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Advanced Forecasting Methods for Sustainable Power Grid

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

Deadline for manuscript submissions: closed (20 February 2025) | Viewed by 13370

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Dr. Cristina Ventura

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Guest Editor

DIEEI—Electrical Electronic and Computer Engineering, University of Catania, 95125 Catania, Italy
Interests: photovoltaic systems; photovoltaic and solar forecasting; photovoltaic/thermal systems; floating photovoltaic systems, photovoltaic systems monitoring; fault detection in photovoltaic systems; distributed photovoltaic resources; renewable energy communities
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Dr. Santi Agatino Rizzo

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Guest Editor

Department of Electrical Engineering and Computer Science, University of Catania, 95125 Catania, Italy
Interests: power electronics; power systems; applied optimization; applied machine learning; reliability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There is currently a large deployment of smart power grid systems that include various renewable energy sources, such as photovoltaic and wind energy. These renewable energy sources could have considerable impacts on power grid systems from both the technical and environmental sides. The generated renewable energy can cause discontinuity of energy production due to the non-programmable and unpredictable nature of renewable sources. In fact, the output of plants powered by non-programmable renewable energy sources (NPRESs) significantly changes the hourly pattern of zonal loads that need to be met by conventional generation plants. Thus, NPRESs introduce a stochastic component into the electricity demand related to the inherent variability of weather conditions, making the residual electricity load increasingly intermittent and harder to predict. As a result, the high penetration of NPRESs plants results in increasing imbalances between demand and generation and an increasing difficulty in building up the reserve margins needed to manage the randomness of the load, while providing security and stability to the grid. For this reason, there have been increasing efforts by the research community to establish accurate forecasting systems. This Special Issue aims to present advanced forecasting methods with applications that cover various practical challenges in sustainable power grids.

Topics to be covered in this Special Issue include but are not limited to the following:
• Forecasting of PV and wind power generation;
• Energy demand forecasting;
• Forecast models for wind speed and solar radiations;
• Forecast models for grid connected MPPT;
• Electric vehicle load forecasting;
• Electricity price forecasting;
• Forecasting techniques for smart grids;
• Artificial intelligence and data-driven approaches;
• Application of forecasting techniques in power systems;
• Anomalies and faults prediction.

Dr. Cristina Ventura
Dr. Santi Agatino Rizzo
Guest Editors

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Keywords

renewable forecasting
renewable energy sources
solar radiation forecasting
wind speed forecasting
fault detection
power forecasting
MPPT forecasting
artificial intelligence

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Related Special Issue

Advanced Forecasting Methods for Sustainable Power Grid: 2nd Edition in Energies

Published Papers (11 papers)

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Research

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28 pages, 2575 KiB

Open AccessArticle

Bi-Level Resilience-Oriented Sitting and Sizing of Energy Hubs in Electrical, Thermal and Gas Networks Considering Energy Management System

by Dhafer M. Dahis, Seyed Saeedallah Mortazavi, Mahmood Joorabian and Alireza Saffarian

Energies 2025, 18(10), 2569; https://doi.org/10.3390/en18102569 - 15 May 2025

Viewed by 136

Abstract

In this article, the planning and energy administration of energy hubs in electric, thermal and gas networks are presented, considering the resilience of the system against natural phenomena like floods and earthquakes. Each hub consists of bio-waste, wind and solar renewable units. These include non-renewable units such as boilers and combined heat and power (CHP) units. Compressed air and thermal energy storage are used in each hub. The design is formed as a bi-level optimization framework. In the upper level of the scheme, the energy management of networks bound to system resiliency is provided. This considers the minimization of annual operating and resilience costs based on optimal power flow equations in networks. In the lower-level model, the planning (placement and sizing) of hubs is considered. This minimizes the total building and operation costs of hubs based on the operation-planning equations for power supplies and storages. Scenario-based stochastic optimization models are used to determine the uncertainties of demand, the power of renewable systems, energy price and the accessibility of distribution networks’ elements against natural disasters. In this study, the Karush–Kuhn–Tucker technique is used to extract the single-level formulation. A numerical report for case studies verifies the potential of the plan to enhance the economic, operation and resilience status of networks with energy administration and the optimal planning of hubs in the mentioned networks. By determining the optimal capacity for resources and storage in the hubs located in the optimal places and the optimal energy administration of the hubs, the economic, exploitation and resilience situation of the networks are improved by about 27.1%, 97.7% and 23–50%, respectively, compared to load flow studies. Full article

(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid)

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18 pages, 4975 KiB

Open AccessArticle

Nonparametric Probabilistic Prediction of Ultra-Short-Term Wind Power Based on MultiFusion–ChronoNet–AMC

by Yan Yan, Yong Qian and Yan Zhou

Energies 2025, 18(7), 1646; https://doi.org/10.3390/en18071646 - 25 Mar 2025

Viewed by 217

Abstract

Accurate forecasting is crucial for enhancing the flexibility and controllability of power grids. Traditional forecasting methods mainly focus on modeling based on a single data source, which leads to an inability to fully capture the underlying relationships in wind power data. In addition, current models often lack dynamic adaptability to data characteristics, resulting in lower prediction accuracy and reliability under different time periods or weather conditions. To address the aforementioned issues, an ultra-short-term hybrid probabilistic prediction model based on MultiFusion, ChronoNet, and adaptive Monte Carlo (AMC) is proposed in this paper. By combining multi-source data fusion and a multiple-gated structure, the nonlinear characteristics and uncertainties of wind power under various input conditions are effectively captured by this model. Additionally, the AMC method is applied in this paper to provide comprehensive, accurate, and flexible ultra-short-term probabilistic predictions. Ultimately, experiments are conducted on multiple datasets, and the results show that the proposed model not only improves the accuracy of deterministic prediction but also enhances the reliability of probabilistic prediction intervals. Full article

(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid)

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17 pages, 9263 KiB

Open AccessArticle

Short-Term Photovoltaic Power Forecasting Based on the VMD-IDBO-DHKELM Model

by Shengli Wang, Xiaolong Guo, Tianle Sun, Lihui Xu, Jinfeng Zhu, Zhicai Li and Jinjiang Zhang

Energies 2025, 18(2), 403; https://doi.org/10.3390/en18020403 - 17 Jan 2025

Viewed by 777

Abstract

A short-term photovoltaic power forecasting method is proposed, integrating variational mode decomposition (VMD), an improved dung beetle algorithm (IDBO), and a deep hybrid kernel extreme learning machine (DHKELM). First, the weather factors less relevant to photovoltaic (PV) power generation are filtered using the Spearman correlation coefficient. Historical data are then clustered into three categories—sunny, cloudy, and rainy days—using the K-means algorithm. Next, the original PV power data are decomposed through VMD. A DHKELM-based combined prediction model is developed for each component of the decomposition, tailored to different weather types. The model’s hyperparameters are optimized using the IDBO. The final power forecast is determined by combining the outcomes of each individual component. Validation is performed using actual data from a PV power plant in Australia and a PV power station in Kashgar, China demonstrates. Numerical evaluation results show that the proposed method improves the Mean Absolute Error (MAE) by 3.84% and the Root-Mean-Squared Error (RMSE) by 3.38%, confirming its accuracy. Full article

(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid)

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16 pages, 5601 KiB

Open AccessArticle

An Intelligent SARIMAX-Based Machine Learning Framework for Long-Term Solar Irradiance Forecasting at Muscat, Oman

by Mazhar Baloch, Mohamed Shaik Honnurvali, Adnan Kabbani, Touqeer Ahmed Jumani and Sohaib Tahir Chauhdary

Energies 2024, 17(23), 6118; https://doi.org/10.3390/en17236118 - 5 Dec 2024

Cited by 1 | Viewed by 1068

Abstract

The intermittent nature of renewable energy sources (RES) restricts their widespread applications and reliability. Nevertheless, with advancements in the field of artificial intelligence, we can predict the variations in parameters such as wind speed and solar irradiance for the short, medium and long terms. As such, this research attempts to develop a machine learning (ML)-based framework for predicting solar irradiance at Muscat, Oman. The developed framework offers a methodological way to choose an appropriate machine learning model for long-term solar irradiance forecasting using Python’s built-in libraries. The five different methods, named linear regression (LR), seasonal autoregressive integrated moving average with exogenous variables (SARIMAX), support vector regression (SVR), Prophet, k-nearest neighbors (k-NN), and long short-term memory (LSTM) network are tested for a fair comparative analysis based on some of the most widely used performance evaluation metrics, such as the mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R²) score. The dataset utilized for training and testing in this research work includes 24 years of data samples (from 2000 to 2023) for solar irradiance, wind speed, humidity, and ambient temperature. Before splitting the data into training and testing, it was pre-processed to impute the missing data entries. Afterward, data scaling was conducted to standardize the data to a common scale, which ensures uniformity across the dataset. The pre-processed dataset was then split into two parts, i.e., training (from 2000 to 2019) and testing (from 2020 to 2023). The outcomes of this study revealed that the SARIMAX model, with an MSE of 0.0746, MAE of 0.2096, and an R² score of 0.9197, performs better than other competitive models under identical datasets, training/testing ratios, and selected features. Full article

(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid)

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12 pages, 2529 KiB

Open AccessArticle

Evaluation and Long-Term Prediction of Annual Wind Farm Energy Production

by Seunggun Hyun and Youn Cheol Park

Energies 2024, 17(21), 5332; https://doi.org/10.3390/en17215332 - 26 Oct 2024

Viewed by 1162

Abstract

A comparison and evaluation of the AEP(Annual Energy Production) of a wind farm were conducted in this study with a feasibility study and using the actual operation data from the S wind farm on Jeju Island from January 2020 to December 2022. The free wind speed data were selected from the data measured from a nacelle anemometer, the correlation equation between wind speed and AEP was obtained, and the annual average wind speed for the past 20 years was predicted using the MCP method. As a result, comparing the AEP from the operation data with that estimated in the feasibility study, we found that the AEP was reduced by approximately 2.40% in 2020 and 12.14% in 2021, and increased by 6.76% in 2022. The wind speeds over the 20-year lifetimes of the wind turbines were obtained, and the AEP that could be generated at the S wind farm indicated that it could be used for operation. In the future, the S wind farm will operate at between 25% and 30% availability for the remaining 17 years of operation. If the availability falls below 25%, there will be a need to check the reasons for the deterioration of wind turbine performance and the frequency of failures. Full article

(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid)

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24 pages, 5740 KiB

Open AccessArticle

Advanced Optimal System for Electricity Price Forecasting Based on Hybrid Techniques

by Hua Luo and Yuanyuan Shao

Energies 2024, 17(19), 4833; https://doi.org/10.3390/en17194833 - 26 Sep 2024

Viewed by 1146

Abstract

In the context of the electricity sector’s liberalization and deregulation, the accurate forecasting of electricity prices has emerged as a crucial strategy for market participants and operators to minimize costs and maximize profits. However, their effectiveness is hampered by the variable temporal characteristics of real-time electricity prices and a wide array of influencing factors. These challenges hinder a single model’s ability to discern the regularity, thereby compromising forecast precision. This study introduces a novel hybrid system to enhance forecast accuracy. Firstly, by employing an advanced decomposition technique, this methodology identifies different variation features within the electricity price series, thus bolstering feature extraction efficiency. Secondly, the incorporation of a novel multi-objective intelligent optimization algorithm, which utilizes two objective functions to constrain estimation errors, facilitates the optimal integration of multiple deep learning models. The case study uses electricity market data from Australia and Singapore to validate the effectiveness of the algorithm. The forecast results indicate that the hybrid short-term electricity price forecasting system proposed in this paper exhibits higher prediction accuracy compared to traditional single-model predictions, with MAE values of 7.3363 and 4.2784, respectively. Full article

(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid)

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22 pages, 4605 KiB

Open AccessArticle

Probabilistic Analysis of Green Hydrogen Production from a Mix of Solar and Wind Energy

by Agnieszka Dudziak, Arkadiusz Małek, Andrzej Marciniak, Jacek Caban and Jarosław Seńko

Energies 2024, 17(17), 4387; https://doi.org/10.3390/en17174387 - 2 Sep 2024

Cited by 2 | Viewed by 1427

Abstract

This article describes an example of using the measurement data from photovoltaic systems and wind turbines to perform practical probabilistic calculations around green hydrogen generation. First, the power generated in one month by a ground-mounted photovoltaic system with a peak power of 3 MWp is described. Using the Metalog family of probability distributions, the probability of generating selected power levels corresponding to the amount of green hydrogen produced is calculated. Identical calculations are performed for the simulation data, allowing us to determine the power produced by a wind turbine with a maximum power of 3.45 MW. After interpolating both time series of the power generated by the renewable energy sources to a common sampling time, they are summed. For the sum of the power produced by the photovoltaic system and the wind turbine, the probability of generating selected power levels corresponding to the amount of green hydrogen produced is again calculated. The presented calculations allow us to determine, with probability distribution accuracy, the amount of hydrogen generated from the energy sources constituting a mix of photovoltaics and wind. The green hydrogen production model includes the hardware and the geographic context. It can be used to determine the preliminary assumptions related to the production of large amounts of green hydrogen in selected locations. The calculations presented in this article are a practical example of Business Intelligence. Full article

(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid)

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24 pages, 5692 KiB

Open AccessArticle

Short-Term Forecasts of Energy Generation in a Solar Power Plant Using Various Machine Learning Models, along with Ensemble and Hybrid Methods

by Paweł Piotrowski and Marcin Kopyt

Energies 2024, 17(17), 4234; https://doi.org/10.3390/en17174234 - 24 Aug 2024

Viewed by 1063

Abstract

High-quality short-term forecasts of electrical energy generation in solar power plants are crucial in the dynamically developing sector of renewable power generation. This article addresses the issue of selecting appropriate (preferred) methods for forecasting energy generation from a solar power plant within a 15 min time horizon. The effectiveness of various machine learning methods was verified. Additionally, the effectiveness of proprietary ensemble and hybrid methods was proposed and examined. The research also aimed to determine the appropriate sets of input variables for the predictive models. To enhance the performance of the predictive models, proprietary additional input variables (feature engineering) were constructed. The significance of individual input variables was examined depending on the predictive model used. This article concludes with findings and recommendations regarding the preferred predictive methods. Full article

(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid)

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23 pages, 5845 KiB

Open AccessArticle

A Novel Twin Support Vector Regression Model for Wind Speed Time-Series Interval Prediction

by Xinyue Fu, Zhongkai Feng, Xinru Yao and Wenjie Liu

Energies 2023, 16(15), 5656; https://doi.org/10.3390/en16155656 - 27 Jul 2023

Cited by 5 | Viewed by 1697

Abstract

Although the machine-learning model demonstrates high accuracy in wind speed prediction, it struggles to accurately depict the fluctuation range of the predicted values due to the inherent uncertainty in wind speed sequences. To address this limitation and enhance the reliability, we propose an effective wind speed interval prediction model that combines twin support vector regression (TSVR), variational mode decomposition (VMD), and the slime mould algorithm (SMA). In our methodology, the complex wind speed series is decomposed into multiple relatively stable subsequences using the VMD method. The principal component and residual series are then subject to interval prediction using the TSVR model, while the remaining components undergo point prediction. The SMA method is employed to search for optimal parameter combinations. The prediction interval of wind speed is obtained by aggregating the forecasting results of all TSVR models for each subseries. Our proposed model has demonstrated superior performance in various applications. It ensures that the wind speed value falls within the designated interval range while achieving the narrowest prediction interval. For instance, in the spring dataset with 1-period, we obtained a predicted interval with a prediction intervals coverage probability (PICP) value of 0.9791 and prediction interval normalized range width (PINRW) value of 0.0641. This outperforms other comparative models and significantly enhances its practical application value. After adding the residual interval prediction model, the reliability of the prediction interval is significantly improved. As a result, this study presents a novel twin support vector regression model as a valuable approach for multi-step wind speed interval prediction. Full article

(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid)

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Review

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25 pages, 1979 KiB

Open AccessFeature PaperReview

Comprehensive Review of Building Energy Management Models: Grid-Interactive Efficient Building Perspective

by Anujin Bayasgalan, Yoo Shin Park, Seak Bai Koh and Sung-Yong Son

Energies 2024, 17(19), 4794; https://doi.org/10.3390/en17194794 - 25 Sep 2024

Cited by 3 | Viewed by 2904

Abstract

Energy management models for buildings have been designed primarily to reduce energy costs and improve efficiency. However, the focus has recently shifted to GEBs with a view toward balancing energy supply and demand while enhancing system flexibility and responsiveness. This paper provides a comprehensive comparative analysis of GEBs and other building energy management models, categorizing their features into internal and external dimensions. This review highlights the evolution of building models, including intelligent buildings, smart buildings, green buildings, and zero-energy buildings, and introduces eight distinct features of GEBs related to their efficient, connected, smart, and flexible aspects. The analysis is based on an extensive literature review and a detailed comparison of building models across the aforementioned features. GEBs prioritize interaction with the power grid, which distinguishes them from traditional models focusing on internal efficiency and occupant comfort. This paper also discusses the technological components and research trends associated with GEBs, providing insights into their development and potential evolution in the context of sustainable and efficient building design. Full article

(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid)

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Other

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51 pages, 5729 KiB

Open AccessSystematic Review

Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review

by Fernando Venâncio Mucomole, Carlos Augusto Santos Silva and Lourenço Lázaro Magaia

Energies 2025, 18(6), 1460; https://doi.org/10.3390/en18061460 - 17 Mar 2025

Viewed by 555

Abstract

To maximize photovoltaic (PV) production, it is necessary to estimate the amount of solar radiation that is available on Earth’s surface, as it can occasionally vary. This study aimed to systematize the parametric forecast (PF) of solar energy over time, adopting the validation of estimates by machine learning models (MLMs), with highly complex analyses as inclusion criteria and studies not validated in the short or long term as exclusion criteria. A total of 145 scholarly sources were examined, with a value of 0.17 for bias risk. Four components were analyzed: atmospheric, temporal, geographic, and spatial components. These quantify dispersed, absorbed, and reflected solar energy, causing energy to fluctuate when it arrives at the surface of a PV plant. The results revealed strong trends towards the adoption of artificial neural network (ANN), random forest (RF), and simple linear regression (SLR) models for a sample taken from the Nipepe station in Niassa, validated by a PF model with errors of 0.10, 0.11, and 0.15. The included studies’ statistically measured parameters showed high trends of dependence on the variability in transmittances. The synthesis of the results, hence, improved the accuracy of the estimations produced by MLMs, making the model applicable to any reality, with a very low margin of error for the calculated energy. Most studies adopted large time intervals of atmospheric parameters. Applying interpolation models can help extrapolate short scales, as their inference and treatment still require a high investment cost. Due to the need to access the forecasted energy over land, this study was funded by CS–OGET. Full article

(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid)

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