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Energies
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Intelligent Forecasting and Optimization in Electrical Power Systems II
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Special Issue "Intelligent Forecasting and Optimization in Electrical Power Systems II"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: 25 October 2023 | Viewed by 732

Special Issue Editors

Prof. Dr. Pawel Piotrowski

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Guest Editor
Electrical Power Engineering Institute, Warsaw University of Technology (WUT), Koszykowa 75 Street, 00-661 Warszawa, Poland
Interests: artificial intelligence; machine learning; forecasting; optimization; power engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dariusz Baczynsk

E-Mail Website
Guest Editor
Electrical Power Engineering Institute, Warsaw University of Technology (WUT), Koszykowa 75 Street, 00-661 Warszawa, Poland
Interests: artificial neural networks, computational intelligence, optimization, forecasting, evolutionary algorithms, swarm intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Grzegorz Dudek

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Czestochowa University of Technology, 42-201 Częstochowa, Poland
Interests: machine learning; data mining; artificial intelligence; pattern recognition; evolutionary computation; their application to classification, regression, forecasting and optimization problems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on applications of artificial intelligence and machine learning models (including hybrid and ensembles methods) for forecasting and optimization in power engineering. ML and AI are one of the most exciting fields of computing today. These methods are effective and popular in regression problems, including forecasting and optimization. Effective operation of electrical power systems of various sizes (including microgrids) require precise short-term forecasts of both electricity generation in Renewable Energy Systems and electricity demand.

The ability to precise forecast electricity generation for example  by a wind farms and solar power plants is very important because RES often creates problems for networks managed by distribution system operators. Forecasts of generation in RES are also important for owners of small energy systems in order to optimize the use of various energy sources and facilitate energy storage.

This Special Issue solicits original papers and review articles that present new research results in forecasting and optimization in electrical power systems. 

 Expected topics include, but are not limited to:

  • Artificial intelligence/machine learning/deep learning for forecasting of electricity generation in RES,
  • Artificial intelligence/machine learning/deep learning for forecasting of power demand in electrical power systems
  • Optimization of electrical power systems,
  • Forecasting of meteorological data (wind speed, solar radiation) important to forecast electricity generation in RES
  • Statistical analysis of data for forecasting models (including problems of big, missing, distorted and uncertain data),  
  • Reliability of electrical power systems.

Prof. Dr. Pawel Piotrowski
Prof. Dr. Dariusz Baczynsk
Dr. Grzegorz Dudek
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.

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Published Papers (1 paper)

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Article
Prediction of the Electricity Generation of a 60-kW Photovoltaic System with Intelligent Models ANFIS and Optimized ANFIS-PSO
by
Luis O. Lara-Cerecedo
,
Jesús F. Hinojosa
,
Nun Pitalúa-Díaz
,
Yasuhiro Matsumoto
and
Alvaro González-Angeles
Energies 2023, 16(16), 6050; https://doi.org/10.3390/en16166050 - 18 Aug 2023
Viewed by 327
Abstract
The development and constant improvement of accurate predictive models of electricity generation from photovoltaic systems provide valuable planning tools for designers, producers, and self-consumers. In this research, an adaptive neuro-fuzzy inference model (ANFIS) was developed, which is an intelligent hybrid model that integrates [...] Read more.
The development and constant improvement of accurate predictive models of electricity generation from photovoltaic systems provide valuable planning tools for designers, producers, and self-consumers. In this research, an adaptive neuro-fuzzy inference model (ANFIS) was developed, which is an intelligent hybrid model that integrates the ability to learn by itself provided by neural networks and the function of language expression, how fuzzy logic infers, and an ANFIS model optimized by the particle swarm algorithm, both with a predictive capacity of about eight months. The models were developed using the Matlab® software and trained with four input variables (solar radiation, module temperature, ambient temperature, and wind speed) and the electrical power generated from a photovoltaic (PV) system as the output variable. The models’ predictions were compared with the experimental data of the system and evaluated with rigorous statistical metrics, obtaining results of RMSE = 1.79 kW, RMSPE = 3.075, MAE = 0.864 kW, and MAPE = 1.47% for ANFIS, and RMSE = 0.754 kW, RMSPE = 1.29, MAE = 0.325 kW, and MAPE = 0.556% for ANFIS-PSO, respectively. The evaluations indicate that both models have good predictive capacity. However, the PSO integration into the hybrid model allows for improving the predictive capability of the behavior of the photovoltaic system, which provides a better planning tool. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems II)
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