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AI-Based Forecasting Models for Renewable Energy Management

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 33405

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Special Issue Editors

Dr. Tong Niu

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Guest Editor

Center for Energy, Environment and Economy Research, Zhengzhou University, Zhengzhou 450001, China
Interests: renewable energy forecasting; deep learning; optimization algorithm

Prof. Dr. Mingjian Cui

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Guest Editor

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: renewable energy; power system operation; power system cybersecurity; power system data analytics; machine learning
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Dr. Pei Du

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Guest Editor

School of Management, Xi’an Jiaotong University, Xi’an 710049, China
Interests: artificial intelligence; optimization algorithms; machine learning; time series forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Guest Editor is inviting submissions to a Special Issue of Energies on the “AI-Based Forecasting Models for Renewable Energy Management”.

In the context of carbon-neutral, the focus of energy development and utilization at a global scale has been shifting from conventional energy, such as coal and oil, to renewable energy, aiming to alleviate the adverse effects of the greenhouse effect. As an important research direction of renewable energy management, renewable energy forecasting is of great significance to realize the safe operation and scientific dispatch of the power system.

At present, artificial intelligence (AI)-related technologies (such as deep learning, heuristic algorithm, reinforcement learning and transfer learning) are in the ascendant. AI has been favored in other fields (such as financial time series forecasting and fault diagnosis) due to its adaptive learning ability and excellent generalization ability. Therefore, research on how to scientifically and effectively apply AI-based models and algorithms to renewable energy forecasting is a promising direction. This Special Issue expects scholars in the field to make significant contributions and advance the field.

This Special Issue aims to exploit the advantages of AI in the field of renewable energy forecasting and drive innovation in renewable energy forecasting methods. Topics of interest for publication include, but are not limited to:

Renewable energy forecasting;
Wind power integration;
Photovoltaic system;
Tidal power generation;
Biomass energy;
Wave energy;
Data analytics;
Neural network;
Deep learning;
Optimization;
Hybrid model;
Transfer learning;
Probabilistic forecasting;
Interval prediction;
Attention mechanism;
Feature extraction.

Dr. Tong Niu
Prof. Dr. Mingjian Cui
Dr. Pei Du
Guest Editors

Manuscript Submission Information

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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.

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Keywords

renewable energy forecasting
wind power integration
photovoltaic system
tidal power generation
biomass energy
wave energy
data analytics
neural network
deep learning
optimization
hybrid model
transfer learning
probabilistic forecasting
interval prediction
attention mechanism
feature extraction

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Published Papers (9 papers)

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Research

23 pages, 4576 KiB

Open AccessArticle

A Method for the Characterization of the Energy Demand Aggregate Based on Electricity Data Provided by AMI Systems and Metering in Substations

by Oscar A. Bustos-Brinez, Javier E. Duarte, Alvaro Zambrano-Pinto, Fabio A. González and Javier Rosero-Garcia

Energies 2024, 17(1), 87; https://doi.org/10.3390/en17010087 - 22 Dec 2023

Cited by 1 | Viewed by 1040

Abstract

This paper presents a methodology developed to perform the processing, analysis, and characterization of AMI measurement data from the substations of three network operators of the Colombian electrical grid. This methodology includes the analysis of the data, which presents the sources of information used by the model, along with the preprocessing and exploratory analysis of the substations data. It also includes the formulation of the data reconstruction method, which uses a constrained optimization model to characterize the substations, based on the different behaviors of the end users of the Colombian electrical grid. In addition to the proposed methodology, the results of its application to the data provided by the operators are provided. These results show the capacity of the proposed methodology to adequately identify the most common behaviors of the users in a given area and characterize most of the energy demand profiles of each substation. Full article

(This article belongs to the Special Issue AI-Based Forecasting Models for Renewable Energy Management)

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15 pages, 6500 KiB

Open AccessArticle

Time-Series Power Forecasting for Wind and Solar Energy Based on the SL-Transformer

by Jian Zhu, Zhiyuan Zhao, Xiaoran Zheng, Zhao An, Qingwu Guo, Zhikai Li, Jianling Sun and Yuanjun Guo

Energies 2023, 16(22), 7610; https://doi.org/10.3390/en16227610 - 16 Nov 2023

Cited by 14 | Viewed by 3548

Abstract

As the urgency to adopt renewable energy sources escalates, so does the need for accurate forecasting of power output, particularly for wind and solar power. Existing models often struggle with noise and temporal intricacies, necessitating more robust solutions. In response, our study presents the SL-Transformer, a novel method rooted in the deep learning paradigm tailored for green energy power forecasting. To ensure a reliable basis for further analysis and modeling, free from noise and outliers, we employed the SG filter and LOF algorithm for data cleansing. Moreover, we incorporated a self-attention mechanism, enhancing the model’s ability to discern and dynamically fine-tune input data weights. When benchmarked against other premier deep learning models, the SL-Transformer distinctly outperforms them. Notably, it achieves a near-perfect

R^{2}

value of 0.9989 and a significantly low SMAPE of 5.8507% in wind power predictions. For solar energy forecasting, the SL-Transformer has achieved a SMAPE of 4.2156%, signifying a commendable improvement of 15% over competing models. The experimental results demonstrate the efficacy of the SL-Transformer in wind and solar energy forecasting. Full article

(This article belongs to the Special Issue AI-Based Forecasting Models for Renewable Energy Management)

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23 pages, 3171 KiB

Open AccessArticle

A Unified Graph Formulation for Spatio-Temporal Wind Forecasting

by Lars Ødegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro and Paal Engelstad

Energies 2023, 16(20), 7179; https://doi.org/10.3390/en16207179 - 20 Oct 2023

Cited by 1 | Viewed by 1729

Abstract

With the rapid adoption of wind energy globally, there is a need for accurate short-term forecasting systems to improve the reliability and integration of such energy resources on a large scale. While most spatio-temporal forecasting systems comprise distinct components to learn spatial and temporal dependencies separately, this paper argues for an approach to learning spatio-temporal information jointly. Many time series forecasting systems also require aligned input information and do not naturally facilitate irregular data. Research is therefore required to investigate methodologies for forecasting in the presence of missing or corrupt measurements. To help combat some of these challenges, this paper studied a unified graph formulation. With the unified formulation, a graph neural network (GNN) was used to extract spatial and temporal dependencies simultaneously, in a single update, while also naturally facilitating missing data. To evaluate the proposed unified approach, the study considered hour-ahead wind speed forecasting in the North Sea under different amounts of missing data. The framework was compared against traditional spatio-temporal architectures that used GNNs together with temporal long short-term memory (LSTM) and Transformer or Autoformer networks, along with the imputation of missing values. The proposed framework outperformed the traditional architectures, with absolute errors of around 0.73–0.90 m per second, when subject to 0–80% of missing input data. The unified graph approach was also better at predicting large changes in wind speed, with an additional 10-percentage-point improvement over the second-best model. Overall, this paper investigated a novel methodology for spatio-temporal wind speed forecasting and showed how the proposed unified graph formulation achieved competitive results compared to more traditional GNN-based architectures. Full article

(This article belongs to the Special Issue AI-Based Forecasting Models for Renewable Energy Management)

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24 pages, 4492 KiB

Open AccessArticle

On the Added Value of State-of-the-Art Probabilistic Forecasting Methods Applied to the Optimal Scheduling of a PV Power Plant with Batteries

by Rafael Alvarenga, Hubert Herbaux and Laurent Linguet

Energies 2023, 16(18), 6543; https://doi.org/10.3390/en16186543 - 11 Sep 2023

Cited by 2 | Viewed by 1447

Abstract

Efforts have been made to develop methods to quantify the uncertainty related to the power production of renewable energy power plants, allowing producers to ensure more reliable engagements related to their future power delivery. Even though diverse probabilistic approaches have been proposed in the literature, giving promising results, the added value of adopting such methods is still unclear. This paper comprehensively assesses the profits obtained when probabilistic forecasts generated with state-of-the-art methods are fed into a stochastic programming decision-making model to optimally schedule an existing PV power plant operating in highly unstable weather. Different representative probabilistic forecasting methods are assessed and compared against deterministic forecasts submitted to varying levels of uncertainty, used to schedule the power plant in standalone operation and hybrid operation with batteries. The main findings reveal that although probabilistic forecasts offer potential benefits in handling uncertainty and utilizing battery assets to mitigate forecast errors, deterministic forecasts consistently yield higher profits than probabilistic forecasts. It is shown that this disparity is primarily attributed to the scenario diversity present in probabilistic forecasts, which leads to over-conservative decisions and the loss of temporal correlation with PV power production variations, resulting in increased imbalances and penalties. Full article

(This article belongs to the Special Issue AI-Based Forecasting Models for Renewable Energy Management)

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28 pages, 8384 KiB

Open AccessArticle

Artificial Intelligence in Wind Speed Forecasting: A Review

by Sandra Minerva Valdivia-Bautista, José Antonio Domínguez-Navarro, Marco Pérez-Cisneros, Carlos Jesahel Vega-Gómez and Beatriz Castillo-Téllez

Energies 2023, 16(5), 2457; https://doi.org/10.3390/en16052457 - 4 Mar 2023

Cited by 49 | Viewed by 8530

Abstract

Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric. The research results indicate that artificial neural network (ANN)-based models can provide accurate wind forecasting and essential information about the specific location of potential wind use for a power plant by understanding the future wind speed values. Full article

(This article belongs to the Special Issue AI-Based Forecasting Models for Renewable Energy Management)

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30 pages, 3612 KiB

Open AccessArticle

Renewable Energy Forecasting Based on Stacking Ensemble Model and Al-Biruni Earth Radius Optimization Algorithm

by Abdulrahman A. Alghamdi, Abdelhameed Ibrahim, El-Sayed M. El-Kenawy and Abdelaziz A. Abdelhamid

Energies 2023, 16(3), 1370; https://doi.org/10.3390/en16031370 - 28 Jan 2023

Cited by 17 | Viewed by 3786

Abstract

Introduction: Wind speed and solar radiation are two of the most well-known and widely used renewable energy sources worldwide. Coal, natural gas, and petroleum are examples of fossil fuels that are not replenished and are thus non-renewable energy sources due to their high carbon content and the methods by which they are generated. To predict energy production of renewable sources, researchers use energy forecasting techniques based on the recent advances in machine learning approaches. Numerous prediction methods have significant drawbacks, including high computational complexity and inability to generalize for various types of sources of renewable energy sources. Methodology: In this paper, we proposed a novel approach capable of generalizing the prediction accuracy for both wind speed and solar radiation forecasting data. The proposed approach is based on a new optimization algorithm and a new stacked ensemble model. The new optimization algorithm is a hybrid of Al-Biruni Earth Radius (BER) and genetic algorithm (GA) and it is denoted by the GABER optimization algorithm. This algorithm is used to optimize the parameters of the proposed stacked ensemble model to boost the prediction accuracy and to improve the generalization capability. Results: To evaluate the proposed approach, several experiments are conducted to study its effectiveness and superiority compared to other optimization methods and forecasting models. In addition, statistical tests are conducted to assess the significance and difference of the proposed approach. The recorded results proved the proposed approach’s superiority, effectiveness, generalization, and statistical significance when compared to state-of-the-art methods. Conclusions: The proposed approach is capable of predicting both wind speed and solar radiation with better generalization. Full article

(This article belongs to the Special Issue AI-Based Forecasting Models for Renewable Energy Management)

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20 pages, 9571 KiB

Open AccessArticle

Development of PMU-Based Transient Stability Detection Methods Using CNN-LSTM Considering Time Series Data Measurement

by Izzuddin Fathin Azhar, Lesnanto Multa Putranto and Roni Irnawan

Energies 2022, 15(21), 8241; https://doi.org/10.3390/en15218241 - 4 Nov 2022

Cited by 17 | Viewed by 2640

Abstract

The development of electric power systems has become more complex. Consequently, electric power systems are operating closer to their limits and are more susceptible to instability when a disturbance occurs. Transient stability problems are especially prevalent. In addition, the identification of transient stability is difficult to achieve in real time using the current measurement data. This research focuses on developing a convolutional neural network—long short-term memory (CNN-LSTM) model using historical data events to detect transient stability considering time-series measurement data. The model was developed by considering noise, delay, and loss in measurement data, line outage and variable renewable energy (VRE) integration scenarios. The model requires PMU measurements to provide high sampling rate time-series information. In addition, the effects of different numbers of PMUs were also simulated. The CNN-LSTM method was trained using a synthetic dataset produced using the DigSILENT PowerFactory simulation to represent the PMU measurement data. The IEEE 39 bus test system was used to simulate the model under different loading conditions. On the basis of the research results, the proposed CNN-LSTM model is able to detect stable and unstable conditions of transient stability only from the magnitude and angle of the bus voltage, without considering system parameter information on the network. The accuracy of transient stability detection reached above 99% in all scenarios. The CNN-LSTM method also required less computation time compared to CNN and conventional LSTM with the average computation times of 190.4, 4001.8 and 229.8 s, respectively. Full article

(This article belongs to the Special Issue AI-Based Forecasting Models for Renewable Energy Management)

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18 pages, 3405 KiB

Open AccessArticle

Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection

by Edna S. Solano, Payman Dehghanian and Carolina M. Affonso

Energies 2022, 15(19), 7049; https://doi.org/10.3390/en15197049 - 25 Sep 2022

Cited by 45 | Viewed by 5123

Abstract

Accurate solar radiation forecasting is essential to operate power systems safely under high shares of photovoltaic generation. This paper compares the performance of several machine learning algorithms for solar radiation forecasting using endogenous and exogenous inputs and proposes an ensemble feature selection method to choose not only the most related input parameters but also their past observations values. The machine learning algorithms used are: Support Vector Regression (SVR), Extreme Gradient Boosting (XGBT), Categorical Boosting (CatBoost) and Voting-Average (VOA), which integrates SVR, XGBT and CatBoost. The proposed ensemble feature selection is based on Pearson coefficient, random forest, mutual information and relief. Prediction accuracy is evaluated based on several metrics using a real database from Salvador, Brazil. Different prediction time-horizons are considered: 1 h, 2 h and 3 h ahead. Numerical results demonstrate that the proposed ensemble feature selection approach improves forecasting accuracy and that VOA performs better than the other algorithms in all prediction time horizons. Full article

(This article belongs to the Special Issue AI-Based Forecasting Models for Renewable Energy Management)

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20 pages, 6431 KiB

Open AccessArticle

A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection

by Kaitong Wu, Xiangang Peng, Zilu Li, Wenbo Cui, Haoliang Yuan, Chun Sing Lai and Loi Lei Lai

Energies 2022, 15(15), 5410; https://doi.org/10.3390/en15155410 - 27 Jul 2022

Cited by 12 | Viewed by 2620

Abstract

High precision short-term photovoltaic (PV) power prediction can reduce the damage associated with large-scale photovoltaic grid-connection to the power system. In this paper, a combination deep learning forecasting method based on variational mode decomposition (VMD), a fast correlation-based filter (FCBF) and bidirectional long short-term memory (BiLSTM) network is developed to minimize PV power forecasting error. In this model, VMD is used to extract the trend feature of PV power, then FCBF is adopted to select the optimal input-set to reduce the forecasting error caused by the redundant feature. Finally, the input-set is put into the BiLSTM network for training and testing. The performance of this model is tested by a case study using the public data-set provided by a PV station in Australia. Comparisons with common short-term PV power forecasting models are also presented. The results show that under the processing of trend feature extraction and feature selection, the proposed methodology provides a more stable and accurate forecasting effect than other forecasting models. Full article

(This article belongs to the Special Issue AI-Based Forecasting Models for Renewable Energy Management)

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