Submit to Special Issue Submit Abstract to Special Issue Review for Energies Edit a Special Issue

Journal Browser

Special Issue "AI-Based Forecasting Models for Renewable Energy Management"

Special Issue Editors
Special Issue Information
Keywords
Published Papers

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: 30 June 2023 | Viewed by 4661

Share This Special Issue

Special Issue Editors

Dr. Tong Niu

E-Mail Website
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

E-Mail Website
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

Dr. Pei Du

E-Mail Website
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

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 2200 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.

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

Published Papers (5 papers)

Download All Papers

Research

Open AccessArticle

Artificial Intelligence in Wind Speed Forecasting: A Review

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 - 04 Mar 2023

Viewed by 370

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)

► Show Figures

Figure 1

Open AccessArticle

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

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

Viewed by 470

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)

► Show Figures

Figure 1

Open AccessArticle

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

Izzuddin Fathin Azhar

Lesnanto Multa Putranto

and

Roni Irnawan

Energies 2022, 15(21), 8241; https://doi.org/10.3390/en15218241 - 04 Nov 2022

Cited by 1 | Viewed by 633

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)

► Show Figures

Figure 1

Open AccessArticle

Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection

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 3 | Viewed by 1339

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)

► Show Figures

Graphical abstract

Open AccessArticle

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

and

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

Viewed by 996

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)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Special Issue "AI-Based Forecasting Models for Renewable Energy Management"

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (5 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI