sustainability-logo

Journal Browser

Journal Browser

Smart Zero-Energy and Zero-Carbon District Energy Systems

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (1 October 2023) | Viewed by 2094

Special Issue Editors

Sustainable Energy and Environment Thrust, Function Hub, The Hong Kong University of Science and Technology, Hong Kong, China
Interests: zero-energy buildings; energy flexible buildings; renewable energy; latent heat storage; smart and optimal systems with machine-learning
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Faculty of Architecture and the Built Environment, Delft University of Technology, Julianalaan 134, 2628 BL Delft, The Netherlands
Interests: green building; diversified renewable energy integration and management; zero-carbon building & community; sustainable transition for building and community; smart building and community based AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the progressively increasing frequency of extreme weather events caused by global climate change and environmental damage, energy efficiency and carbon emissions have been a growing political priority for various countries in recent years. Smart technologies can provide a flexible approach to the utilization of district energy systems and improve their operational efficiency and decrease carbon emissions, which is important for achieving carbon neutrality goals for building sectors. Smart technologies can synergistically consider local energy/resource application potential, optimal supply radius, and customer energy demand to achieve optimal distributed/district energy system deployment, installed power, and operating time–space strategies. Smart energy systems with optimized energy management systems can significantly reduce electricity operating costs and peak demand in residential communities. Common intelligent techniques include metaheuristics (e.g., particle swarm optimization and genetic algorithms), fuzzy logic, neural networks, multi-intelligent body approaches, and stochastic and robust planning. Recently, researchers have increasingly focused on the utilization of more complex energy systems integrated with renewable energy sources, domestic intelligent energy automation systems, and intelligent thermoelectric network communications. In this context, the research on smart zero-energy and zero-carbon district energy systems as well as the consideration of multi-energy smart networks and their interactions are essential in accurately assessing the economic and environmental benefits of district energy systems.

Considering the interest of this topic, we are organizing a Special Issue entitled “Smart Zero-Energy and Zero-Carbon District Energy Systems”, with the aim of reporting the most recent new findings from researchers and sector professionals within the scope of the themes below.

Original manuscripts covering the following broad themes are invited from researchers and agencies, namely:

  • Smart Zero-Energy Buildings;
  • Smart Zero-Carbon District Energy Systems;
  • Artificial Intelligence/Machine Learning for District Energy;
  • Intelligent Energy Management Systems;
  • Zero-Carbon District Cooling/Heating Systems;
  • Smart Low-Carbon Energy Systems;
  • Smart Phase Change Energy Storage;
  • Renewable Energy Technologies;
  • Distributed Energy Sources.

Dr. Yuekuan Zhou
Dr. Zhengxuan Liu
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. Sustainability 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 2400 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

  • smart zero-energy system
  • zero-carbon district energy systems
  • smart renewable energy system
  • smart energy management
  • artificial intelligence
  • machine learning

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

15 pages, 2514 KiB  
Article
A Framework Based on Deep Learning for Predicting Multiple Safety-Critical Parameter Trends in Nuclear Power Plants
by Haixia Gu, Gaojun Liu, Jixue Li, Hongyun Xie and Hanguan Wen
Sustainability 2023, 15(7), 6310; https://doi.org/10.3390/su15076310 - 06 Apr 2023
Viewed by 1430
Abstract
Operators in the main control room of a nuclear power plant have a crucial role in supervising all operations, and any human error can be fatal. By providing operators with information regarding the future trends of plant safety-critical parameters based on their actions, [...] Read more.
Operators in the main control room of a nuclear power plant have a crucial role in supervising all operations, and any human error can be fatal. By providing operators with information regarding the future trends of plant safety-critical parameters based on their actions, human errors can be detected and prevented in a timely manner. This paper proposed a Sequence-to-Sequence (Seq2Seq)-based Long Short-Term Memory (LSTM) model to predict safety-critical parameters and their future trends. The PCTran was used to extract data for four typical faults and fault levels, and eighty-six parameters were selected as characteristic quantities. The training, validation, and testing sets were collected in a ratio of 13:3:1, and appropriate hyperparameters were used to construct the Seq2Seq neural network. Compared with conventional deep learning models, the results indicated that the proposed model could successfully solve the complex problem of the trend estimation of key system parameters under the influence of operator action factors in multiple abnormal operating conditions. It is believed that the proposed model can help operators reduce the risk of human-caused errors and diagnose potential accidents. Full article
(This article belongs to the Special Issue Smart Zero-Energy and Zero-Carbon District Energy Systems)
Show Figures

Figure 1

Back to TopTop