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Energies
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Data-Driven Techniques for Energy Management and Power Generation
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Data-Driven Techniques for Energy Management and Power Generation

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

Deadline for manuscript submissions: closed (10 November 2020) | Viewed by 10751

Special Issue Editor

Dr. Ricardo J. Bessa

E-Mail Website
Guest Editor
Center for Power and Energy Systems, INESC TEC, Campus da FEUPRua Dr Roberto Frias, 4200-465 Porto, Portugal
Interests: renewable energy; energy analytics; smart grids and electricity markets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The digital revolution in the energy sector is producing large volumes of data with relevant impacts on the business and functional processes of system operators, energy utilities, and grid users. The main challenge is to develop advanced data-driven methods integrating domain knowledge, which extract value from data for different domains: descriptive, predictive, and prescriptive. This Special Issue aims at encouraging researchers to address the following and related topics of interest:

- Frequency and non-frequency system services from distributed energy resources (DER)
- Energy efficiency, smart homes, and buildings
- Flexibility modelling and control of DER: standalone or combined with renewable energy sources
- Optimal combination of different energy carriers
- New business models: local energy communities, transactive energy, federated/virtual power plants, etc.
- Wholesale and retail energy markets
- Monitoring and predictive maintenance of DER
- Data privacy and economy in the energy sector

Dr. Ricardo Bessa
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

  • data-driven
  • artificial intelligence
  • energy management
  • renewable energy digitalization
  • smart grids
  • electricity market
  • distributed energy resources
  • energy efficiency

Published Papers (1 paper)

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Research

20 pages, 13040 KiB  
Article
Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models
by João Vitor Leme, Wallace Casaca, Marilaine Colnago and Maurício Araújo Dias
Energies 2020, 13(6), 1407; https://doi.org/10.3390/en13061407 - 18 Mar 2020
Cited by 15 | Viewed by 10046
Abstract
The prediction of electricity generation is one of the most important tasks in the management of modern energy systems. Improving the assertiveness of this prediction can support government agencies, electric companies, and power suppliers in minimizing the electricity cost to the end consumer. [...] Read more.
The prediction of electricity generation is one of the most important tasks in the management of modern energy systems. Improving the assertiveness of this prediction can support government agencies, electric companies, and power suppliers in minimizing the electricity cost to the end consumer. In this study, the problem of forecasting the energy demand in the Brazilian Interconnected Power Grid was addressed, by gathering different energy-related datasets taken from public Brazilian agencies into a unified and open database, used to tune three machine learning models. In contrast to several works in the Brazilian context, which provide only annual/monthly load estimations, the learning approaches Random Forest, Gradient Boosting, and Support Vector Machines were trained and optimized as new ensemble-based predictors with parameter tuning to reach accurate daily/monthly forecasts. Moreover, a detailed and in-depth exploration of energy-related data as obtained from the Brazilian power grid is also given. As shown in the validation study, the tuned predictors were effective in producing very small forecasting errors under different evaluation scenarios. Full article
(This article belongs to the Special Issue Data-Driven Techniques for Energy Management and Power Generation)
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