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Machine Learning and Deep Learning for Energy Systems II

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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 (30 September 2023) | Viewed by 13457

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Prof. Dr. Valentina E. Balas

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Automatics and Applied Software Department, Aurel Vlaicu University of Arad, 310130 Arad, Romania
Interests: intelligent systems; soft computing; fuzzy control; modeling and simulation; biometrics
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Special Issue Information

Dear Colleagues,

An energy system can be a combination of mechanical, chemical, and electrical, and it can cover various dimensions of energy types that include renewables and other alternative energy systems as well. High-scale advancement, however, is facing a critical decision-making crisis, as most energy systems are not able to satisfy the demand–supply ratio and performance optimization, do not know how to deal with performance efficiency, are less understanding of the impact of energy outcomes to the environment, and are not of use in the renewable energy front. Energy firms are generating huge data, both structured and unstructured. IoT alongside smart sensors are participating in the collection of massive data on energy production and consumption. As data are getting bigger and bigger, the number of challenges is also growing at a rate never seen before.

Recently, it has been noted that the machine learning and deep learning models are growing in popularity when it comes to handling big data for energy optimization, and decision-making processes. Moreover, a lot of prediction models proposed in the last two years based on machine learning and, very recently, deep learning have performed considerably well and led toward energy-data-related predictions. The reason is that in the case of extraction of functional dependencies from observations of energy-related projects, these data-driven models have experienced a leap in performance. Today, the scenarios are such that the machine learning, data science, and deep learning models are almost essential for predictive modeling of energy consumption and production rate maintenance, and, finally, accurate demand analysis with high speed. The proposed models now understand the functionalities of energy much better than earlier ones. In addition, machine learning, data science, and deep learning are providing considerable performance efficiency on renewable energy related projects as well. In fact, scientists have started to organize top-level conferences on deep learning technology adaptations on energy-related high-value projects.

This Special Issue aims to provide comprehensive coverage on cutting-edge research and state-of-the-art methods on machine learning, data science, and deep learning applications on energy-related projects. Authors are requested to submit papers on (but not limited to) the following topics:

Optimization of renewable energy using machine learning and deep learning;
Machine learning and deep learning models for mitigation of wind power fluctuation and methods for power generation;
Prediction of levelized cost of electricity;
Forecasting model for wind speed and hourly and daily solar radiation;
Predictive models for smart building with heating and cooling load prediction;
Saving energy using predictive models;
Prediction of hourly global solar irradiation;
Forecasting of PV power generation;
Performance evaluation of solar thermal energy systems;
Classifications using deep learning or advanced machine learning for power quality disturbances;
Electricity market price prediction using advanced machine learning;
Case study on combined applications of machine learning, IoT and big data for energy efficiency.

Prof. Dr. Valentina E. Balas
Guest Editor

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.

Keywords

optimization
prediction
performance evaluation
IoT
classification

Related Special Issue

Machine Learning and Deep Learning for Energy Systems in Energies (14 articles)

Published Papers (10 papers)

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Research

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23 pages, 4290 KiB

Open AccessArticle

Machine Learning-Based Automated Fault Detection and Diagnostics in Building Systems

by William Nelson and Christopher Dieckert

Energies 2024, 17(2), 529; https://doi.org/10.3390/en17020529 - 22 Jan 2024

Viewed by 1132

Abstract

Automated fault detection and diagnostics analysis in commercial building systems using machine learning (ML) can improve the building’s efficiency and conserve energy costs from inefficient equipment operation. However, ML can be challenging to implement in existing systems due to a lack of common data standards and because of a lack of building operators trained in ML techniques. Additionally, results from ML procedures can be complicated for untrained users to interpret. Boolean rule-based analysis is standard in current automated fault detection and diagnostics (AFDD) solutions but limits analysis to the rules defined and calibrated by energy engineers. Boolean rule-based analysis and ML can be combined to create an effective fault detection and diagnostics (FDD) tool. Three examples of ML’s advantages over rule-based analysis are explored by analyzing functional building equipment. ML can detect long-term faults in the system caused by a lack of system maintenance. It can also detect faults in system components with incomplete sets of sensors by modeling expected system operations and by making comparisons to actual system operations. An example of ML detecting a failure in a building is shown along with a demonstration of the soft decision boundaries of ML-based FDD compared to Boolean rule-based FDD analysis. The results from the three examples are used to demonstrate the strengths and weaknesses of using ML for AFDD analysis. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Energy Systems II)

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20 pages, 3243 KiB

Open AccessArticle

A Novel Approach for Evaluating Power Quality in Distributed Power Distribution Networks Using AHP and S-Transform

by Yin Chen, Zhenli Tang, Xiaofeng Weng, Min He, Guanghong Zhang, Ding Yuan and Tao Jin

Energies 2024, 17(2), 411; https://doi.org/10.3390/en17020411 - 14 Jan 2024

Viewed by 727

Abstract

As the penetration rate of new energy generation in distributed distribution networks continues to increase, the integration of numerous new energy power plants and associated power electronic devices presents challenges to the power quality of traditional power systems. Therefore, conducting power quality-related research in distribution networks is of significant importance for maintaining power system stability, safeguarding electrical equipment, and enhancing electrical safety. A framework for evaluating the overall power quality of new energy-penetrated distribution network systems based on the analytic hierarchy process (AHP) is proposed. This framework aggregates and calculates the global power quality index (GPQI) through averaging, thereby completing the assessment of power quality situations. By enhancing the computation speed of evaluation metrics through an improved S-transform and considering various disturbances such as diminished illumination, wind power disconnection, and high-current grounding, the GPQI values are used to assess power quality under diverse scenarios. Simulation and experimental results confirm the framework’s close alignment with real scenarios and its effectiveness in evaluating power quality within distribution networks. This method is crucial for maintaining power system stability, protecting electrical equipment, and enhancing overall electrical safety within distribution networks. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Energy Systems II)

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26 pages, 10030 KiB

Open AccessArticle

A Diagnostic Method for Open-Circuit Faults in DC Charging Stations Based on Improved S-Transform and LightGBM

by Yin Chen, Zhenli Tang, Xiaofeng Weng, Min He, Sheng Zhou, Ziqiang Liu and Tao Jin

Energies 2024, 17(2), 404; https://doi.org/10.3390/en17020404 - 13 Jan 2024

Viewed by 637

Abstract

The open-circuit fault in electric vehicle charging stations not only impacts the power quality of the electrical grid but also poses a threat to charging safety. Therefore, it is of great significance to study open-circuit fault diagnosis for ensuring the safe and stable operation of power grids and reducing the maintenance cost of charging stations. This paper addresses the multidimensional characteristics of open-circuit fault signals in charging stations and proposes a fault diagnosis method based on an improved S-transform and LightGBM. The method first utilizes improved incomplete S-transform and principal component analysis (PCA) to extract features of front- and back-stage faults separately. Subsequently, LightGBM is employed to classify the extracted features, ultimately achieving fault diagnosis. Simulation results demonstrate the method’s effectiveness in feature extraction, achieving an average diagnostic accuracy of 97.04% on the test dataset, along with notable noise resistance and real-time performance. Additionally, we designed an experimental platform for diagnosing open-circuit faults in DC charging station and collected experimental fault data. The results further validate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Energy Systems II)

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15 pages, 5343 KiB

Open AccessArticle

Predicting Steam Turbine Power Generation: A Comparison of Long Short-Term Memory and Willans Line Model

by Mostafa Pasandideh, Matthew Taylor, Shafiqur Rahman Tito, Martin Atkins and Mark Apperley

Energies 2024, 17(2), 352; https://doi.org/10.3390/en17020352 - 10 Jan 2024

Viewed by 745

Abstract

This study focuses on using machine learning techniques to accurately predict the generated power in a two-stage back-pressure steam turbine used in the paper production industry. In order to accurately predict power production by a steam turbine, it is crucial to consider the time dependence of the input data. For this purpose, the long-short-term memory (LSTM) approach is employed. Correlation analysis is performed to select parameters with a correlation coefficient greater than 0.8. Initially, nine inputs are considered, and the study showcases the superior performance of the LSTM method, with an accuracy rate of 0.47. Further refinement is conducted by reducing the inputs to four based on correlation analysis, resulting in an improved accuracy rate of 0.39. The comparison between the LSTM method and the Willans line model evaluates the efficacy of the former in predicting production power. The root mean square error (RMSE) evaluation parameter is used to assess the accuracy of the prediction algorithm used for the generator’s production power. By highlighting the importance of selecting appropriate machine learning techniques, high-quality input data, and utilising correlation analysis for input refinement, this work demonstrates a valuable approach to accurately estimating and predicting power production in the energy industry. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Energy Systems II)

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18 pages, 3936 KiB

Open AccessArticle

A Novel Electricity Theft Detection Strategy Based on Dual-Time Feature Fusion and Deep Learning Methods

by Qinyu Huang, Zhenli Tang, Xiaofeng Weng, Min He, Fang Liu, Mingfa Yang and Tao Jin

Energies 2024, 17(2), 275; https://doi.org/10.3390/en17020275 - 05 Jan 2024

Cited by 1 | Viewed by 863

Abstract

To enhance the accuracy of theft detection for electricity consumers, this paper introduces a novel strategy based on the fusion of the dual-time feature and deep learning methods. Initially, considering electricity-consumption features at dual temporal scales, the paper employs temporal convolutional networks (TCN) with a long short-term memory (LSTM) multi-level feature extraction module (LSTM-TCN) and deep convolutional neural network (DCNN) to parallelly extract features at these scales. Subsequently, the extracted features are coupled and input into a fully connected (FC) layer for classification, enabling the precise detection of theft users. To validate the method’s effectiveness, real electricity-consumption data from the State Grid Corporation of China (SGCC) is used for testing. The experimental results demonstrate that the proposed method achieves a remarkable detection accuracy of up to 94.7% during testing, showcasing excellent performance across various evaluation metrics. Specifically, it attained values of 0.932, 0.964, 0.948, and 0.986 for precision, recall, F1 score, and AUC, respectively. Additionally, the paper conducts a comparative analysis with mainstream theft identification approaches. In the comparison of training processes, the proposed method exhibits significant advantages in terms of identification accuracy and fitting degree. Moreover, with adjustments to the training set proportions, the proposed method shows minimal impact, indicating robustness. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Energy Systems II)

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23 pages, 3550 KiB

Open AccessArticle

Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms

by Mustafa Saglam, Catalina Spataru and Omer Ali Karaman

Energies 2023, 16(11), 4499; https://doi.org/10.3390/en16114499 - 02 Jun 2023

Cited by 7 | Viewed by 2035

Abstract

Medium Neural Networks (MNN), Whale Optimization Algorithm (WAO), and Support Vector Machine (SVM) methods are frequently used in the literature for estimating electricity demand. The objective of this study was to make an estimation of the electricity demand for Turkey’s mainland with the use of mixed methods of MNN, WAO, and SVM. Imports, exports, gross domestic product (GDP), and population data are used based on input data from 1980 to 2019 for mainland Turkey, and the electricity demands up to 2040 are forecasted as an output value. The performance of methods was analyzed using statistical error metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared, and Mean Square Error (MSE). The correlation matrix was utilized to demonstrate the relationship between the actual data and calculated values and the relationship between dependent and independent variables. The p-value and confidence interval analysis of statistical methods was performed to determine which method was more effective. It was observed that the minimum RMSE, MSE, and MAE statistical errors are 5.325 × 10⁻¹⁴, 28.35 × 10⁻²⁸, and 2.5 × 10⁻¹⁴, respectively. The MNN methods showed the strongest correlation between electricity demand forecasting and real data among all the applications tested. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Energy Systems II)

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12 pages, 1765 KiB

Open AccessEditor’s ChoiceArticle

Machine Learning Requirements for Energy-Efficient Virtual Network Embedding

by Xavier Hesselbach and David Escobar-Perez

Energies 2023, 16(11), 4439; https://doi.org/10.3390/en16114439 - 31 May 2023

Viewed by 880

Abstract

Network virtualization is a technology proven to be a key enabling a family of strategies in different targets, such as energy efficiency, economic revenue, network usage, adaptability or failure protection. Network virtualization allows us to adapt the needs of a network to new circumstances, resulting in greater flexibility. The allocation decisions of the demands onto the physical network resources impact the costs and the benefits. Therefore it is one of the major current problems, called virtual network embedding (VNE). Many algorithms have been proposed recently in the literature to solve the VNE problem for different targets. Due to the current successful rise of artificial intelligence, it has been widely used recently to solve technological problems. In this context, this paper investigates the requirements and analyses the use of the Q-learning algorithm for energy-efficient VNE. The results achieved validate the strategy and show clear improvements in terms of cost/revenue and energy savings, compared to traditional algorithms. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Energy Systems II)

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27 pages, 6170 KiB

Open AccessArticle

T-LGBKS: An Interpretable Machine Learning Framework for Electricity Consumption Forecasting

by Mengkun Liang, Renjing Guo, Hongyu Li, Jiaqi Wu and Xiangdong Sun

Energies 2023, 16(11), 4294; https://doi.org/10.3390/en16114294 - 24 May 2023

Cited by 1 | Viewed by 1062

Abstract

Electricity is an essential resource that plays a vital role in modern society, and its demand has increased rapidly alongside industrialization. The accurate forecasting of a country’s electricity demand is crucial for economic development. A high-precision electricity forecasting framework can assist electricity system managers in predicting future demand and production more accurately, thereby effectively planning and scheduling electricity resources and improving the operational efficiency and reliability of the electricity system. To address this issue, this study proposed a hybrid forecasting framework called T-LGBKS, which incorporates TPE-LightGBM, k-nearest neighbor (KNN), and the Shapley additive explanation (SHAP) methods. The T-LGBKS framework was tested using Chinese provincial panel data from 2005 to 2021 and compared with seven other mainstream machine learning models. Our testing demonstrated that the proposed framework outperforms other models, with the highest accuracy (

R^{2} = 0.9732

). This study also analyzed the interpretability of this framework by introducing the SHAP method to reveal the relationship between municipal electricity consumption and socioeconomic characteristics (such as how changes in economic strength, traffic levels, and energy structure affect urban electricity demand). The findings of this study provide guidance for policymakers and assist decision makers in designing and implementing electricity management systems in China. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Energy Systems II)

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25 pages, 2343 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Deep Learning with Dipper Throated Optimization Algorithm for Energy Consumption Forecasting in Smart Households

by Abdelaziz A. Abdelhamid, El-Sayed M. El-Kenawy, Fadwa Alrowais, Abdelhameed Ibrahim, Nima Khodadadi, Wei Hong Lim, Nuha Alruwais and Doaa Sami Khafaga

Energies 2022, 15(23), 9125; https://doi.org/10.3390/en15239125 - 01 Dec 2022

Cited by 3 | Viewed by 1716

Abstract

One of the relevant factors in smart energy management is the ability to predict the consumption of energy in smart households and use the resulting data for planning and operating energy generation. For the utility to save money on energy generation, it must be able to forecast electrical demands and schedule generation resources to meet the demand. In this paper, we propose an optimized deep network model for predicting future consumption of energy in smart households based on the Dipper Throated Optimization (DTO) algorithm and Long Short-Term Memory (LSTM). The proposed deep network consists of three parts, the first part contains a single layer of bidirectional LSTM, the second part contains a set of stacked unidirectional LSTM, and the third part contains a single layer of fully connected neurons. The design of the proposed deep network targets represents the temporal dependencies of energy consumption for boosting prediction accuracy. The parameters of the proposed deep network are optimized using the DTO algorithm. The proposed model is validated using the publicly available UCI household energy dataset. In comparison to the other competing machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Sequence-to-Sequence (Seq2Seq), and standard LSTM, the performance of the proposed model shows promising effectiveness and superiority when evaluated using eight evaluation criteria including Root Mean Square Error (RMSE) and

R^{2}

. Experimental results show that the proposed optimized deep model achieved an RMSE of (0.0047) and

R^{2}

of (0.998), which outperform those values achieved by the other models. In addition, a sensitivity analysis is performed to study the stability and significance of the proposed approach. The recorded results confirm the effectiveness, superiority, and stability of the proposed approach in predicting the future consumption of energy in smart households. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Energy Systems II)

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Review

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29 pages, 5308 KiB

Open AccessReview

The Optimal Configuration of Wave Energy Conversions Respective to the Nearshore Wave Energy Potential

by Alireza Shadmani, Mohammad Reza Nikoo, Riyadh I. Al-Raoush, Nasrin Alamdari and Amir H. Gandomi

Energies 2022, 15(20), 7734; https://doi.org/10.3390/en15207734 - 19 Oct 2022

Cited by 7 | Viewed by 2832

Abstract

Ocean energy is one potential renewable energy alternative to fossil fuels that has a more significant power generation due to its better predictability and availability. In order to harness this source, wave energy converters (WECs) have been devised and used over the past several years to generate as much energy and power as is feasible. While it is possible to install these devices in both nearshore and offshore areas, nearshore sites are more appropriate places since more severe weather occurs offshore. Determining the optimal location might be challenging when dealing with sites along the coast since they often have varying capacities for energy production. Constructing wave farms requires determining the appropriate location for WECs, which may lead us to its correct and optimum design. The WEC size, shape, and layout are factors that must be considered for installing these devices. Therefore, this review aims to explain the methodologies, advancements, and effective hydrodynamic parameters that may be used to discover the optimal configuration of WECs in nearshore locations using evolutionary algorithms (EAs). Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Energy Systems II)

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