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Electricity
Special Issues
AI in Power Systems
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AI in Power Systems

A special issue of Electricity (ISSN 2673-4826).

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 4230

Special Issue Editors

Dr. Mònica Aragüés-Peñalba

E-Mail Website
Guest Editor
CITCEA-UPC, Department of Electrical Engineering, Universitat Politecnica de Catalunya, 08028 Barcelona, Spain
Interests: grid integration of renewable energy generation; wind power; solar power; energy storage systems; HVDC transmission; microgrids; smart grids; big data for electrical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Hussain Syed Kazmi

E-Mail Website
Guest Editor
Department of Electrical Engineering, KU Leuven, 3001 Leuven, Belgium
Interests: low-voltage grids; energy management; modelling and control; artificial intelligence

Special Issue Information

Dear Colleagues,

The progress of information and communication technologies and digitalization are enabling the transition towards smart energy systems. The deployment of smart meters, together with other monitoring and metering devices, is giving us access to a large volume and significant variety of data in different power system domains, including generation, transmission, distribution, and consumption.

Artificial intelligence (AI), and in particular machine learning techniques, can help us to extract value from this data and guide some of the decision-making processes required for power systems management. Appropriate data processing has the potential to enhance the operation, control, maintenance, and planning of electrical grids.

The increasing penetration of renewables, combined with the installation of energy storage systems, is contributing to power systems’ decarbonization, but at the same time adding uncertainty into their operation. In this sense, AI techniques that can contribute to forecasting renewable power generation, power demand, flexibility, and electricity prices at different time horizons are of interest. Other applications of interest for AI techniques include, but are not limited to, topology identification, fault identification, systems observability, predictive maintenance, and energy management system design.

We invite you to contribute to this Special Issue.

Dr. Mònica Aragüés-Peñalba
Dr. Hussain Syed Kazmi
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. Electricity is an international peer-reviewed open access quarterly 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 1000 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
  • Machine Learning
  • Power System
  • Smart Grids
  • Smart Meters
  • Distribution grids
  • Transmission grids
  • Flexibility

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Published Papers (1 paper)

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Research

25 pages, 6491 KiB  
Article
Structural Ensemble Regression for Cluster-Based Aggregate Electricity Demand Forecasting
by Dimitrios Kontogiannis, Dimitrios Bargiotas, Aspassia Daskalopulu, Athanasios Ioannis Arvanitidis and Lefteri H. Tsoukalas
Electricity 2022, 3(4), 480-504; https://doi.org/10.3390/electricity3040025 - 2 Oct 2022
Cited by 5 | Viewed by 3001
Abstract
Accurate electricity demand forecasting is vital to the development and evolution of smart grids as well as the reinforcement of demand side management strategies in the energy sector. Since this forecasting task requires the efficient processing of load profiles extracted from smart meters [...] Read more.
Accurate electricity demand forecasting is vital to the development and evolution of smart grids as well as the reinforcement of demand side management strategies in the energy sector. Since this forecasting task requires the efficient processing of load profiles extracted from smart meters for large sets of clients, the challenges of high dimensionality often lead to the adoption of cluster-based aggregation strategies, resulting in scalable estimation models that operate on aggregate times series formed by client groups that share similar load characteristics. However, it is evident that the clustered time series exhibit different patterns that may not be processed efficiently by a single estimator or a fixed hybrid structure. Therefore, ensemble learning methods could provide an additional layer of model fusion, enabling the resulting estimator to adapt to the input series and yield better performance. In this work, we propose an adaptive ensemble member selection approach for stacking and voting regressors in the cluster-based aggregate forecasting framework that focuses on the examination of forecasting performance on peak and non-peak observations for the development of structurally flexible estimators for each cluster. The resulting ensemble models yield better overall performance when compared to the standalone estimators and our experiments indicate that member selection strategies focusing on the influence of non-peak performance lead to more performant ensemble models in this framework. Full article
(This article belongs to the Special Issue AI in Power Systems)
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