order to capture interactions and macro-level patterns. Due to the variations in city size, urban configurations, and weather conditions, energy modeling has to be customized according to local context.

Data analytics—a process of modelling, summarizing, and interpreting quantitative outcomes—has been considered one of the viable approaches to generate synthetic data, predict energy demand, provide global control signals, extract spatio-temporal interactions, and test automation algorithms. Along with the evolution of sensor techniques and digitalization, future data analytics should comprise collaborating fields for building energy modeling such as artificial intelligence, decision-support systems, resource allocation, experimental design, geographical information systems, and statistical inference. In the future, an integrated platform of data acquisition, analytics, and management for multiple building is envisioned to help stakeholders monitor the digitized information.

There will be many uncertain and challenging factors in the future, such as climate change and population growth, that affect the modeling methods. Advanced methods handling complex data are also expected to address these challenges. With the increase of modelled building scales, new collaborative controls among energy systems are also becoming popular for performance improvements at the community, district, or urban level. Due to the complexity of computation, advanced data analytic techniques and intelligent algorithms are often needed to solve the optimization problems. Topics addressing controls at these levels are also welcome.

The aim of this Topical Collection is to give an opportunity to scientists investigating building energy modelling and optimization at a large scale to publish their works and make large-scale building energy modelling more accurate and feasible.

Dr. Mengjie Han
Dr. Pei Huang
Prof. Dr. Xingxing Zhang
Collection Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data analytics
  • large-scale buildings
  • energy modeling
  • artificial intelligence
  • statistical inference
  • machine learning
  • future challenge
  • uncertainty
  • IoT (Internet of Things)
  • geographical information systems

Published Papers (10 papers)

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2024

Jump to: 2023, 2022, 2021

28 pages, 3935 KiB  
Review
Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts
by Mengjie Han, Ilkim Canli, Juveria Shah, Xingxing Zhang, Ipek Gursel Dino and Sinan Kalkan
Buildings 2024, 14(2), 371; https://doi.org/10.3390/buildings14020371 - 30 Jan 2024
Viewed by 1295
Abstract
The concept of a Positive Energy District (PED) has become a vital component of the efforts to accelerate the transition to zero carbon emissions and climate-neutral living environments. Research is shifting its focus from energy-efficient single buildings to districts, where the aim is [...] Read more.
The concept of a Positive Energy District (PED) has become a vital component of the efforts to accelerate the transition to zero carbon emissions and climate-neutral living environments. Research is shifting its focus from energy-efficient single buildings to districts, where the aim is to achieve a positive energy balance across a given time period. Various innovation projects, programs, and activities have produced abundant insights into how to implement and operate PEDs. However, there is still no agreed way of determining what constitutes a PED for the purpose of identifying and evaluating its various elements. This paper thus sets out to create a process for characterizing PEDs. First, nineteen different elements of a PED were identified. Then, two AI techniques, machine learning (ML) and natural language processing (NLP), were introduced and examined to determine their potential for modeling, extracting, and mapping the elements of a PED. Lastly, state-of-the-art research papers were reviewed to identify any contribution they can make to the determination of the effectiveness of the ML and NLP models. The results suggest that both ML and NLP possess significant potential for modeling most of the identified elements in various areas, such as optimization, control, design, and stakeholder mapping. This potential is realized through the utilization of vast amounts of data, enabling these models to generate accurate and useful insights for PED planning and implementation. Several practical strategies have been identified to enhance the characterization of PEDs. These include a clear definition and quantification of the elements, the utilization of urban-scale energy modeling techniques, and the development of user-friendly interfaces capable of presenting model insights in an accessible manner. Thus, developing a holistic approach that integrates existing and novel techniques for PED characterization is essential to achieve sustainable and resilient urban environments. Full article
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2023

Jump to: 2024, 2022, 2021

22 pages, 3031 KiB  
Article
The Challenge of Multiple Thermal Comfort Prediction Models: Is TSV Enough?
by Betty Lala, Amogh Biju, Vanshita, Anmol Rastogi, Kunal Dahiya, Srikant Manas Kala and Aya Hagishima
Buildings 2023, 13(4), 890; https://doi.org/10.3390/buildings13040890 - 28 Mar 2023
Cited by 2 | Viewed by 1591
Abstract
Classroom thermal comfort has a direct effect on student health and educational outcomes. However, measuring thermal comfort (TC) is a non-trivial task. It is represented by several subjective metrics e.g., Thermal Sensation Vote, Thermal Comfort Vote, Thermal Preference Vote, etc. Since machine learning [...] Read more.
Classroom thermal comfort has a direct effect on student health and educational outcomes. However, measuring thermal comfort (TC) is a non-trivial task. It is represented by several subjective metrics e.g., Thermal Sensation Vote, Thermal Comfort Vote, Thermal Preference Vote, etc. Since machine learning (ML) is being increasingly used to predict occupant comfort, multiple TC metrics for the same indoor space may yield contradictory results. This poses the challenge of selecting the most suitable single TC metric or the minimal TC metric combination for a given indoor space. Ideally, it will be a metric that can be used to predict all other TC metrics and occupant behavior with high accuracy. This work addresses this problem by using a primary student thermal comfort dataset gathered from 11 schools and over 500 unique students. A comprehensive evaluation is carried out through hundreds of TC prediction models using several ML algorithms. It evaluates the ability of TC metrics to predict (a) other TC metrics, and (b) the adaptive behavior of primary students. An algorithm is proposed to select the most suitable single TC metric or the minimal TC metric input combination. Results show that ML models can accurately predict all TC metrics and occupant-adaptive behavior using a small subset of TC metrics with an average accuracy as high as 79%. This work also found Thermal Sensation Vote to be the most significant single TC predictor, followed by Thermal Satisfaction Level. Interestingly, satisfaction with clothing was found to be as equally relevant as thermal preference. Furthermore, the impact of seasons and choice of ML algorithms on TC metric and occupant behavior prediction is shown. Full article
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17 pages, 6620 KiB  
Article
Predicting Diverse Behaviors of Occupants When Turning Air Conditioners on/off in Residential Buildings: An Extreme Gradient Boosting Approach
by Jiajun Lyu and Aya Hagishima
Buildings 2023, 13(2), 521; https://doi.org/10.3390/buildings13020521 - 14 Feb 2023
Viewed by 1093
Abstract
Occupant behavior (OB) has a significant impact on household air-conditioner (AC) energy use. In recent years, bottom-up simulation coupled with stochastic OB modeling has been intensively developed for estimating residential AC consumption. However, a comprehensive analysis of the diverse behavioral preference patterns of [...] Read more.
Occupant behavior (OB) has a significant impact on household air-conditioner (AC) energy use. In recent years, bottom-up simulation coupled with stochastic OB modeling has been intensively developed for estimating residential AC consumption. However, a comprehensive analysis of the diverse behavioral preference patterns of occupants regarding AC use is hampered by the limited availability of large-scale residential energy demand data. Therefore, this study aimed to develop a prediction model for the residential household’s AC usage considering various OB-related diversity patterns based on monitoring data of appliance-level electricity use in a residential community of 586 households in Osaka, Japan. First, individual operation schedules and thermal preferences were identified and quantitatively extracted as the two main factors for the diverse behaviors across the whole community. Then, a clustering analysis classified the target households, finding four typical patterns for schedule preferences and three typical patterns for thermal preferences. These results were used, with time and meteorological data in the summer seasons of 2013 and 2014, as inputs for the proposed prediction model using Extreme Gradient Boosting (XGBoost). The optimized XGBoost model showed a satisfactory prediction performance for the on/off state in the testing dataset, with an F1 score of 0.80 and an Area under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.845. Full article
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2022

Jump to: 2024, 2023, 2021

27 pages, 3777 KiB  
Article
A Review of Thermal Comfort in Primary Schools and Future Challenges in Machine Learning Based Prediction for Children
by Betty Lala and Aya Hagishima
Buildings 2022, 12(11), 2007; https://doi.org/10.3390/buildings12112007 - 17 Nov 2022
Cited by 9 | Viewed by 4941
Abstract
Children differ from adults in their physiology and cognitive ability. Thus, they are extremely vulnerable to classroom thermal comfort. However, very few reviews on the thermal comfort of primary school students are available. Further, children-focused surveys have not reviewed the state-of-the-art in thermal [...] Read more.
Children differ from adults in their physiology and cognitive ability. Thus, they are extremely vulnerable to classroom thermal comfort. However, very few reviews on the thermal comfort of primary school students are available. Further, children-focused surveys have not reviewed the state-of-the-art in thermal comfort prediction using machine learning (AI/ML). Consequently, there is a need for discussion on children-specific challenges in AI/ML-based prediction. This article bridges these research gaps. It presents a comprehensive review of thermal comfort studies in primary school classrooms since 1962. It considers both conventional (non-ML) studies and the recent AI/ML studies performed for children, classrooms, and primary students. It also underscores the importance of AI/ML prediction by analyzing adaptive opportunities for children/students in classrooms. Thereafter, a review of AI/ML-based prediction studies is presented. Through an AI/ML case-study, it demonstrates that model performance for children and adults differs markedly. Performance of classification models trained on ASHRAE-II database and a recent primary students’ dataset shows a 29% difference in thermal sensation and 86% difference in thermal preference, between adults and children. It then highlights three major children-specific AI/ML challenges, viz., “illogical votes”, “multiple comfort metrics”, and “extreme class imbalance”. Finally, it offers several technical solutions and discusses open problems. Full article
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17 pages, 3745 KiB  
Article
Non-Intrusive Load Disaggregation Based on a Feature Reused Long Short-Term Memory Multiple Output Network
by Yifan Fang, Shanshan Jiang, Shengxuan Fang, Zhenxi Gong, Min Xia and Xiaodong Zhang
Buildings 2022, 12(7), 1048; https://doi.org/10.3390/buildings12071048 - 19 Jul 2022
Cited by 7 | Viewed by 1304
Abstract
Load decomposition technology is an important aspect of power intelligence. At present, there are mainly machine learning methods based on artificial features and deep learning methods for load decomposition. The method based on artificial features has a difficult time obtaining effective load features, [...] Read more.
Load decomposition technology is an important aspect of power intelligence. At present, there are mainly machine learning methods based on artificial features and deep learning methods for load decomposition. The method based on artificial features has a difficult time obtaining effective load features, leading to low accuracy. The method based on deep learning can automatically extract load characteristics, which improves the accuracy of load decomposition. However, with the deepening of the model structure, the number of parameters becomes too large, the training speed is slow, and the computing cost is high, which leads to the reduction of redundant features and the learning ability in some shallow networks, and the traditional deep learning model has a difficult time obtaining effective features on the time scale. To address these problems, a feature reused long short-term memory multiple output network (M-LSTM) is proposed and used for non-invasive load decomposition tasks. The network proposes an improved multiscale fusion residual module to extract basic load features and proposes the use of LSTM cyclic units to extract time series information. Feature reuse is achieved by combining it with the reorganization of the input data into multiple branches. The proposed structure reduces the difficulty of network optimization, and multi-scale fusion can obtain features on multiple time scales, which improves the ability of model feature extraction. Compared with common network models that tend to train network models for a single target load, the structure can simultaneously decompose the target load power while ensuring the accuracy of load decomposition, thus reducing computational costs, avoiding repetitive model training, and improving training efficiency. Full article
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26 pages, 6828 KiB  
Article
Multi-Task Learning for Concurrent Prediction of Thermal Comfort, Sensation and Preference in Winters
by Betty Lala, Hamada Rizk, Srikant Manas Kala and Aya Hagishima
Buildings 2022, 12(6), 750; https://doi.org/10.3390/buildings12060750 - 31 May 2022
Cited by 12 | Viewed by 2253
Abstract
Indoor thermal comfort immensely impacts the health and performance of occupants. Therefore, researchers and engineers have proposed numerous computational models to estimate thermal comfort (TC). Given the impetus toward energy efficiency, the current focus is on data-driven TC prediction solutions that leverage state-of-the-art [...] Read more.
Indoor thermal comfort immensely impacts the health and performance of occupants. Therefore, researchers and engineers have proposed numerous computational models to estimate thermal comfort (TC). Given the impetus toward energy efficiency, the current focus is on data-driven TC prediction solutions that leverage state-of-the-art machine learning (ML) algorithms. However, an occupant’s perception of indoor thermal comfort (TC) is subjective and multi-dimensional. Different aspects of TC are represented by various standard metrics/scales viz., thermal sensation (TSV), thermal comfort (TCV), and thermal preference (TPV). The current ML-based TC prediction solutions adopt the Single-task Learning approach, i.e., one prediction model per metric. Consequently, solutions often focus on only one TC metric. Moreover, when several metrics are considered, multiple ML models for a single indoor space lead to conflicting predictions, rendering real-world deployment infeasible. This work addresses these problems by leveraging Multi-task Learning for TC prediction in naturally ventilated buildings. First, a survey-and-measurement study is conducted in the composite climatic region of north India, in 14 naturally ventilated classrooms of 5 schools, involving 512 primary school students. Next, the dataset is analyzed for important environmental, physiological, and psycho-social factors that influence thermal comfort of children. Further, “DeepComfort”, a deep neural network based Multi-task Learning model is proposed. DeepComfort predicts multiple TC output metrics viz., TSV, TPV, and TCV, simultaneously through a single model. It is validated on ASHRAE-II database and the primary student dataset created in this study. It demonstrates high F1-scores, Accuracy (≈90%), and generalization capability, despite the challenges of illogical responses and data imbalance. DeepComfort is also shown to outperform 6 popular metric-specific single-task machine learning algorithms. Full article
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16 pages, 4051 KiB  
Article
Numerical Study of Micro-Thermal Environment in Block Based on Porous Media Model
by Jie Lei, Dengyun Wang and Zhenqian Chen
Buildings 2022, 12(5), 595; https://doi.org/10.3390/buildings12050595 - 04 May 2022
Cited by 1 | Viewed by 1088
Abstract
The mitigation of the heat island effect has become one of the most challenging issues with the rapid urbanization and increased human activities. A standard model and a porous media model were developed to simulate the microthermal environment in the presence of anthropogenic [...] Read more.
The mitigation of the heat island effect has become one of the most challenging issues with the rapid urbanization and increased human activities. A standard model and a porous media model were developed to simulate the microthermal environment in the presence of anthropogenic heat in Nanjing Xinjiekou block. The accuracy of the simulation results was verified by field measurement data. Compared with the standard CFD method, the porous media method reduces the number of meshes by 27.8% and the total computation time by 66.7%. By comparing and analyzing the thermal environment of the block with various porosities and heat intensities at different heights, calculations proved that the velocity is positively correlated with the porosity change, and the temperature is negatively correlated with it in contrast. The temperature increases linearly with the increase in anthropogenic heat intensity under the block height range, and the gradient is about 0.025 K/W at the height of 2 m. The porous media approach allows for effective prediction of the micro-thermal environment in the initial stages of urban design while increasing the porosity of the block and controlling the intensity of anthropogenic heat emissions can be a prominent means of improving the thermal environment. Full article
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2021

Jump to: 2024, 2023, 2022

21 pages, 5694 KiB  
Article
Data Reconstruction of Wireless Sensor Network and Zonal Demand Control in a Large-Scale Indoor Space Considering Thermal Coupling
by Pei Zhou, Songjie Wang, Zhao Jin, Gongsheng Huang, Jian Zhu and Xiaoping Liu
Buildings 2022, 12(1), 15; https://doi.org/10.3390/buildings12010015 - 27 Dec 2021
Cited by 2 | Viewed by 2504
Abstract
An indoor high and open space is characterized by high mobility of people and uneven temperature distribution, so the conventional design and operation of air conditioning systems makes it difficult to regulate the air conditioning system precisely and efficiently. Thus, a Wireless Sensor [...] Read more.
An indoor high and open space is characterized by high mobility of people and uneven temperature distribution, so the conventional design and operation of air conditioning systems makes it difficult to regulate the air conditioning system precisely and efficiently. Thus, a Wireless Sensor Network was constructed in an indoor space located in Hong Kong to monitor the indoor environmental parameters of the space and improve the temperature control effectively. To ensure the continuity of the measurement data, three algorithms for reconstructing temperature, relative humidity and carbon dioxide data were implemented and compared. The results demonstrate the accuracy of support vector regression model and multiple linear regression model is higher than Back Propagation neural network model for reconstructing temperature data. Multiple linear regression is the most convenient from the perspective of program complexity, computing speed and difficulty in obtaining input conditions. Based on the data we collected, the traditional single-input-single-output control, zonal temperature control and the proposed zonal demand control methods were modeled on a Transient System Simulation Program (TRNSYS) control platform, the thermal coupling between the subzones without physical partition was taken into account, and the mass transfer between the virtual boundaries was calculated by an external CONTAM program. The simulation results showed the proposed zonal demand control can alleviate the over-cooling or over-heating phenomenon in conventional temperature control, thermal comfort and energy reduction is enhanced as well. Full article
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11 pages, 3108 KiB  
Article
Comparative Evaluation of Predicting Energy Consumption of Absorption Heat Pump with Multilayer Shallow Neural Network Training Algorithms
by Jee-Heon Kim, Nam-Chul Seong and Won-Chang Choi
Buildings 2022, 12(1), 13; https://doi.org/10.3390/buildings12010013 - 26 Dec 2021
Cited by 3 | Viewed by 2262
Abstract
The performance of various multilayer neural network algorithms to predict the energy consumption of an absorption chiller in an air conditioning system under the same conditions was compared and evaluated in this study. Each prediction model was created using 12 representative multilayer shallow [...] Read more.
The performance of various multilayer neural network algorithms to predict the energy consumption of an absorption chiller in an air conditioning system under the same conditions was compared and evaluated in this study. Each prediction model was created using 12 representative multilayer shallow neural network algorithms. As training data, about a month of actual operation data during the heating period was used, and the predictive performance of 12 algorithms according to the training size was evaluated. The prediction results indicate that the error rates using the measured values are 0.09% minimum, 5.76% maximum, and 1.94 standard deviation (SD) for the Levenberg–Marquardt backpropagation model and 0.41% minimum, 5.05% maximum, and 1.68 SD for the Bayesian regularization backpropagation model. The conjugate gradient with Polak–Ribiére updates backpropagation model yielded lower values than the other two models, with 0.31% minimum, 5.73% maximum, and 1.76 SD. Based on the results for the predictive performance evaluation index, CvRMSE, all other models (conjugate gradient with Fletcher–Reeves updates backpropagation, one-step secant backpropagation, gradient descent with momentum and adaptive learning rate backpropagation, gradient descent with momentum backpropagation) except for the gradient descent backpropagation model yielded results that satisfy ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) Guideline 14. The results of this study confirm that the prediction performance may differ for each multilayer neural network training algorithm. Therefore, selecting the appropriate model to fit the characteristics of a specific project is essential. Full article
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18 pages, 3358 KiB  
Article
A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest
by Zaixun Ling, Qian Tao, Jingwen Zheng, Ping Xiong, Manjia Liu, Ziwei Xiao and Wenjie Gang
Buildings 2021, 11(10), 449; https://doi.org/10.3390/buildings11100449 - 30 Sep 2021
Cited by 5 | Viewed by 1850
Abstract
Load monitoring can help users learn end-use energy consumption so that specific energy-saving actions can be taken to reduce the energy consumption of buildings. Nonintrusive monitoring (NIM) is preferred because of its low cost and nondisturbance of occupied space. In this study, a [...] Read more.
Load monitoring can help users learn end-use energy consumption so that specific energy-saving actions can be taken to reduce the energy consumption of buildings. Nonintrusive monitoring (NIM) is preferred because of its low cost and nondisturbance of occupied space. In this study, a NIM method based on random forest was proposed to determine the energy consumption of building subsystems from the building-level energy consumption: the heating, ventilation and air conditioning system; lighting system; plug-in system; and elevator system. Three feature selection methods were used and compared to achieve accurate NIM based on weather parameters, wavelet analysis, and principal component analysis. The implementation of the proposed method in an office building showed that it can obtain the subloads accurately, with root-mean-square errors of less than 46.4 kW and mean relative errors of less than 12.7%. The method based on weather parameters can provide the most accurate results. The proposed method can help improve the energy efficiency of building service systems during the operation or renovation stage. Full article
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