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Building Energy Simulation & Artificial Intelligence: a Way toward a Sustainable Built Environment

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 15818

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Special Issue Editor

Dr. Gerardo Maria Mauro

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Department of Engineering, Università degli Studi del Sannio, Piazza Roma 21, 82100 Benevento, Italy
Interests: thermodynamics; modeling of energy systems; energy optimization; energy efficiency; building performance simulation; building optimization; energy retrofit; sustainable design; cost-optimal analysis; energy policies
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Special Issue Information

Dear Colleagues,

It is well known that building energy optimization and sustainable development are on the same path. New efficient and effective technologies in this sector are needed to fight some critical issues of our times, such as climatic change, energy poverty, and economic crisis. We are living the age of energy and digital transitions, which pave the way to new efficient and smart designs for a sustainable built environment. Accordingly, artificial intelligence can provide a pivotal tool for building energy simulation and optimization.

In this frame, this Special Issue wants to provide a collection of worthy studies concerning:

Machine/deep learning applied to the prediction and labelling of building energy performance;
Frameworks coupling numerical optimization and machine/deep learning for the design of sustainable and low-energy buildings;
Combination of machine learning or other forecasting methods with smart control strategies, e.g., model predictive control, to minimize building energy consumption and discomfort;
Innovative integrated technologies concerning envelope and systems to optimize building energy performance implementing artificial intelligence tools.

Original papers related to the above topics and also dealing generally with technologies, methodologies, numerical, and experimental investigations addressing artificial intelligence applied to building energy simulation and optimization are welcome. This a possible way for a sustainable built environment. We need to pave such a way.

Thank you for your contributions.

Prof. Dr. Gerardo Maria Mauro
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

building energy simulation
building energy optimization
building energy labelling
sustainable built environment
machine learning
deep learning
artificial intelligence
artificial neural networks
model predictive control
short term forecasting
innovative building technologies

Published Papers (3 papers)

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Research

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18 pages, 12451 KiB

Open AccessArticle

Analysis of the Operation of Smoke Exhaust Ventilation in the Metro’s Technological Corridor Based on Numerical Simulation of Selected Locations of Fire

by Hanna Jędrzejuk and Faustyna Orzełowska

Energies 2023, 16(2), 849; https://doi.org/10.3390/en16020849 - 11 Jan 2023

Viewed by 1595

Abstract

The aim of the paper is to analyze the effectiveness of smoke exhaust ventilation for the protection of metro technical personnel. Therefore, the specified technological corridor as a part of the underground station was chosen. The Fire Dynamics Simulator (FDS) was used to carry out numerical simulations. Due to the low fire hazard, the heat release rate (HRR) was set at 1 MW after 250 s. Four cases were analyzed: three differing in the location of the fire source and a reference case in which the smoke exhaust ventilation is turned off. The analysis took into account temperature distributions and gas flow speeds, and qualitative verification of visibility. It was shown that the variant in which the fire source was located in the middle of the corridor turned out to be the most unfavorable variant in terms of the effectiveness of smoke exhaust ventilation. The operation of the smoke exhaust ventilation improved visibility, and reduced the temperature from 270 °C to 120–155 °C, depending on the variant, with local maximum flow speeds not exceeding 10 m/s. It was shown that properly designed smoke exhaust ventilation enables the evacuation of employees within the required safe evacuation time (RSET). Full article

(This article belongs to the Special Issue Building Energy Simulation & Artificial Intelligence: a Way toward a Sustainable Built Environment)

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16 pages, 19568 KiB

Open AccessArticle

Design and Implementation of Real-Time Kitchen Monitoring and Automation System Based on Internet of Things

by Ch Anwar Ul Hassan, Jawaid Iqbal, Muhammad Sufyan Khan, Saddam Hussain, Adnan Akhunzada, Mudabbir Ali, Abdullah Gani, Mueen Uddin and Syed Sajid Ullah

Energies 2022, 15(18), 6778; https://doi.org/10.3390/en15186778 - 16 Sep 2022

Cited by 10 | Viewed by 4600

Abstract

Automation can now be found in nearly every industry. However, home automation has yet to reach Pakistan. This paper presents an Internet of Things smart kitchen project that includes automation and monitoring. In this project, a system was developed that automatically detects the kitchen temperature. It also monitors the humidity level in the kitchen. This system includes built-in gas detection sensors that detect any gas leaks in the kitchen and notify the user if the gas pressure in the kitchen exceeds a certain level. This system also allows the user to remotely control appliances such as freezers, ovens, and air conditioners using a mobile phone. The user can control gas levels using their phone with this system. In this paper, the ESP32, DHT11 Sensor, 5 V Relay X 8, and MQ-135 gas sensors create a smart kitchen by controlling the temperature, managing humidity, and detecting gas leakage. The system was built on an Arduino board that is connected to the Internet. The hardware was integrated and programmed using an Arduino, and a user Android application was developed. The project’s goal is to allow any Android smartphone to remotely control devices. This method is commonly used in homes, businesses, and grocery stores. Users will be able to control all of their instruments from anywhere, including switches, fans, and lights. Furthermore, simulation was performed using Matlab2016b on multiple houses. In the simulation, not only was the kitchen considered, but also two, four, and six houses. Each house has two bedrooms, one living room, one guest room, two bathrooms, and one kitchen. The results revealed that using this system will have a scientifically significant impact on electricity consumption and cost. In the case of the houses, the cost was USD 33.32, 32.64, 22.32, and 19.54 for unscheduled, two, four, and six houses, respectively. Thus, it was observed that the cost and power are directly proportional to each other. The results reveal that the proposed solution efficiently reduces the cost as compared to that of unscheduled houses. Full article

(This article belongs to the Special Issue Building Energy Simulation & Artificial Intelligence: a Way toward a Sustainable Built Environment)

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Review

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23 pages, 1318 KiB

Open AccessReview

A Systematic Study on Reinforcement Learning Based Applications

by Keerthana Sivamayil, Elakkiya Rajasekar, Belqasem Aljafari, Srete Nikolovski, Subramaniyaswamy Vairavasundaram and Indragandhi Vairavasundaram

Energies 2023, 16(3), 1512; https://doi.org/10.3390/en16031512 - 03 Feb 2023

Cited by 17 | Viewed by 8353

Abstract

We have analyzed 127 publications for this review paper, which discuss applications of Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural language processing (NLP), internet of things security, recommendation systems, finance, and energy management. The optimization of energy use is critical in today’s environment. We mainly focus on the RL application for energy management. Traditional rule-based systems have a set of predefined rules. As a result, they may become rigid and unable to adjust to changing situations or unforeseen events. RL can overcome these drawbacks. RL learns by exploring the environment randomly and based on experience, it continues to expand its knowledge. Many researchers are working on RL-based energy management systems (EMS). RL is utilized in energy applications such as optimizing energy use in smart buildings, hybrid automobiles, smart grids, and managing renewable energy resources. RL-based energy management in renewable energy contributes to achieving net zero carbon emissions and a sustainable environment. In the context of energy management technology, RL can be utilized to optimize the regulation of energy systems, such as building heating, ventilation, and air conditioning (HVAC) systems, to reduce energy consumption while maintaining a comfortable atmosphere. EMS can be accomplished by teaching an RL agent to make judgments based on sensor data, such as temperature and occupancy, to modify the HVAC system settings. RL has proven beneficial in lowering energy usage in buildings and is an active research area in smart buildings. RL can be used to optimize energy management in hybrid electric vehicles (HEVs) by learning an optimal control policy to maximize battery life and fuel efficiency. RL has acquired a remarkable position in robotics, automated cars, and gaming applications. The majority of security-related applications operate in a simulated environment. The RL-based recommender systems provide good suggestions accuracy and diversity. This article assists the novice in comprehending the foundations of reinforcement learning and its applications. Full article

(This article belongs to the Special Issue Building Energy Simulation & Artificial Intelligence: a Way toward a Sustainable Built Environment)

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