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Intelligent Forecasting and Optimization in Electrical Power Systems II
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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: closed (30 September 2024) | Viewed by 9687

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 Baczyński

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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Related Special Issue

Published Papers (5 papers)

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Research

22 pages, 4837 KiB  
Article
A Machine Learning Approach to Forecasting Hydropower Generation
by Sarah Di Grande, Mariaelena Berlotti, Salvatore Cavalieri and Roberto Gueli
Energies 2024, 17(20), 5163; https://doi.org/10.3390/en17205163 - 17 Oct 2024
Viewed by 1934
Abstract
In light of challenges like climate change, pollution, and depletion of fossil fuel reserves, governments and businesses prioritize renewable energy sources such as solar, wind, and hydroelectric power. Renewable energy forecasting models play a crucial role for energy market operators and prosumers, aiding [...] Read more.
In light of challenges like climate change, pollution, and depletion of fossil fuel reserves, governments and businesses prioritize renewable energy sources such as solar, wind, and hydroelectric power. Renewable energy forecasting models play a crucial role for energy market operators and prosumers, aiding in planning, decision-making, optimization of energy sales, and evaluation of investments. This study aimed to develop machine learning models for hydropower forecasting in plants integrated into Water Distribution Systems, where energy is generated from water flow used for municipal water supply. The study involved developing and comparing monthly and two-week forecasting models, utilizing both one-step-ahead and two-step-ahead forecasting methodologies, along with different missing data imputation techniques. The tested algorithms—Seasonal Autoregressive Integrated Moving Average, Random Forest, Temporal Convolutional Network, and Neural Basis Expansion Analysis for Time Series—produced varying levels of performance. The Random Forest model proved to be the most effective for monthly forecasting, while the Temporal Convolutional Network delivered the best results for two-week forecasting. Across all scenarios, the seasonal–trend decomposition using the LOESS technique emerged as the most successful for missing data imputation. The accurate predictions obtained demonstrate the effectiveness of using these models for energy planning and decision-making. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems II)
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18 pages, 3639 KiB  
Article
Photovoltaic Power Generation Forecasting with Hidden Markov Model and Long Short-Term Memory in MISO and SISO Configurations
by Carlos J. Delgado, Estefanía Alfaro-Mejía, Vidya Manian, Efrain O’Neill-Carrillo and Fabio Andrade
Energies 2024, 17(3), 668; https://doi.org/10.3390/en17030668 - 30 Jan 2024
Cited by 8 | Viewed by 1788
Abstract
Photovoltaic (PV) power generation forecasting is an important research topic, aiming to mitigate variability caused by weather conditions and improve power generation planning. Climate factors, including solar irradiance, temperature, and cloud cover, influence the energy conversion achieved by PV systems. Long-term weather forecasting [...] Read more.
Photovoltaic (PV) power generation forecasting is an important research topic, aiming to mitigate variability caused by weather conditions and improve power generation planning. Climate factors, including solar irradiance, temperature, and cloud cover, influence the energy conversion achieved by PV systems. Long-term weather forecasting improves PV power generation planning, while short-term forecasting enhances control methods, such as managing ramp rates. The stochastic nature of weather variables poses a challenge for linear regression methods. Consequently, advanced, state-of-the-art machine learning (ML) approaches capable of handling non-linear data, such as long short-term memory (LSTM), have emerged. This paper introduces the implementation of a multivariate machine learning model to forecast PV power generation, considering multiple weather variables. A deep learning solution was implemented to analyze weather variables in a short time horizon. Utilizing a hidden Markov model for data preprocessing, an LSTM model was trained using the Alice Spring dataset provided by DKA Solar Center. The proposed workflow demonstrated superior performance compared to the results obtained by state-of-the-art methods, including support vector machine, radiation classification coordinate with LSTM (RCC-LSTM), and ESNCNN specifically concerning the proposed multi-input single-output LSTM model. This improvement is attributed to incorporating input features such as active power, temperature, humidity, horizontal and diffuse irradiance, and wind direction, with active power serving as the output variable. The proposed workflow achieved a mean square error (MSE) of 2.17×10−7, a root mean square error (RMSE) of 4.65×10−4, and a mean absolute error (MAE) of 4.04×10−4. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems II)
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21 pages, 7164 KiB  
Article
Daily Peak-Valley Electric-Load Forecasting Based on an SSA-LSTM-RF Algorithm
by Yaoying Wang, Shudong Sun and Zhiqiang Cai
Energies 2023, 16(24), 7964; https://doi.org/10.3390/en16247964 - 8 Dec 2023
Cited by 8 | Viewed by 1506
Abstract
In recent years, with the development of societies and economies, the demand for social electricity has further increased. The efficiency and accuracy of electric-load forecasting is an important guarantee for the safety and reliability of power system operation. With the sparrow search algorithm [...] Read more.
In recent years, with the development of societies and economies, the demand for social electricity has further increased. The efficiency and accuracy of electric-load forecasting is an important guarantee for the safety and reliability of power system operation. With the sparrow search algorithm (SSA), long short-term memory (LSTM), and random forest (RF), this research proposes an SSA-LSTM-RF daily peak-valley forecasting model. First, this research uses the Pearson correlation coefficient and the random forest model to select features. Second, the forecasting model takes the target value, climate characteristics, time series characteristics, and historical trend characteristics as input to the LSTM network to obtain the daily-load peak and valley values. Third, the super parameters of the LSTM network are optimized by the SSA algorithm and the global optimal solution is obtained. Finally, the forecasted peak and valley values are input into the random forest as features to obtain the output of the peak-valley time. The forest value of the SSA-LSTM-RF model is good, and the fitting ability is also good. Through experimental comparison, it can be seen that the electric-load forecasting algorithm based on the SSA-LSTM-RF model has higher forecasting accuracy and provides ideal performance for electric-load forecasting with different time steps. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems II)
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18 pages, 7025 KiB  
Article
Probabilistic Solar Forecasts as a Binary Event Using a Sky Camera
by Mathieu David, Joaquín Alonso-Montesinos, Josselin Le Gal La Salle and Philippe Lauret
Energies 2023, 16(20), 7125; https://doi.org/10.3390/en16207125 - 17 Oct 2023
Cited by 2 | Viewed by 1356
Abstract
With the fast increase of solar energy plants, a high-quality short-term forecast is required to smoothly integrate their production in the electricity grids. Usually, forecasting systems predict the future solar energy as a continuous variable. But for particular applications, such as concentrated solar [...] Read more.
With the fast increase of solar energy plants, a high-quality short-term forecast is required to smoothly integrate their production in the electricity grids. Usually, forecasting systems predict the future solar energy as a continuous variable. But for particular applications, such as concentrated solar plants with tracking devices, the operator needs to anticipate the achievement of a solar irradiance threshold to start or stop their system. In this case, binary forecasts are more relevant. Moreover, while most forecasting systems are deterministic, the probabilistic approach provides additional information about their inherent uncertainty that is essential for decision-making. The objective of this work is to propose a methodology to generate probabilistic solar forecasts as a binary event for very short-term horizons between 1 and 30 min. Among the various techniques developed to predict the solar potential for the next few minutes, sky imagery is one of the most promising. Therefore, we propose in this work to combine a state-of-the-art model based on a sky camera and a discrete choice model to predict the probability of an irradiance threshold suitable for plant operators. Two well-known parametric discrete choice models, logit and probit models, and a machine learning technique, random forest, were tested to post-process the deterministic forecast derived from sky images. All three models significantly improve the quality of the original deterministic forecast. However, random forest gives the best results and especially provides reliable probability predictions. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems II)
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26 pages, 5669 KiB  
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
Cited by 12 | Viewed by 1645
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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