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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 (28 September 2023) | Viewed by 11207

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Special Issue Editors

Dr. Ali Ahmadian

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Guest Editor

1. Department of Chemical Engineering, University of Waterloo, Waterloo, ON, Canada
2. Department of Electrical Engineering, University of Bonab, Bonab, Iran
Interests: transportation electrification; energy storage; energy; modern environment energy systems; machine learning

Prof. Dr. Ali Almansoori

E-Mail Website
Guest Editor

Department of Chemical Engineering, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates
Interests: design, operation, and management of energy supply chain; process simulation, optimization and modelling; techno-economic and environmental analyses

Special Issue Information

Dear Colleagues,

Optimization in sustainable energy systems is necessary for sustainable development. This Special Issue will consider various important factors, such as minimizing performance costs and environmental pollution and increasing sustainability and reliability. The uncertainty modeling of input parameters should also be considered in this problem. Furthermore, the risk associated with these uncertainty parameters should be managed to reduce the related risk in uncertain environments. Finally, the flexibility of designed systems should be increased in different conditions. To reach this goal, the purpose of this Special Issue is to provide an opportunity for researchers to present new machine learning methods in sustainable modern energy systems in order to decrease the performance cost and environmental pollution and increase the sustainability and reliability.

Topics of interest include, but are not limited to:

The application of machine learning in the energy, water, and food nexus;
Uncertainties modeling of stochastic parameters in energy systems;
Load demand forecasting in smart cities;
Electricity price forecasting in modern energy markets;
Electric vehicles load demand forecasting in order to schedule in smart grids;
Integrated natural gas and electric networks;
Sustainable development;
Environmental investigation of modern energy systems;
Techno-economic evaluation of energy systems;
Energy management in modern energy hubs;
Network optimization models in energy supply chains;
Optimization of reverse logistics networks for performance cost optimization;
Optimization of reverse logistics networks for environmental pollution minimization;
Optimization of reverse logistics networks for increasing sustainability;
Optimization of reverse logistics networks for the improvement of reliability
Reverse logistics network design under uncertainty management;
Reverse logistics network design under risk management.

Dr. Ali Ahmadian
Prof. Dr. Ali Almansoori
Guest Editors

Manuscript Submission Information

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Keywords

artificial intelligence
machine learning
deep learning
smart energy systems
smart grids
sustainable development

Published Papers (6 papers)

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Research

25 pages, 1258 KiB

Open AccessArticle

Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand

by Merve Kayacı Çodur

Energies 2024, 17(1), 74; https://doi.org/10.3390/en17010074 - 22 Dec 2023

Viewed by 800

Abstract

Energy demand forecasting is a fundamental aspect of modern energy management. It impacts resource planning, economic stability, environmental sustainability, and energy security. This importance is making it critical for countries worldwide, particularly in cases like Türkiye, where the energy dependency ratio is notably high. The goal of this study is to propose ensemble machine learning methods such as boosting, bagging, blending, and stacking with hyperparameter tuning and k-fold cross-validation, and investigate the application of these methods for predicting Türkiye’s energy demand. This study utilizes population, GDP per capita, imports, and exports as input parameters based on historical data from 1979 to 2021 in Türkiye. Eleven combinations of all predictor variables were analyzed, and the best one was selected. It was observed that a very high correlation exists among population, GDP, imports, exports, and energy demand. In the first phase, the preliminary performance was investigated of 19 different machine learning algorithms using 5-fold cross-validation, and their performance was measured using five different metrics: MSE, RMSE, MAE, R-squared, and MAPE. Secondly, ensemble models were constructed by utilizing individual machine learning algorithms, and the performance of these ensemble models was compared, both with each other and the best-performing individual machine learning algorithm. The analysis of the results revealed that placing Ridge as the meta-learner and using ET, RF, and Ridge as the base learners in the stacking ensemble model yielded the highest R-squared value, which was 0.9882, indicating its superior performance. It is anticipated that the findings of this research can be applied globally and prove valuable for energy policy planning in any country. The results obtained not only highlight the accuracy and effectiveness of the predictive model but also underscore the broader implications of this study within the framework of the United Nations’ Sustainable Development Goals (SDGs). Full article

(This article belongs to the Special Issue Advanced Machine Learning Applications in Modern Energy Systems)

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24 pages, 2544 KiB

Open AccessArticle

A Short-Term Load Forecasting Model Based on Crisscross Grey Wolf Optimizer and Dual-Stage Attention Mechanism

by Renxi Gong and Xianglong Li

Energies 2023, 16(6), 2878; https://doi.org/10.3390/en16062878 - 21 Mar 2023

Cited by 5 | Viewed by 1721

Abstract

Accurate short-term load forecasting is of great significance to the safe and stable operation of power systems and the development of the power market. Most existing studies apply deep learning models to make predictions considering only one feature or temporal relationship in load time series. Therefore, to obtain an accurate and reliable prediction result, a hybrid prediction model combining a dual-stage attention mechanism (DA), crisscross grey wolf optimizer (CS-GWO) and bidirectional gated recurrent unit (BiGRU) is proposed in this paper. DA is introduced on the input side of the model to improve the sensitivity of the model to key features and information at key time points simultaneously. CS-GWO is formed by combining the horizontal and vertical crossover operators, to enhance the global search ability and the diversity of the population of GWO. Meanwhile, BiGRU is optimized by CS-GWO to accelerate the convergence of the model. Finally, a collected load dataset, four evaluation metrics and parametric and non-parametric testing manners are used to evaluate the proposed CS-GWO-DA-BiGRU short-term load prediction model. The experimental results show that the RMSE, MAE and SMAPE are reduced respectively by 3.86%, 1.37% and 0.30% of those of the second-best performing CSO-DA-BiGRU model, which demonstrates that the proposed model can better fit the load data and achieve better prediction results. Full article

(This article belongs to the Special Issue Advanced Machine Learning Applications in Modern Energy Systems)

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26 pages, 7099 KiB

Open AccessArticle

Agent-Based Simulation and Micro Supply Chain of the Food–Energy–Water Nexus for Collaborating Urban Farms and the Incorporation of a Community Microgrid Based on Renewable Energy

by Marwen Elkamel, Luis Rabelo and Alfonso T. Sarmiento

Energies 2023, 16(6), 2614; https://doi.org/10.3390/en16062614 - 10 Mar 2023

Viewed by 2012

Abstract

An agent-based modeling framework is developed and employed to replicate the interactions among urban farms. The objectives are to efficiently manage an urban farm’s food, energy, and water resources, decrease food waste, and increase the food availability for the local community. A case study of eleven farms was investigated in Vancouver, Canada to study the linkages between the resources in the urban food, energy, and water nexus. Each urban farm in the simulation belonged to a community microgrid generating electricity from solar and wind. The local farms aimed to provide fresh produce for their respective local communities. However, at some points, they lacked supply, and at other points, there was excess supply, leading to food waste. Food waste can be converted into fertilizers or bioenergy. However, an alternative solution must be employed due to the natural resources required for production, efficiently managing resources, and adhering to sustainability guidelines. In this paper, an optimization framework was integrated within the agent-based model to create a micro supply chain. The supply chain directly linked the producers with the consumers by severing the links involved in a traditional food supply. Each urban farm in the study collaborated to reduce food wastage and meet consumer demands, establishing farmer-to-farmer exchange in transitional agriculture. The optimization-based micro supply chain aimed to minimize costs and meet the equilibrium between food supply and demand. Regular communication between the farms reduced food waste by 96.9% over 16 weeks. As a result, the fresh food availability increased for the local community, as exemplified by the consumer purchases over the same period. Moreover, the simulation results indicated that the renewable energy generation at the community microgrids aided in the generation of 22,774 Mwh from solar and 2568 Mwh from wind. This has the potential to significantly reduce CO₂ emissions in areas that heavily rely on non-renewable energy sources. Full article

(This article belongs to the Special Issue Advanced Machine Learning Applications in Modern Energy Systems)

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22 pages, 6965 KiB

Open AccessArticle

Evaluation of Machine Learning Algorithms for Supervised Anomaly Detection and Comparison between Static and Dynamic Thresholds in Photovoltaic Systems

by Thitiphat Klinsuwan, Wachiraphong Ratiphaphongthon, Rabian Wangkeeree, Rattanaporn Wangkeeree and Chatchai Sirisamphanwong

Energies 2023, 16(4), 1947; https://doi.org/10.3390/en16041947 - 15 Feb 2023

Cited by 6 | Viewed by 2099

Abstract

The use of photovoltaic systems has increased in recent years due to their decreasing costs and improved performance. However, these systems can be susceptible to faults that can reduce efficiency and energy yield. To prevent and reduce these problems, preventive or predictive maintenance and effective monitoring are necessary. PV health monitoring systems and automatic fault detection and diagnosis methods are critical for ensuring PV plants’ reliability, high-efficiency operation, and safety. This paper presents a new framework for developing fault detection in photovoltaic (PV) systems. The proposed approach uses machine learning algorithms to predict energy power production and detect anomalies in PV plants by comparing the predicted power from a model and the measured power from sensors. The framework utilizes historical data to train the prediction model, and live data is compared with predicted values to analyze residuals and detect abnormal scenarios. The proposed approach has been shown to accurately distinguish anomalies using constructed thresholding, either static or dynamic thresholds. The paper also reports experimental results using the Matthews correlation coefficient, a more reliable statistical rate for an imbalanced dataset. The proposed approach leads to a reasonable anomaly detection rate, with an MCC of 0.736 and a balanced ACC of 0.863. Full article

(This article belongs to the Special Issue Advanced Machine Learning Applications in Modern Energy Systems)

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17 pages, 3561 KiB

Open AccessArticle

Optimal Management of a Virtual Power Plant Consisting of Renewable Energy Resources and Electric Vehicles Using Mixed-Integer Linear Programming and Deep Learning

by Ali Ahmadian, Kumaraswamy Ponnambalam, Ali Almansoori and Ali Elkamel

Energies 2023, 16(2), 1000; https://doi.org/10.3390/en16021000 - 16 Jan 2023

Cited by 10 | Viewed by 2197

Abstract

Recently, renewable energy resources (RESs) and electric vehicles (EVs), in addition to other distributed energy resources (DERs), have gained high popularity in power systems applications. These resources bring quite a few advantages for power systems—reducing carbon emission, increasing efficiency, and reducing power loss. However, they also bring some disadvantages for the network because of their intermittent behavior and their high number in the grid which makes the optimal management of the system a tough task. Virtual power plants (VPPs) are introduced as a promising solution to make the most out of these resources by aggregating them as a single entity. On the other hand, VPP’s optimal management depends on its accuracy in modeling stochastic parameters in the VPP body. In this regard, an efficient approach for a VPP is a method that can overcome these intermittent resources. In this paper, a comprehensive study has been investigated for the optimal management of a VPP by modeling different resources—RESs, energy storages, EVs, and distributed generations. In addition, a method based on bi-directional long short-term memory networks is investigated for forecasting various stochastic parameters, wind speed, electricity price, load demand, and EVs’ behavior. The results of this study show the superiority of BLSTM methods for modeling these parameters with an error of 1.47% in comparison with real data. Furthermore, to show the performance of BLSTMs, its results are compared with other benchmark methods such as shallow neural networks, support vector machines, and long short-term memory networks. Full article

(This article belongs to the Special Issue Advanced Machine Learning Applications in Modern Energy Systems)

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15 pages, 3534 KiB

Open AccessArticle

Development and Validation of a Nuclear Power Plant Fault Diagnosis System Based on Deep Learning

by Bing Liu, Jichong Lei, Jinsen Xie and Jianliang Zhou

Energies 2022, 15(22), 8629; https://doi.org/10.3390/en15228629 - 17 Nov 2022

Cited by 4 | Viewed by 1774

Abstract

As artificial intelligence technology has progressed, numerous businesses have used intelligent diagnostic technology. This study developed a deep LSTM neural network for a nuclear power plant to defect diagnostics. PCTRAN is used to accomplish data extraction for distinct faults and varied fault degrees of the PCTRAN code, and some essential nuclear parameters are chosen as feature quantities. The training, validation, and test sets are collected using random sampling at a ratio of 7:1:2, and the proper hyperparameters are selected to construct the deep LSTM neural network. The test findings indicate that the fault identification rate of the nuclear power plant fault diagnostic model based on a deep LSTM neural network is more than 99 percent, first validating the applicability of a deep LSTM neural network for a nuclear power plant fault-diagnosis model. Full article

(This article belongs to the Special Issue Advanced Machine Learning Applications in Modern Energy Systems)

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