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Intelligent Forecasting and Optimization in Electrical Power Systems
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Intelligent Forecasting and Optimization in Electrical Power Systems

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

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 39800

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Paweł 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
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
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

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. Paweł Piotrowski
Prof. Dr. Grzegorz Dudek
Prof. Dr. Dariusz Baczyński
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 (19 papers)

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Editorial

Jump to: Research, Review

11 pages, 212 KiB  
Editorial
Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications
by Grzegorz Dudek, Paweł Piotrowski and Dariusz Baczyński
Energies 2023, 16(7), 3024; https://0-doi-org.brum.beds.ac.uk/10.3390/en16073024 - 26 Mar 2023
Cited by 4 | Viewed by 1480
Abstract
A modern power system is a complex network of interconnected components, such as generators, transmission lines, and distribution subsystems, that are designed to provide electricity to consumers in an efficient and reliable manner [...] Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)

Research

Jump to: Editorial, Review

18 pages, 1083 KiB  
Article
Short-Term Occupancy Forecasting for a Smart Home Using Optimized Weight Updates Based on GA and PSO Algorithms for an LSTM Network
by Sameh Mahjoub, Sami Labdai, Larbi Chrifi-Alaoui, Bruno Marhic and Laurent Delahoche
Energies 2023, 16(4), 1641; https://0-doi-org.brum.beds.ac.uk/10.3390/en16041641 - 07 Feb 2023
Cited by 7 | Viewed by 1282
Abstract
In this work, we provide a smart home occupancy prediction technique based on environmental variables such as CO2, noise, and relative temperature via our machine learning method and forecasting strategy. The proposed algorithms enhance the energy management system through the optimal [...] Read more.
In this work, we provide a smart home occupancy prediction technique based on environmental variables such as CO2, noise, and relative temperature via our machine learning method and forecasting strategy. The proposed algorithms enhance the energy management system through the optimal use of the electric heating system. The Long Short-Term Memory (LSTM) neural network is a special deep learning strategy for processing time series prediction that has shown promising prediction results in recent years. To improve the performance of the LSTM algorithm, particularly for autocorrelation prediction, we will focus on optimizing weight updates using various approaches such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performances of the proposed methods are evaluated using real available datasets. Test results reveal that the GA and the PSO can forecast the parameters with higher prediction fidelity compared to the LSTM networks. Indeed, all experimental predictions reached a range in their correlation coefficients between 99.16% and 99.97%, which proves the efficiency of the proposed approaches. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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14 pages, 1825 KiB  
Article
Six Days Ahead Forecasting of Energy Production of Small Behind-the-Meter Solar Sites
by Hugo Bezerra Menezes Leite and Hamidreza Zareipour
Energies 2023, 16(3), 1533; https://0-doi-org.brum.beds.ac.uk/10.3390/en16031533 - 03 Feb 2023
Cited by 5 | Viewed by 1382
Abstract
Due to the growing penetration of behind-the-meter (BTM) photovoltaic (PV) installations, accurate solar energy forecasts are required for a reliable economic energy system operation. A new hybrid methodology is proposed in this paper with a sequence of one-step ahead models to accumulate 144 [...] Read more.
Due to the growing penetration of behind-the-meter (BTM) photovoltaic (PV) installations, accurate solar energy forecasts are required for a reliable economic energy system operation. A new hybrid methodology is proposed in this paper with a sequence of one-step ahead models to accumulate 144 h for a small-scale BTM PV site. Three groups of models with different inputs are developed to cover 6 days of forecasting horizon, with each group trained for each hour of the above zero irradiance. In addition, a novel dataset preselection is proposed, and neighboring solar farms’ power predictions are used as a feature to boost the accuracy of the model. Two techniques are selected: XGBoost and CatBoost. An extensive assessment for 1 year is conducted to evaluate the proposed method. Numerical results highlight that training the models with the previous, current, and 1 month ahead from the previous year referenced by the target month can improve the model’s accuracy. Finally, when solar energy predictions from neighboring solar farms are incorporated, this further increases the overall forecast accuracy. The proposed method is compared with the complete-history persistence ensemble (CH-PeEn) model as a benchmark. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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22 pages, 857 KiB  
Article
Analysis and Forecasting of Monthly Electricity Demand Time Series Using Pattern-Based Statistical Methods
by Paweł Pełka
Energies 2023, 16(2), 827; https://0-doi-org.brum.beds.ac.uk/10.3390/en16020827 - 11 Jan 2023
Cited by 13 | Viewed by 4765
Abstract
This article provides a solution based on statistical methods (ARIMA, ETS, and Prophet) to predict monthly power demand, which approximates the relationship between historical and future demand patterns. The energy demand time series shows seasonal fluctuation cycles, long-term trends, instability, and random noise. [...] Read more.
This article provides a solution based on statistical methods (ARIMA, ETS, and Prophet) to predict monthly power demand, which approximates the relationship between historical and future demand patterns. The energy demand time series shows seasonal fluctuation cycles, long-term trends, instability, and random noise. In order to simplify the prediction issue, the monthly load time series is represented by an annual cycle pattern, which unifies the data and filters the trends. A simulation study performed on the monthly electricity load time series for 35 European countries confirmed the high accuracy of the proposed models. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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20 pages, 672 KiB  
Article
Multi-Task Autoencoders and Transfer Learning for Day-Ahead Wind and Photovoltaic Power Forecasts
by Jens Schreiber and Bernhard Sick
Energies 2022, 15(21), 8062; https://0-doi-org.brum.beds.ac.uk/10.3390/en15218062 - 30 Oct 2022
Cited by 4 | Viewed by 1399
Abstract
Integrating new renewable energy resources requires robust and reliable forecasts to ensure a stable electrical grid and avoid blackouts. Sophisticated representation learning techniques, such as autoencoders, play an essential role, as they allow for the extraction of latent features to forecast the expected [...] Read more.
Integrating new renewable energy resources requires robust and reliable forecasts to ensure a stable electrical grid and avoid blackouts. Sophisticated representation learning techniques, such as autoencoders, play an essential role, as they allow for the extraction of latent features to forecast the expected generated wind and photovoltaic power for the next seconds up to days. Thereby, autoencoders reduce the required training time and the time spent in manual feature engineering and often improve the forecast error. However, most current renewable energy forecasting research on autoencoders focuses on smaller forecast horizons for the following seconds and hours based on meteorological measurements. At the same time, larger forecast horizons, such as day-ahead power forecasts based on numerical weather predictions, are crucial for planning loads and demands within the electrical grid to prevent power failures. There is little evidence on the ability of autoencoders and their respective forecasting models to improve through multi-task learning and time series autoencoders for day-ahead power forecasts. We can close these gaps by proposing a multi-task learning autoencoder based on the recently introduced temporal convolution network. This approach reduces the number of trainable parameters by 38 for photovoltaic data and 202 for wind data while having the best reconstruction error compared to nine other representation learning techniques. At the same time, this model decreases the day-ahead forecast error up to 18.3% for photovoltaic parks and 1.5% for wind parks. We round off these results by analyzing the influences of the latent size and the number of layers to fine-tune the encoder for wind and photovoltaic power forecasts. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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19 pages, 2285 KiB  
Article
A Comprehensive Study of Random Forest for Short-Term Load Forecasting
by Grzegorz Dudek
Energies 2022, 15(20), 7547; https://0-doi-org.brum.beds.ac.uk/10.3390/en15207547 - 13 Oct 2022
Cited by 27 | Viewed by 2235
Abstract
Random forest (RF) is one of the most popular machine learning (ML) models used for both classification and regression problems. As an ensemble model, it demonstrates high predictive accuracy and low variance, while being easy to learn and optimize. In this study, we [...] Read more.
Random forest (RF) is one of the most popular machine learning (ML) models used for both classification and regression problems. As an ensemble model, it demonstrates high predictive accuracy and low variance, while being easy to learn and optimize. In this study, we use RF for short-term load forecasting (STLF), focusing on data representation and training modes. We consider seven methods of defining input patterns and three training modes: local, global and extended global. We also investigate key RF hyperparameters to learn about their optimal settings. The experimental part of the work demonstrates on four STLF problems that our model, in its optimal variant, can outperform both statistical and ML models, providing the most accurate forecasts. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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24 pages, 800 KiB  
Article
A Spiking Neural Network Based Wind Power Forecasting Model for Neuromorphic Devices
by Juan Manuel González Sopeña, Vikram Pakrashi and Bidisha Ghosh
Energies 2022, 15(19), 7256; https://0-doi-org.brum.beds.ac.uk/10.3390/en15197256 - 02 Oct 2022
Cited by 5 | Viewed by 2056
Abstract
Many authors have reported the use of deep learning techniques to model wind power forecasts. For shorter-term prediction horizons, the training and deployment of such models is hindered by their computational cost. Neuromorphic computing provides a new paradigm to overcome this barrier through [...] Read more.
Many authors have reported the use of deep learning techniques to model wind power forecasts. For shorter-term prediction horizons, the training and deployment of such models is hindered by their computational cost. Neuromorphic computing provides a new paradigm to overcome this barrier through the development of devices suited for applications where latency and low-energy consumption play a key role, as is the case in real-time short-term wind power forecasting. The use of biologically inspired algorithms adapted to the architecture of neuromorphic devices, such as spiking neural networks, is essential to maximize their potential. In this paper, we propose a short-term wind power forecasting model based on spiking neural networks adapted to the computational abilities of Loihi, a neuromorphic device developed by Intel. A case study is presented with real wind power generation data from Ireland to evaluate the ability of the proposed approach, reaching a normalised mean absolute error of 2.84 percent for one-step-ahead wind power forecasts. The study illustrates the plausibility of the development of neuromorphic devices aligned with the specific demands of the wind energy sector. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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21 pages, 10050 KiB  
Article
Offshore Wind Power Forecasting—A New Hyperparameter Optimisation Algorithm for Deep Learning Models
by Shahram Hanifi, Saeid Lotfian, Hossein Zare-Behtash and Andrea Cammarano
Energies 2022, 15(19), 6919; https://0-doi-org.brum.beds.ac.uk/10.3390/en15196919 - 21 Sep 2022
Cited by 15 | Viewed by 2310
Abstract
The main obstacle against the penetration of wind power into the power grid is its high variability in terms of wind speed fluctuations. Accurate power forecasting, while making maintenance more efficient, leads to the profit maximisation of power traders, whether for a wind [...] Read more.
The main obstacle against the penetration of wind power into the power grid is its high variability in terms of wind speed fluctuations. Accurate power forecasting, while making maintenance more efficient, leads to the profit maximisation of power traders, whether for a wind turbine or a wind farm. Machine learning (ML) models are recognised as an accurate and fast method of wind power prediction, but their accuracy depends on the selection of the correct hyperparameters. The incorrect choice of hyperparameters will make it impossible to extract the maximum performance of the ML models, which is attributed to the weakness of the forecasting models. This paper uses a novel optimisation algorithm to tune the long short-term memory (LSTM) model for short-term wind power forecasting. The proposed method improves the power prediction accuracy and accelerates the optimisation process. Historical power data of an offshore wind turbine in Scotland is utilised to validate the proposed method and compare its outcome with regular ML models tuned by grid search. The results revealed the significant effect of the optimisation algorithm on the forecasting models’ performance, with improvements of the RMSE of 7.89, 5.9, and 2.65 percent, compared to the persistence and conventional grid search-tuned Auto-Regressive Integrated Moving Average (ARIMA) and LSTM models. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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22 pages, 3640 KiB  
Article
The Use of Singular Spectrum Analysis and K-Means Clustering-Based Bootstrap to Improve Multistep Ahead Load Forecasting
by Winita Sulandari, Yudho Yudhanto and Paulo Canas Rodrigues
Energies 2022, 15(16), 5838; https://0-doi-org.brum.beds.ac.uk/10.3390/en15165838 - 11 Aug 2022
Cited by 5 | Viewed by 2017
Abstract
In general, studies on short-term hourly electricity load modeling and forecasting do not investigate in detail the sources of uncertainty in forecasting. This study aims to evaluate the impact and benefits of applying bootstrap aggregation in overcoming the uncertainty in time series forecasting, [...] Read more.
In general, studies on short-term hourly electricity load modeling and forecasting do not investigate in detail the sources of uncertainty in forecasting. This study aims to evaluate the impact and benefits of applying bootstrap aggregation in overcoming the uncertainty in time series forecasting, thereby increasing the accuracy of multistep ahead point forecasts. We implemented the existing and proposed clustering-based bootstrapping methods to generate new electricity load time series. In the proposed method, we use singular spectrum analysis to decompose the series between signal and noise to reduce the variance of the bootstrapped series. The noise is then bootstrapped by K-means clustering-based generation of Gaussian normal distribution (KM.N) before adding it back to the signal, resulting in the bootstrapped series. We apply the benchmark models for electricity load forecasting, SARIMA, NNAR, TBATS, and DSHW, to model all new bootstrapped series and determine the multistep ahead point forecasts. The forecast values obtained from the original series are compared with the mean and median across all forecasts calculated from the bootstrapped series using the Malaysian, Polish, and Indonesian hourly load series for 12, 24, and 36 steps ahead. We conclude that, in this case, the proposed bootstrapping method improves the accuracy of multistep-ahead forecast values, especially when considering the SARIMA and NNAR models. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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27 pages, 5169 KiB  
Article
Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development
by Paweł Piotrowski, Dariusz Baczyński and Marcin Kopyt
Energies 2022, 15(15), 5578; https://0-doi-org.brum.beds.ac.uk/10.3390/en15155578 - 01 Aug 2022
Cited by 4 | Viewed by 1181
Abstract
The main objective of this study was to conduct multi-stage and multi-variant prognostic research to assess the impact of e-mobility development on the Polish power system for the period 2022–2027. The research steps were as follows: forecast the number of electric vehicles (using [...] Read more.
The main objective of this study was to conduct multi-stage and multi-variant prognostic research to assess the impact of e-mobility development on the Polish power system for the period 2022–2027. The research steps were as follows: forecast the number of electric vehicles (using seven methods), forecast annual power demand arising solely out of the operation of the forecast number of electric vehicles, forecast annual power demand with and without the impact of e-mobility growth (using six methods), forecast daily profiles of typical days with and without the impact of e-mobility growth (using three methods). For the purpose of this research, we developed a unique Growth Dynamics Model to forecast the number of electric vehicles in Poland. The application of Multi-Layer Perceptron (MLP) to the extrapolation of non-linear functions (to the forecast number of electric vehicles and forecast annual power demand without the impact of e-mobility growth) is our original, unique proposal to use the Artificial Neural Network (ANN). Another unique, innovative proposal is to include Artificial Neural Networks (Multi-Layer Perceptron and Long short-term memory (LSTM)) in an Ensemble Model for simultaneous extrapolation of 24 non-linear functions to forecast daily profiles of typical days without taking e-mobility into account. This research determined the impact of e-mobility development on the Polish power system, both in terms of annual growth of demand for power and within particular days (hourly distribution) for two typical days (summer and winter). Under the (most likely) balanced growth variant of annual demand for power, due to e-mobility, such demand would grow by more than 4%, and almost 7% under the optimistic variant. Percentage growth of power demand in terms of variation according to time of day was determined. For instance, for the balanced variant, the largest percentage share of e-mobility was in the evening “peak” time (about 6%), and the smallest percentage was in the night “valley” (about 2%). Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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27 pages, 1730 KiB  
Article
Evolutionary Multi-Objective Optimization Applied to Industrial Refrigeration Systems for Energy Efficiency
by Nadia Nedjah, Luiza de Macedo Mourelle and Marcelo Silveira Dantas Lizarazu
Energies 2022, 15(15), 5575; https://0-doi-org.brum.beds.ac.uk/10.3390/en15155575 - 01 Aug 2022
Cited by 3 | Viewed by 1504
Abstract
Refrigeration systems based on cooling towers and chillers are widely used equipment in industrial buildings, such as shopping centers, gas and oil refineries and power plants, among many others. Cooling towers are used to recover the heat rejected by the refrigeration system. In [...] Read more.
Refrigeration systems based on cooling towers and chillers are widely used equipment in industrial buildings, such as shopping centers, gas and oil refineries and power plants, among many others. Cooling towers are used to recover the heat rejected by the refrigeration system. In this work, the refrigeration is composed of cooling towers dotted with ventilators and compression chillers. The growing environmental concerns and the current scenario of scarce water and energy resources have lead to the adoption of actions to obtain the maximum energy efficiency in such refrigeration equipment. This backs up the application of computational intelligence to optimize the operating conditions of the involved equipment and cooling processes. In this context, we utilize multi-objective optimization algorithms to determine the optimal operational setpoints of the cooling system regarding the cooling towers, its fans and the included chillers. We use evolutionary multi-objective optimization to provide the best trade-offs between two conflicting objectives: maximization of the effectiveness of the cooling towers and minimization of the overall power requirement of the refrigeration system. The optimization process respects the constraints to guarantee the correct and safe operation of the equipment when the evolved solution is implemented. In this work, we apply three evolutionary multi-objective algorithms: Non-dominated Sorting Genetic Algorithm (NSGA-II), Micro-Genetic Algorithm (Micro-GA) and Strength Pareto Evolutionary Algorithm (SPEA2). The results obtained are analyzed under different scenarios and models of the cooling system’s equipment, allowing for the selection of the best algorithm and best equipment’s model to achieve energy efficiency of the studied refrigeration system. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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34 pages, 3469 KiB  
Article
Reliability Analysis of MV Electric Distribution Networks Including Distributed Generation and ICT Infrastructure
by Miroslaw Parol, Jacek Wasilewski, Tomasz Wojtowicz, Bartlomiej Arendarski and Przemyslaw Komarnicki
Energies 2022, 15(14), 5311; https://0-doi-org.brum.beds.ac.uk/10.3390/en15145311 - 21 Jul 2022
Cited by 8 | Viewed by 2280
Abstract
In recent years, the increased distributed generation (DG) capacity in electric distribution systems has been observed. Therefore, it is necessary to research existing structures of distribution networks as well as to develop new (future) system structures. There are many works on the reliability [...] Read more.
In recent years, the increased distributed generation (DG) capacity in electric distribution systems has been observed. Therefore, it is necessary to research existing structures of distribution networks as well as to develop new (future) system structures. There are many works on the reliability of distribution systems with installed DG sources. This paper deals with a reliability analysis for both present and future medium voltage (MV) electric distribution system structures. The impact of DG technology used and energy source location on the power supply reliability has been analyzed. The reliability models of electrical power devices, conventional and renewable energy sources as well as information and communications technology (ICT) components have been proposed. Main contribution of this paper are the results of performed calculations, which have been analyzed for specific system structures (two typical present network structures and two future network structures), using detailed information on DG types, their locations and power capacities, as well as distribution system automation applied (automatic stand-by switching on—ASS and automatic power restoration—APR). The reliability of the smart grid consisting of the distribution network and the coupled communications network was simulated and assessed. The observations and conclusions based on calculation results have been made. More detailed modeling and consideration of system automation of distribution grids with DG units coupled with the communication systems allows the design and application of more reliable MV network structures. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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23 pages, 3998 KiB  
Article
Advanced Forecasting Methods of 5-Minute Power Generation in a PV System for Microgrid Operation Control
by Paweł Piotrowski, Mirosław Parol, Piotr Kapler and Bartosz Fetliński
Energies 2022, 15(7), 2645; https://0-doi-org.brum.beds.ac.uk/10.3390/en15072645 - 04 Apr 2022
Cited by 6 | Viewed by 1578
Abstract
This paper concerns very-short-term (5-Minute) forecasting of photovoltaic power generation. Developing the methods useful for this type of forecast is the main aim of this study. We prepared a comprehensive study based on fragmentary time series, including 4 full days, of 5 min [...] Read more.
This paper concerns very-short-term (5-Minute) forecasting of photovoltaic power generation. Developing the methods useful for this type of forecast is the main aim of this study. We prepared a comprehensive study based on fragmentary time series, including 4 full days, of 5 min power generation. This problem is particularly important to microgrids’ operation control, i.e., for the proper operation of small energy micro-systems. The forecasting of power generation by renewable energy sources on a very-short-term horizon, including PV systems, is very important, especially in the island mode of microgrids’ operation. Inaccurate forecasts can lead to the improper operation of microgrids or increasing costs/decreasing profits for microgrid operators. This paper presents a short description of the performance of photovoltaic systems, particularly the main environmental parameters, and a very detailed statistical analysis of data collected from four sample time series of power generation in an existing PV system, which was located on the roof of a building. Different forecasting methods, which can be employed for this type of forecast, and the choice of proper input data in these methods were the subject of special attention in this paper. Ten various prognostic methods (including hybrid and team methods) were tested. A new, proprietary forecasting method—a hybrid method using three independent MLP-type neural networks—was a unique technique devised by the authors of this paper. The forecasts achieved with the use of various methods are presented and discussed in detail. Additionally, a qualitative analysis of the forecasts, achieved using different measures of quality, was performed. Some of the presented prognostic models are, in our opinion, promising tools for practical use, e.g., for operation control in low-voltage microgrids. The most favorable forecasting methods for various sets of input variables were indicated, and practical conclusions regarding the problem under study were formulated. Thanks to the analysis of the utility of different forecasting methods for four analyzed, separate time series, the reliability of conclusions related to the recommended methods was significantly increased. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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22 pages, 12452 KiB  
Article
Voltage Control in MV Network with Distributed Generation—Possibilities of Real Quality Enhancement
by Paweł Pijarski, Piotr Kacejko and Marek Wancerz
Energies 2022, 15(6), 2081; https://0-doi-org.brum.beds.ac.uk/10.3390/en15062081 - 12 Mar 2022
Cited by 4 | Viewed by 2291
Abstract
Connecting an increasing number of distributed sources in MV (medium voltage) and LV (low voltage) distribution networks causes voltage problems resulting mainly from periodic power flows towards the HV/MV (HV—high voltage) transformer station. This temporarily changes the nature of distribution networks from receiving [...] Read more.
Connecting an increasing number of distributed sources in MV (medium voltage) and LV (low voltage) distribution networks causes voltage problems resulting mainly from periodic power flows towards the HV/MV (HV—high voltage) transformer station. This temporarily changes the nature of distribution networks from receiving to supply networks and causes an increase in the voltage values deep within the network, often above the permissible level. Therefore, it is necessary to search for new voltage control methods that take into account the active participation of distributed sources. The article proposes a concept of such a system in which the control signals are transformer taps in the HV/LV station and the values of reactive powers generated or consumed by RES (renewable energy sources). These values can be determined either by solving the optimisation problem (according to a given quality indicator criterion) or on the basis of appropriately selected settings of the Q(U) characteristics of the inverters and the HV/LV transformer ratio. The article describes both approaches, pointing to the advantages and disadvantages of each of them. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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30 pages, 5715 KiB  
Article
Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms
by Paweł Piotrowski, Dariusz Baczyński, Marcin Kopyt and Tomasz Gulczyński
Energies 2022, 15(4), 1252; https://0-doi-org.brum.beds.ac.uk/10.3390/en15041252 - 09 Feb 2022
Cited by 19 | Viewed by 2469
Abstract
The ability to precisely forecast power generation for large wind farms is very important, since such generation is highly unstable and creates problems for Distribution and Transmission System Operators to properly prepare the power system for operation. Forecasts for the next 24 h [...] Read more.
The ability to precisely forecast power generation for large wind farms is very important, since such generation is highly unstable and creates problems for Distribution and Transmission System Operators to properly prepare the power system for operation. Forecasts for the next 24 h play an important role in this process. They are also used in energy market transactions. Even a small improvement in the quality of these forecasts translates into more security of the system and savings for the economy. Using two wind farms for statistical analyses and forecasting considerably increases credibility of newly created effective prediction methods and formulated conclusions. In the first part of our study, we have analysed the available data to identify potentially useful explanatory variables for forecasting models with additional development of new input data based on the basic data set. We demonstrate that it is better to use Numerical Weather Prediction (NWP) point forecasts for hourly lags: −3, 2, −1, 0, 1, 2, 3 (original contribution) as input data than lags 0, 1 that are typically used. Also, we prove that it is better to use forecasts from two NWP models as input data. Ensemble, hybrid and single methods are used for predictions, including machine learning (ML) solutions like Gradient-Boosted Trees (GBT), Random Forest (RF), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), K-Nearest Neighbours Regression (KNNR) and Support Vector Regression (SVR). Original ensemble methods, developed for researching specific implementations, have reduced errors of forecast energy generation for both wind farms as compared to single methods. Predictions by the original ensemble forecasting method, called “Ensemble Averaging Without Extremes” have the lowest normalized mean absolute error (nMAE) among all tested methods. A new, original “Additional Expert Correction” additionally reduces errors of energy generation forecasts for both wind farms. The proposed ensemble methods are also applicable to short-time generation forecasting for other renewable energy sources (RES), e.g., hydropower or photovoltaic (PV) systems. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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24 pages, 7586 KiB  
Article
Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator
by Łukasz Rokicki
Energies 2021, 14(19), 6351; https://0-doi-org.brum.beds.ac.uk/10.3390/en14196351 - 05 Oct 2021
Cited by 4 | Viewed by 1305
Abstract
The issue of optimization of the configuration and operating states in low voltage microgrids is important both from the point of view of the proper operation of the microgrid and its impact on the medium voltage distribution network to which such microgrid is [...] Read more.
The issue of optimization of the configuration and operating states in low voltage microgrids is important both from the point of view of the proper operation of the microgrid and its impact on the medium voltage distribution network to which such microgrid is connected. Suboptimal microgrid configuration may cause problems in networks managed by distribution system operators, as well as for electricity consumers and owners of microsources and energy storage systems connected to the microgrid. Structures particularly sensitive to incorrect determination of the operating states of individual devices are hybrid microgrids that combine an alternating current and direct current networks with the use of a bidirectional power electronic converter. An analysis of available literature shows that evolutionary and swarm optimization algorithms are the most frequently chosen for the optimization of power systems. The research presented in this article concerns the assessment of the possibilities of using artificial immune systems, operating on the basis of the CLONALG algorithm, as tools enabling the effective optimization of low voltage hybrid microgrids. In his research, the author developed a model of a hybrid low voltage microgrid, formulated three optimization tasks, and implemented an algorithm for solving the formulated tasks based on an artificial immune system using the CLONALG algorithm. The conducted research consisted of performing a 24 h simulation of microgrid operation for each of the formulated optimization tasks (divided into 10 min independent optimization periods). A novelty in the conducted research was the modification of the hypermutation operator, which is the key mechanism for the functioning of the CLONALG algorithm. In order to verify the changes introduced in the CLONALG algorithm and to assess the effectiveness of the artificial immune system in solving optimization tasks, optimization was also carried out with the use of an evolutionary algorithm, commonly used in solving such tasks. Based on the analysis of the obtained results of optimization calculations, it can be concluded that the artificial immune system proposed in this article, operating on the basis of the CLONALG algorithm with a modified hypermutation operator, in most of the analyzed cases obtained better results than the evolutionary algorithm. In several cases, both algorithms obtained identical results, which also proves that the CLONALG algorithm can be considered as an effective tool for optimizing modern power structures, such as low voltage microgrids, including hybrid AC/DC microgrids. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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Review

Jump to: Editorial, Research

38 pages, 5013 KiB  
Review
Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors
by Paweł Piotrowski, Inajara Rutyna, Dariusz Baczyński and Marcin Kopyt
Energies 2022, 15(24), 9657; https://0-doi-org.brum.beds.ac.uk/10.3390/en15249657 - 19 Dec 2022
Cited by 12 | Viewed by 2733
Abstract
Power generation forecasts for wind farms, especially with a short-term horizon, have been extensively researched due to the growing share of wind farms in total power generation. Detailed forecasts are necessary for the optimization of power systems of various sizes. This review and [...] Read more.
Power generation forecasts for wind farms, especially with a short-term horizon, have been extensively researched due to the growing share of wind farms in total power generation. Detailed forecasts are necessary for the optimization of power systems of various sizes. This review and analytical paper is largely focused on a statistical analysis of forecasting errors based on more than one hundred papers on wind generation forecasts. Factors affecting the magnitude of forecasting errors are presented and discussed. Normalized root mean squared error (nRMSE) and normalized mean absolute error (nMAE) have been selected as the main error metrics considered here. A new and unique error dispersion factor (EDF) is proposed, being the ratio of nRMSE to nMAE. The variability of EDF depending on selected factors (size of wind farm, forecasting horizons, and class of forecasting method) has been examined. This is unique and original research, a novelty in studies on errors of power generation forecasts in wind farms. In addition, extensive quantitative and qualitative analyses have been conducted to assess the magnitude of forecasting error depending on selected factors (such as forecasting horizon, wind farm size, and a class of the forecasting method). Based on these analyses and a review of more than one hundred papers, a unique set of recommendations on the preferred content of papers addressing wind farm generation forecasts has been developed. These recommendations would make it possible to conduct very precise benchmarking meta-analyses of forecasting studies described in research papers and to develop valuable general conclusions concerning the analyzed phenomena. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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24 pages, 481 KiB  
Review
A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction
by Manisha Sawant, Rupali Patil, Tanmay Shikhare, Shreyas Nagle, Sakshi Chavan, Shivang Negi and Neeraj Dhanraj Bokde
Energies 2022, 15(21), 8107; https://0-doi-org.brum.beds.ac.uk/10.3390/en15218107 - 31 Oct 2022
Cited by 9 | Viewed by 1778
Abstract
With large penetration of wind power into power grids, the accurate prediction of wind power generation is becoming extremely important. Planning, scheduling, maintenance, trading and smooth operations all depend on the accuracy of the prediction. However due to the highly non-stationary and chaotic [...] Read more.
With large penetration of wind power into power grids, the accurate prediction of wind power generation is becoming extremely important. Planning, scheduling, maintenance, trading and smooth operations all depend on the accuracy of the prediction. However due to the highly non-stationary and chaotic behaviour of wind, accurate forecasting of wind power for different intervals of time becomes more challenging. Forecasting of wind power generation over different time spans is essential for different applications of wind energy. Recent development in this research field displays a wide spectrum of wind power prediction methods covering different prediction horizons. A detailed review of recent research achievements, performance, and information about possible future scope is presented in this article. This paper systematically reviews long term, short term and ultra short term wind power prediction methods. Each category of forecasting methods is further classified into four subclasses and a comparative analysis is presented. This study also provides discussions of recent development trends, performance analysis and future recommendations. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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31 pages, 908 KiB  
Review
A Review of Auto-Regressive Methods Applications to Short-Term Demand Forecasting in Power Systems
by Rafał Czapaj, Jacek Kamiński and Maciej Sołtysik
Energies 2022, 15(18), 6729; https://0-doi-org.brum.beds.ac.uk/10.3390/en15186729 - 14 Sep 2022
Cited by 5 | Viewed by 1384
Abstract
The paper conducts a literature review of applications of autoregressive methods to short-term forecasting of power demand. This need is dictated by the advancement of modern forecasting methods and their achievement in good forecasting efficiency in particular. The annual effectiveness of forecasting power [...] Read more.
The paper conducts a literature review of applications of autoregressive methods to short-term forecasting of power demand. This need is dictated by the advancement of modern forecasting methods and their achievement in good forecasting efficiency in particular. The annual effectiveness of forecasting power demand for the Polish National Power Grid for the next day is approx. 1%; therefore, the main objective of the review is to verify whether it is possible to improve efficiency while maintaining the minimum financial outlays and time-consuming efforts. The methods that fulfil these conditions are autoregressive methods; therefore, the paper focuses on autoregressive methods, which are less time-consuming and, as a result, cheaper in development and applications. The prepared review ranks the forecasting models in terms of the forecasting effectiveness achieved in the literature on the subject, which enables the selection of models that may improve the currently achieved effectiveness of the transmission system operator. Due to the applied approach, a transparent set of forecasting methods and models was obtained, in addition to knowledge about their potential in the context of the needs for short-term forecasting of electricity demand in the national power system. The articles in which the MAPE error was used to assess the quality of short-term forecasts were analyzed. The investigation included 47 articles, several dozen forecasting methods, and 264 forecasting models. The articles date from 1997 and, apart from the autoregressive methods, also include the methods and models that use explanatory variables (non-autoregressive ones). The input data used come from the period 1998–2014. The analysis included 25 power systems located on four continents (Asia, Europe, North America, and Australia) that were published by 44 different research teams. The results of the review show that in the autoregressive methods applied to forecasting short-term power demand, there is a potential to improve forecasting effectiveness in power systems. The most promising prognostic models using the autoregressive approach, based on the review, include Fuzzy Logic, Artificial Neural Networks, Wavelet Artificial Neural Networks, Adaptive Neurofuse Inference Systems, Genetic Algorithms, Fuzzy Regression, and Data Envelope Analysis. These methods make it possible to achieve the efficiency of short-term forecasting of electricity demand with hourly resolution at the level below 1%, which confirms the assumption made by the authors about the potential of autoregressive methods. Other forecasting models, the effectiveness of which is high, may also prove useful in forecasting by electricity system operators. The paper also discusses the classical methods of Artificial Intelligence, Data Mining, Big Data, and the state of research in short-term power demand forecasting in power systems using autoregressive and non-autoregressive methods and models. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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