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Artificial Intelligence for Buildings
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Artificial Intelligence for 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 (20 May 2022) | Viewed by 8910

Special Issue Editor

Dr. Joshua New

E-Mail Website
Guest Editor
Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Interests: Human/Computer Interaction (HCI); Artificial Intelligence (AI); visualization; automation; optimization; energy efficient buildings; urban-scale building energy modeling; computer vision; robotics; augmented and virtual reality; Brain Machine Interfaces (BMI)

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) continues to disrupt existing service industries, allowing innovative business models, and shows significant potential for improving the ways in which we live and work. Globally, buildings are responsible for over one third of primary energy consumption and nearly 40% of total direct and indirect CO2 emissions. This Special Issue seeks to address outstanding issues related to AI for buildings. At a time when devices are becoming more connected, cities are becoming smarter, and critical infrastructure (e.g., the electric grid) is evolving to allow enhanced demand flexibility of building loads, decentralized generation, and energy storage, the benefits of intelligent devices and services are often neither acquired nor shared in an equitable manner. While the academic publication of failures is not as prevalent as successes, this Special Issue encourages articles, reviews, or case studies that highlight the failure of data, AI applications, or training due to insufficient consideration of bias, equity, or diversity related to AI in buildings.

This Special Issue seeks manuscripts that address the following themes related to AI in buildings:

1) Bias in AI data—AI is often only as good as the data on which it was trained. There is a need for improved methods for ensuring sufficient data for proper functioning of the trained AI agent, testing bias inherent in a training dataset, including an incomplete sampling range of input variables, underlying human-derived bias of input data, or the challenging aspect of identifying additional input variables needed to sufficiently capture the target function.

2) Equitable AI applications—AI’s capabilities for automation, prediction, and optimization can improve our quality of life while considering trade-offs of costs including energy, environmental, social, and other constraints. There is a need for improved methods for scalably and dynamically leveraging enhanced sensors and controls (e.g., standards and semantic interoperability), model-informed operational control of building devices, urban-scale building energy modelling, or consideration of how a trained AI agent’s utility is equitably distributed.

3) Diverse AI workforce—there is global concern over the retraining necessary for AI-disrupted industries and stress that AI-displaced workers may put on nations’ social support structures. There is a need for improved methods or case studies for AI-enhanced training programs, effective methods for retraining individuals within AI-disrupted markets, AI-driven methods that allow for intuitive functioning within a disrupted market, or assessment of social justice as it relates to workforce diversity and how AI might empower higher-quality jobs for individuals that are more representative of the population.

Dr. Joshua New
Guest Editor

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

  • artificial intelligence
  • buildings
  • social justice
  • data bias
  • energy equity
  • AI applications
  • sensors and controls
  • diversity
  • workforce development

Published Papers (4 papers)

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Research

19 pages, 1592 KiB  
Article
Extracting Influential Factors for Building Energy Consumption via Data Mining Approaches
by Jihoon Jang, Jinmog Han, Min-Hwi Kim, Deuk-won Kim and Seung-Bok Leigh
Energies 2021, 14(24), 8505; https://doi.org/10.3390/en14248505 - 16 Dec 2021
Viewed by 2694
Abstract
To effectively analyze building energy, it is important to utilize the environmental data that influence building energy consumption. This study analyzed outdoor and indoor data collected from buildings to find out the conditions of rooms that had a significant effect on heating and [...] Read more.
To effectively analyze building energy, it is important to utilize the environmental data that influence building energy consumption. This study analyzed outdoor and indoor data collected from buildings to find out the conditions of rooms that had a significant effect on heating and cooling energy consumption. To examine the conditions of the rooms in each building, the energy consumption importance priority was derived using the Gini importance of the random forest algorithm on external and internal environmental data. The conditions that had a significant effect on energy consumption were analyzed to be: (i) conditions related to the building design—wall, floor, and window area ratio, the window-to-wall ratio (WWR), the window-to-floor area ratio (WFR), and the azimuth, and (ii) the internal conditions of the building—the illuminance, occupancy density, plug load, and frequency of room utilization. The room conditions derived through analysis were considered in each sample, and the final influential building energy consumption factors were derived by using them in a decision tree as being the WFR, window area ratio, floor area ratio, wall area ratio, and frequency of use. Furthermore, four room types were classified by combining the room conditions obtained from the key factor classifications derived in this study. Full article
(This article belongs to the Special Issue Artificial Intelligence for Buildings)
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21 pages, 4849 KiB  
Article
A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms
by Tingting Hou, Rengcun Fang, Jinrui Tang, Ganheng Ge, Dongjun Yang, Jianchao Liu and Wei Zhang
Energies 2021, 14(22), 7820; https://doi.org/10.3390/en14227820 - 22 Nov 2021
Cited by 20 | Viewed by 2279
Abstract
Short-term residential load forecasting is the precondition of the day-ahead and intra-day scheduling strategy of the household microgrid. Existing short-term electric load forecasting methods are mainly used to obtain regional power load for system-level power dispatch. Due to the high volatility, strong randomness, [...] Read more.
Short-term residential load forecasting is the precondition of the day-ahead and intra-day scheduling strategy of the household microgrid. Existing short-term electric load forecasting methods are mainly used to obtain regional power load for system-level power dispatch. Due to the high volatility, strong randomness, and weak regularity of the residential load of a single household, the mean absolute percentage error (MAPE) of the traditional methods forecasting results would be too big to be used for home energy management. With the increase in the total number of households, the aggregated load becomes more and more stable, and the cyclical pattern of the aggregated load becomes more and more distinct. In the meantime, the maximum daily load does not increase linearly with the increase in households in a small area. Therefore, in our proposed short-term residential load forecasting method, an optimal number of households would be selected adaptively, and the total aggregated residential load of the selected households is used for load prediction. In addition, ordering points to identify the clustering structure (OPTICS) algorithm are also selected to cluster households with similar power consumption patterns adaptively. It can be used to enhance the periodic regularity of the aggregated load in alternative. The aggregated residential load and encoded external factors are then used to predict the load in the next half an hour. The long short-term memory (LSTM) deep learning algorithm is used in the prediction because of its inherited ability to maintain historical data regularity in the forecasting process. The experimental data have verified the effectiveness and accuracy of our proposed method. Full article
(This article belongs to the Special Issue Artificial Intelligence for Buildings)
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16 pages, 84111 KiB  
Article
Interactive Smart Space for Single-Person Households Using Electroencephalogram through Fusion of Digital Twin and Artificial Intelligence
by Seung Yeul Ji
Energies 2021, 14(22), 7771; https://doi.org/10.3390/en14227771 - 19 Nov 2021
Viewed by 1601
Abstract
The core technology for building a smart space includes the capability to analyse the space for users using various sensors. The purpose of this study was to propose a personalised interactive smart space implementation model driven by the fusion of digital twin (DT) [...] Read more.
The core technology for building a smart space includes the capability to analyse the space for users using various sensors. The purpose of this study was to propose a personalised interactive smart space implementation model driven by the fusion of digital twin (DT) and artificial intelligence (AI) based on electroencephalogram (EEG) data. This study utilised a handheld EEG sensor to identify a user’s emotion information and focused on the connection with the space. A smart space for single-person households that responds to EEG-based biometric information was designed for an interactive space that can improve the current emotional state of the space user. The technical characteristics of DT and AI were analysed to control spatial changes according to the user’s emotional state and to address safety-related issues. Furthermore, a fusion mechanism for DT and AI was developed for intelligent motor control to change the dimensions of the space in order to improve the EEG state of the user. In addition, using an AI model that converts EEG data into emotional state information, the user’s emotional state was analysed, and key issues related to the spatial dimensions and change of space that induce psychological stability were investigated. Full article
(This article belongs to the Special Issue Artificial Intelligence for Buildings)
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11 pages, 848 KiB  
Article
An Intelligent Approach for Performing Energy-Driven Classification of Buildings Utilizing Joint Electricity–Gas Patterns
by Cristina Nichiforov, Antonio Martinez-Molina and Miltiadis Alamaniotis
Energies 2021, 14(22), 7465; https://doi.org/10.3390/en14227465 - 9 Nov 2021
Cited by 2 | Viewed by 1376
Abstract
Building type identification is an important task that may be used in confirming and verifying its legitimate operation. One of the main sources of information over the operation of a building is its energy consumption, with the analysis of electricity patterns being at [...] Read more.
Building type identification is an important task that may be used in confirming and verifying its legitimate operation. One of the main sources of information over the operation of a building is its energy consumption, with the analysis of electricity patterns being at the spotlight of a non-intrusive identification approach. However, electricity patterns are the only source of information, and therefore, their analysis imposes several restrictions. In this work, we introduce a new approach in energy-driven identification by adding one more source of information beyond the electricity pattern that may be utilized, namely the gas consumption pattern. In particular, we propose a new intelligent approach that jointly analyzes the electricity–gas patterns to provide the type of building at hand. Our approach exploits the synergism of the matrix profile data analysis technique with a feed-forward artificial neural network. This approach has applicability in the energy waste elimination through the implementation of different energy efficiency solutions, as well as the optimization of the demand-side process management, safer and reliable operation through fault detection, and the identification and validation of the real operation of the building. The obtained results demonstrate the improvement in identifying the type of the building by employing the proposed approach for joint electricity–gas patterns as compared to only using the electricity patterns. Full article
(This article belongs to the Special Issue Artificial Intelligence for Buildings)
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