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Artificial Intelligence for Smart Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 7461

Special Issue Editors


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Guest Editor
GECAD, Institute of Engineering, Polytechnic of Porto, 4200-072 Porto, Portugal
Interests: artificial intelligence; multiagent systems; emotional agents; persuasive argumentation; group decision support systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-465 Porto, Portugal
Interests: artificial intelligence; demand response; electric vehicles; electricity markets; power and energy systems; renewable and sustainable energy; smart grids
Special Issues, Collections and Topics in MDPI journals

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Guest Editor

Special Issue Information

Dear Colleagues,

In recent years, with the development of Information and Communication Technologies (ICT) and the advent of Artificial Intelligence, technology has become immerse in all major industry sectors, and the power and energy sector is no exception.

Power and energy systems have been undertaking major changes which require different approaches and methods for their planning and operation. On one hand, distributed resources, from distributed generation to distributed mobile and stationary storage and demand flexibility, are making the traditional centralized management approaches to give place to more decentralized approaches. On the other hand, consumers and prosumers are being put in the center of the sector policy and market-driven approaches are currently the rule driving the sector dynamics and activities.

The ongoing changes bring enormous opportunities and challenges to traditional and to new players requiring huge changes in planning and operation methods. With more and innovative players entering in the sector, artificial intelligence-based approaches can be the key to deal with the new challenges and to ensure the systems and the respective players sustainability, both in economic and environmental terms.

Distributed decision-making, players’ modeling, as well as different artificial intelligence-based forecasting and optimization approaches are some of the techniques that are already being successfully used, even if only in a emergent and timid way.

This Special Issue aims at making known the most relevant advances on the development of smart energy systems that are grounded in Artificial Intelligence techniques such as machine learning, multiagent systems, and semantics. The ultimate goal is to identify the most promising approaches for each of the current and future challenges in the power and energy sector as well as to provide readers with a set of concrete applications of Artificial Intelligence that have the potential to be the basis for more intelligent, inclusive, and sustainable energy industry and use.

Prof. Dr. Goreti Marreiros
Prof. Dr. Zita Vale
Prof. Dr. Bo Nørregaard Jørgensen
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

  • artificial intelligence (AI)
  • collaborative learning
  • consumers profiling
  • deep learning
  • demand response
  • distributed energy resources
  • electricity markets
  • energy efficiency
  • energy internet
  • explainable AI
  • human and AI cooperation
  • intelligent systems
  • load and generation forecasting
  • machine learning
  • multiagent systems
  • ontologies
  • optimization
  • personalization
  • power and energy systems
  • semantics
  • smart grids
  • sustainability
  • transactive energy

Published Papers (3 papers)

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Research

42 pages, 8882 KiB  
Article
Accelerating Energy-Economic Simulation Models via Machine Learning-Based Emulation and Time Series Aggregation
by Alexander J. Bogensperger, Yann Fabel and Joachim Ferstl
Energies 2022, 15(3), 1239; https://doi.org/10.3390/en15031239 - 8 Feb 2022
Cited by 1 | Viewed by 2242
Abstract
Energy-economic simulation models with high levels of detail, high time resolutions, or large populations (e.g., distribution networks, households, electric vehicles, energy communities) are often limited due to their computational complexity. This paper introduces a novel methodology, combining cluster-based time series aggregation and sampling [...] Read more.
Energy-economic simulation models with high levels of detail, high time resolutions, or large populations (e.g., distribution networks, households, electric vehicles, energy communities) are often limited due to their computational complexity. This paper introduces a novel methodology, combining cluster-based time series aggregation and sampling methods, to efficiently emulate simulation models using machine learning and significantly reduce both simulation and training time. Machine learning-based emulation models require sufficient and high-quality data to generalize the dataset. Since simulations are computationally complex, their maximum number is limited. Sampling methods come into play when selecting the best parameters for a limited number of simulations ex ante. This paper introduces and compares multiple sampling methods on three energy-economic datasets and shows their advantage over a simple random sampling for small sample-sizes. The results show that a k-means cluster sampling approach (based on unsupervised learning) and adaptive sampling (based on supervised learning) achieve the best results especially for small sample sizes. While a k-means cluster sampling is simple to implement, it is challenging to increase the sample sizes if the emulation model does not achieve sufficient accuracy. The iterative adaptive sampling is more complex during implementation, but can be re-applied until a certain accuracy threshold is met. Emulation is then applied on a case study, emulating an energy-economic simulation framework for peer-to-peer pricing models in Germany. The evaluated pricing models are the “supply and demand ratio” (SDR) and “mid-market rate pricing” (MMR). A time series aggregation can reduce time series data of municipalities by 99.4% with less than 5% error for 98.2% (load) and 95.5% (generation) of all municipalities and hence decrease the simulation time needed to create sufficient training data. This paper combines time series aggregation and emulation in a novel approach and shows significant acceleration by up to 88.9% of the model’s initial runtime for the simulation of the entire population of around 12,000 municipalities. The time for re-calculating the population (e.g., for different scenarios or sensitivity analysis) can be increased by a factor of 1100 while still retaining high accuracy. The analysis of the simulation time shows that time series aggregation and emulation, considered individually, only bring minor improvements in the runtime but can, however, be combined effectively. This can significantly speed up both the simulation itself and the training of the emulation model and allows for flexible use, depending on the capabilities of the models and the practitioners. The results of the peer-to-peer pricing for approximately 12,000 German municipalities show great potential for energy communities. The mechanisms offer good incentives for the addition of necessary flexibility. Full article
(This article belongs to the Special Issue Artificial Intelligence for Smart Energy Systems)
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18 pages, 2418 KiB  
Article
Outage Estimation in Electric Power Distribution Systems Using a Neural Network Ensemble
by Sanjoy Das, Padmavathy Kankanala and Anil Pahwa
Energies 2021, 14(16), 4797; https://doi.org/10.3390/en14164797 - 6 Aug 2021
Cited by 4 | Viewed by 1536
Abstract
Outages in an overhead power distribution system are caused by multiple environmental factors, such as weather, trees, and animal activity. Since they form a major portion of the outages, the ability to accurately estimate these outages is a significant step towards enhancing the [...] Read more.
Outages in an overhead power distribution system are caused by multiple environmental factors, such as weather, trees, and animal activity. Since they form a major portion of the outages, the ability to accurately estimate these outages is a significant step towards enhancing the reliability of power distribution systems. Earlier research with statistical models, neural networks, and committee machines to estimate weather-related and animal-related outages has reported some success. In this paper, a deep neural network ensemble model for outage estimation is proposed. The entire input space is partitioned with a distinct neural network in the ensemble performing outage estimate in each partition. A novel algorithm is proposed to train the neural networks in the ensemble, while simultaneously partitioning the input space in a suitable manner. The proposed approach has been compared with the earlier approaches for outage estimation for four U.S. cities. The results suggest that the proposed method significantly improves the estimates of outages caused by wind and lightning in power distribution systems. A comparative analysis with a previously published model for animal-related outages further establishes the overall effectiveness of the deep neural network ensemble. Full article
(This article belongs to the Special Issue Artificial Intelligence for Smart Energy Systems)
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14 pages, 741 KiB  
Article
Upgrading BRICKS—The Context-Aware Semantic Rule-Based System for Intelligent Building Energy and Security Management
by Gabriel Santos, Tiago Pinto, Zita Vale, Rui Carvalho, Brígida Teixeira and Carlos Ramos
Energies 2021, 14(15), 4541; https://doi.org/10.3390/en14154541 - 27 Jul 2021
Cited by 5 | Viewed by 1812
Abstract
Building management systems (BMSs) are being implemented broadly by industries in recent decades. However, BMSs focus on specific domains, and when installed on the same building, they lack interoperability to work on a centralized user interface. On the other hand, BMSs interoperability allows [...] Read more.
Building management systems (BMSs) are being implemented broadly by industries in recent decades. However, BMSs focus on specific domains, and when installed on the same building, they lack interoperability to work on a centralized user interface. On the other hand, BMSs interoperability allows the implementation of complex rules based on multi-domain contexts. The Building’s Reasoning for Intelligent Control Knowledge-based System (BRICKS) is a context-aware semantic rule-based system for the intelligent management of buildings’ energy and security. It uses ontologies and semantic web technologies to interact with different domains, taking advantage of cross-domain knowledge to apply context-based rules. This work upgrades the previously presented version of BRICKS by including services for energy consumption and generation forecast, demand response, a configuration user interface (UI), and a dynamic building monitoring and management UI. The case study demonstrates BRICKS deployed at different aggregation levels in the authors’ laboratory building, managing a demand response event and interacting autonomously with other BRICKS instances. The results validate the correct functioning of the proposed tool, which contributes to the flexibility, efficiency, and security of building energy systems. Full article
(This article belongs to the Special Issue Artificial Intelligence for Smart Energy Systems)
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