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Energies
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Data Modeling and Analytics Applied to Buildings
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Data Modeling and Analytics Applied to Buildings

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G: Energy and Buildings".

Deadline for manuscript submissions: closed (25 October 2021) | Viewed by 15661

Special Issue Editors

Dr. Marco Pritoni

E-Mail Website
Guest Editor
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Interests: Smart buildings; building analytics; advanced building controls; data-driven modeling; human-building interaction; behavior and energy use; data science; semantic data models; smart grid
Dr. Clayton Miller

E-Mail Website
Guest Editor
Building and Urban Data Science (BUDS) Lab, Department of Building, School of Design and Environment (SDE), National University of Singapore (NUS), Singapore, Singapore
Interests: building energy prediction; machine learning; human-building interaction; data-driven modeling; behavior and energy use

Special Issue Information

The availability and accessibility of building and occupant data through smart meters, internet-connected automation systems and modern IoT sensors has significantly increased in the last decade. These data present great opportunities to scale operational energy savings and to increase grid-responsiveness of buildings. In the marketplace, progress is evident, with several software companies introducing new products allowing users to access, store, visualize, and analyze utility meter and building automation system data. However, these solutions tend to ignore recent academic progress in this area.

To enable real-world deployment of innovative modeling and analytics techniques, new research has to address several challenges including : 1) how to scale algorithms to a large set of very heterogeneous buildings;  2) how to define metadata schemas to represent buildings in a more abstract manner;  3) how to deal with the lack of consistency and completeness in the data; 4) how to integrate diverse sources of data (e.g., meters, images, wearable IoT sensing); 5) how to better present the information to users;  6) how to create and share open-data repositories to be used by the research and industry community. 

In this context, this Special Issue compiles recent research and development efforts in the area of data modeling and analytics applied to buildings. The Guest Editors welcome high-quality articles that investigate the aforementioned areas and provide new paths for further advancement in this research arena.   

Dr. Marco Pritoni
Dr. Clayton Miller
Guest 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 special issue 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. Energies is an international peer-reviewed open access semimonthly 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

  • Building Analytics
  • Energy Management & Information Systems (EMIS)
  • Smart Building
  • Fault Detection and Diagnostic (FDD)
  • Building Performance Evaluation and Benchmarking
  • Measuring and Verification (M&V)
  • Non-Intrusive Load Monitoring (NILM)
  • Occupancy and Behavioral Prediction Techniques
  • Data-Driven Modeling
  • Machine Learning and Artificial Intelligence
  • Open Data, Data Platforms and Datasets for Buildings
  • Building Digitalization Process
  • Semantic Modeling for Buildings

Published Papers (4 papers)

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Research

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12 pages, 702 KiB  
Article
The Challenge for Energy Saving in Smart Homes: Exploring the Interest for IoT Devices Acquisition in Romania
by Adrian Micu, Angela-Eliza Micu, Marius Geru, Alexandru Capatina and Mihaela-Carmen Muntean
Energies 2021, 14(22), 7589; https://doi.org/10.3390/en14227589 - 12 Nov 2021
Cited by 6 | Viewed by 1641
Abstract
The Internet of Things (IoT) is a shift towards a digitally enriched environment that connects smart objects and users, aiming to provide merchants with innovative ways to communicate with customers. Therefore, the aim of this research was to identify the Romanian consumer′s openness [...] Read more.
The Internet of Things (IoT) is a shift towards a digitally enriched environment that connects smart objects and users, aiming to provide merchants with innovative ways to communicate with customers. Therefore, the aim of this research was to identify the Romanian consumer′s openness to technological autonomy and the degree of acceptance of IoT services and technologies to address the green deal principle of low energy consumption. This article investigated the factors that influence the decision to buy smart IoT devices and customers′ perception regarding the security of the data generated in this process. Based on the Technology Acceptance Model (TAM), this research proposed an alternative model consisting of 18 items measured on a Likert scale in order to identify the factors that contribute to the perceived value of the consumer and the behavioral precursors impacting the decision to purchase IoT products. More and more products have built-in sensors and through the Internet connection generate valuable data from a managerial point of view in relation to the customer. Although these data are expected to be of great value to companies, the way they are used is not always transparent and can affect the purchasing decisions and the behavior of IoT products′ customers. The findings of this paper aimed to better promote Smart Home IoT technologies and devices among Romanian people, making possible the control of consumption and the generation of energy savings. Full article
(This article belongs to the Special Issue Data Modeling and Analytics Applied to Buildings)
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17 pages, 2984 KiB  
Article
Temporal Patternization of Power Signatures for Appliance Classification in NILM
by Hwan Kim and Sungsu Lim
Energies 2021, 14(10), 2931; https://doi.org/10.3390/en14102931 - 19 May 2021
Cited by 7 | Viewed by 2324
Abstract
Non-Intrusive Load Monitoring (NILM) techniques are effective for managing energy and for addressing imbalances between the energy demand and supply. Various studies based on deep learning have reported the classification of appliances from aggregated power signals. In this paper, we propose a novel [...] Read more.
Non-Intrusive Load Monitoring (NILM) techniques are effective for managing energy and for addressing imbalances between the energy demand and supply. Various studies based on deep learning have reported the classification of appliances from aggregated power signals. In this paper, we propose a novel approach called a temporal bar graph, which patternizes the operational status of the appliances and time in order to extract the inherent features from the aggregated power signals for efficient load identification. To verify the effectiveness of the proposed method, a temporal bar graph was applied to the total power and tested on three state-of-the-art deep learning techniques that previously exhibited superior performance in image classification tasks—namely, Extreme Inception (Xception), Very Deep One Dimensional CNN (VDOCNN), and Concatenate-DenseNet121. The UK Domestic Appliance-Level Electricity (UK-DALE) and Tracebase datasets were used for our experiments. The results of the five-appliance case demonstrated that the accuracy and F1-score increased by 19.55% and 21.43%, respectively, on VDOCNN, and by 33.22% and 35.71%, respectively, on Xception. A performance comparison with the state-of-the-art deep learning methods and image-based spectrogram approach was conducted. Full article
(This article belongs to the Special Issue Data Modeling and Analytics Applied to Buildings)
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22 pages, 1101 KiB  
Article
Uncertainty Matters: Bayesian Probabilistic Forecasting for Residential Smart Meter Prediction, Segmentation, and Behavioral Measurement and Verification
by Jonathan Roth, Jayashree Chadalawada, Rishee K. Jain and Clayton Miller
Energies 2021, 14(5), 1481; https://doi.org/10.3390/en14051481 - 08 Mar 2021
Cited by 10 | Viewed by 2481
Abstract
As new grid edge technologies emerge—such as rooftop solar panels, battery storage, and controllable water heaters—quantifying the uncertainties of building load forecasts is becoming more critical. The recent adoption of smart meter infrastructures provided new granular data streams, largely unavailable just ten years [...] Read more.
As new grid edge technologies emerge—such as rooftop solar panels, battery storage, and controllable water heaters—quantifying the uncertainties of building load forecasts is becoming more critical. The recent adoption of smart meter infrastructures provided new granular data streams, largely unavailable just ten years ago, that can be utilized to better forecast building-level demand. This paper uses Bayesian Structural Time Series for probabilistic load forecasting at the residential building level to capture uncertainties in forecasting. We use sub-hourly electrical submeter data from 120 residential apartments in Singapore that were part of a behavioral intervention study. The proposed model addresses several fundamental limitations through its flexibility to handle univariate and multivariate scenarios, perform feature selection, and include either static or dynamic effects, as well as its inherent applicability for measurement and verification. We highlight the benefits of this process in three main application areas: (1) Probabilistic Load Forecasting for Apartment-Level Hourly Loads; (2) Submeter Load Forecasting and Segmentation; (3) Measurement and Verification for Behavioral Demand Response. Results show the model achieves a similar performance to ARIMA, another popular time series model, when predicting individual apartment loads, and superior performance when predicting aggregate loads. Furthermore, we show that the model robustly captures uncertainties in the forecasts while providing interpretable results, indicating the importance of, for example, temperature data in its predictions. Finally, our estimates for a behavioral demand response program indicate that it achieved energy savings; however, the confidence interval provided by the probabilistic model is wide. Overall, this probabilistic forecasting model accurately measures uncertainties in forecasts and provides interpretable results that can support building managers and policymakers with the goal of reducing energy use. Full article
(This article belongs to the Special Issue Data Modeling and Analytics Applied to Buildings)
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Review

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37 pages, 2585 KiB  
Review
Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis
by Marco Pritoni, Drew Paine, Gabriel Fierro, Cory Mosiman, Michael Poplawski, Avijit Saha, Joel Bender and Jessica Granderson
Energies 2021, 14(7), 2024; https://doi.org/10.3390/en14072024 - 06 Apr 2021
Cited by 44 | Viewed by 8440
Abstract
Digital and intelligent buildings are critical to realizing efficient building energy operations and a smart grid. With the increasing digitalization of processes throughout the life cycle of buildings, data exchanged between stakeholders and between building systems have grown significantly. However, a lack of [...] Read more.
Digital and intelligent buildings are critical to realizing efficient building energy operations and a smart grid. With the increasing digitalization of processes throughout the life cycle of buildings, data exchanged between stakeholders and between building systems have grown significantly. However, a lack of semantic interoperability between data in different systems is still prevalent and hinders the development of energy-oriented applications that can be reused across buildings, limiting the scalability of innovative solutions. Addressing this challenge, our review paper systematically reviews metadata schemas and ontologies that are at the foundation of semantic interoperability necessary to move toward improved building energy operations. The review finds 40 schemas that span different phases of the building life cycle, most of which cover commercial building operations and, in particular, control and monitoring systems. The paper’s deeper review and analysis of five popular schemas identify several gaps in their ability to fully facilitate the work of a building modeler attempting to support three use cases: energy audits, automated fault detection and diagnosis, and optimal control. Our findings demonstrate that building modelers focused on energy use cases will find it difficult, labor intensive, and costly to create, sustain, and use semantic models with existing ontologies. This underscores the significant work still to be done to enable interoperable, usable, and maintainable building models. We make three recommendations for future work by the building modeling and energy communities: a centralized repository with a search engine for relevant schemas, the development of more use cases, and better harmonization and standardization of schemas in collaboration with industry to facilitate their adoption by stakeholders addressing varied energy-focused use cases. Full article
(This article belongs to the Special Issue Data Modeling and Analytics Applied to Buildings)
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