Sustainable Energy Management Research Articles

Optimizing ELD in power systems applying GWO: A Practical Approach

Addressing the Economic load Dispatch (ELD) Problem in power systems is crucial for minimizing generation cost and transmission losses while meeting the load demand. This research explores the application of Grey Wolf Optimization (GWO) to solve the ELD problem, leveraging ‘GWO’s inspiration from grey wolf social behavior. Through simulation, ‘GWO’s superior convergence speed and solution quality compared to traditional techniques is demonstrated. The finding highlights ‘GWO’s effectiveness in enhancing the economic and operational efficiency of power systems, offering promising avenues for sustainable energy management strategies.

Harnessing the energy potential of rosehip wastes towards sustainable energy supply

Using renewable energy technologies comes to the forefront for sustainable energy, economy, and environmental management. In regions with high agricultural production, it is becoming increasingly important to contribute to sustainable energy production by using agricultural wastes as biomass. For this purpose, the evaluation of the energy potential and catalytic pyrolysis of rosehip seed (RS) and rosehip pomace (RP), which are considered the main wastes of rosehip, were investigated to obtain improved bio-oil. While C10+ hydrocarbons of alkane and alkene origin were mostly found in the composition of bio-oil, the highest ratio belongs to 50CL50RP650. Especially when only RS and RP were present, both the temperature of 650°C and the presence of catalyst were more effective in obtaining aliphatics. In the use of the blend, one of these parameters was sufficient and the volatile matter increased and fixed carbon decreased, especially as the RP ratio (70%) in the blend increased. The heating value (18.5–19 MJ/kg) and volatile matter (63–66%) of the wastes were high, while ash (0%) and moisture (2%-3%) contents were low, which is important in terms of energy potential. In the combustion profiles, the Tmax was generally higher for the blends.

Magnetic Field Effect on Photocatalytic Dye Degradation: A Review

AbstractIn the present day, photocatalysis has become the primary and most promising technology to generate renewable energy and degrade environmental pollutants. Remarkable efforts have been made to improve photocatalytic efficiency. Among these, introducing an external magnetic field is considered a promising strategy to boost photocatalytic performance. This review explores the dynamic realm of improving photocatalytic efficiency, with a particular emphasis on the strategic integration of external magnetic fields. Examining recent breakthroughs, we reviewed the influence of magnetic fields on diverse catalyst materials, including both magnetic and nonmagnetic variants, 2D materials, and semiconductors. Comprehensive coverage is provided on the mechanisms governing photocatalytic dye degradation, materials utilized for catalysis, and the profound impact of magnetic fields on enhancing reaction kinetics. This review also focuses on recent advances in the application and effect of magnetic fields in the magnetic and nonmagnetic catalysts towards dye degradation. This review provides a forward‐looking view, highlighting the potential of magnetic field‐enhanced photocatalysis to play a central role in sustainable energy and environmental management.

Harvesting Earth's heat: A deep learning Odyssey for reservoir characterization and sustainable geothermal energy management

Characterizing and predicting reservoir behavior pose significant challenges in geothermal reservoir engineering. For sustainable geothermal field management, the potential occurrence of thermal breakthroughs in producers during the injection of cold water necessitates a profound understanding of how production is influenced by the injection philosophy. The fractured nature of geothermal reservoirs adds complexity and nonlinearity to the relationship between production and injection wells. In this study, we explore alternative models to simulate reservoir behavior as substitutes for full reservoir simulations. Utilizing deep learning algorithms, we investigate two different architectures, namely the standard Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). These models map injection flow rates at the injectors to tracer concentration data at the producers through layers, nodes, and activation functions. Training these models on a synthetic geothermal reservoir, Notably, the GRU architecture consistently demonstrates superior predictive capabilities across various scenarios for all three producers. Specifically, in configurations involving a target feed, the GRU model yields higher R2 values compared to its LSTM counterpart, indicating its effectiveness in capturing inter-well relationships. Furthermore, when considering all producers collectively, the GRU model exhibits a trend of lower test errors and higher R2 values, reaffirming its proficiency in modeling complex reservoir dynamics. These findings underscore the significance of GRU as a preferred choice for accurate reservoir behavior prediction. In addition to the aforementioned insights, our study contributes novel approaches to reservoir behavior prediction in the field of geothermal reservoir engineering. By leveraging deep learning algorithms, specifically the GRU and LSTM architectures, we introduce innovative methodologies for simulating reservoir behavior as alternatives to full reservoir simulations. Through comprehensive analysis, we demonstrate the superior predictive capabilities of the GRU model, particularly in capturing inter-well relationships and modeling complex reservoir dynamics.

Deriving Input Variables through Applied Machine Learning for Short-Term Electric Load Forecasting in Eskilstuna, Sweden

As the demand for electricity, electrification, and renewable energy rises, accurate forecasting and flexible energy management become imperative. Distribution network operators face capacity limits set by regional grids, risking economic penalties if exceeded. This study examined data-driven approaches of load forecasting to address these challenges on a city scale through a use case study of Eskilstuna, Sweden. Multiple Linear Regression was used to model electric load data, identifying key calendar and meteorological variables through a rolling origin validation process, using three years of historical data. Despite its low cost, Multiple Linear Regression outperforms the more expensive non-linear Light Gradient Boosting Machine, and both outperform the “weekly Naïve” benchmark with a relative Root Mean Square Errors of 32–34% and 39–40%, respectively. Best-practice hyperparameter settings were derived, and they emphasize frequent re-training, maximizing the training data size, and setting a lag size larger than or equal to the forecast horizon for improved accuracy. Combining both models into an ensemble could the enhance accuracy. This paper demonstrates that robust load forecasts can be achieved by leveraging domain knowledge and statistical analysis, utilizing readily available machine learning libraries. The methodology for achieving this is presented within the paper. These models have the potential for economic optimization and load-shifting strategies, offering valuable insights into sustainable energy management.

Exploiting building information modeling and machine learning for optimizing rooftop photovoltaic systems

The primary objective of this study is to develop a strategy to maximize the potential of Building Applied Photovoltaics (BAPV) by providing researchers and experts in the field with the appropriate tools. By utilizing operational data from an operational roof-mounted BAPV system and incorporating Building Information Modeling (BIM) to improve its design and smooth integration into built settings, the study offers novel insights.A three-phase research methodology is applied, encompassing data collection and performance assessment, BIM modeling, and machine learning algorithms for energy forecasting. The case study involves a 160 kWp photovoltaic system in Abruzzo, Italy, over a three-year period. The research employs different performance metrics recommended by IEC 61724 to compare experimental and theoretical data. BIM simulations are exploited as they are crucial for identifying the correct design process. Furthermore, four cutting-edge machine learning algorithms are used to forecast daily energy production. The random forest is identified as the best model for forecasting and the effect of tuning of hyperparameters on the efficiency of all models is also reported.The research adds to the larger conversation on sustainable energy management and solutions by providing researchers involved in the design and improvement of BAPV systems with a solid foundation for future developments.

Deep fuzzy nets approach for energy efficiency optimization in smart grids

Using smart grids has become crucial for achieving efficient and sustainable energy management. One of the main challenges in smart grids is optimizing energy efficiency by managing and controlling electricity generation, transmission, and distribution. The Deep Fuzzy Nets (DFN) approach has been proposed as a novel technique that combines the capabilities of deep learning and fuzzy login to optimize energy efficiency in smart grids. The proposed approach utilizes a deep learning architecture to learn the complex relationships between various parameters within the smart grid system. The fuzzy logic component handles uncertainties and imprecision’s in the data, making the DFN approach well-suited for real-world energy management applications. The proposed approach can provide accurate and reliable predictions and enhancing energy efficiency in a dynamic and evolving smart grid environment. The proposed deep fuzzy nets approach reached 91% sensitivity, 94.45% specificity, 92/37% prevalence threshold, and 96.54% critical success index. This approach has been tested in various energy systems and has demonstrated capabilities to improve system-level energy efficiency while still giving users control of their energy usage. As energy efficiency optimization in intelligent grids continues to be a primary focus of energy research, deep fuzzy nets could provide a powerful solution for energy optimization.

Role of Education and Awareness Programs in Fostering Energy Conservation Behavior in Cities: Empowering Urban Sustainability Using Deep Learning Approach

This paper introduces a pioneering framework that emphasizes the critical role of education in promoting energy conservation behavior within urban environments. By synergizing deep learning methodologies with education-awareness formulations and algorithm, the framework aims to bolster energy conservation security in cities. A central aspect of this framework is the development of an advanced Intrusion Detection System (IDS) model, utilizing Restricted Boltzmann Machines (RBMs) tailored to enhance energy consumption security. Through six strategic integrations of educational components, the IDS not only identifies but also mitigates potential threats to energy systems with unprecedented efficiency. Additionally, the research introduces a novel evolutionary feature selection method based on Memetic Algorithms (MAs), which strategically identifies crucial features for RBM training, ensuring optimal model performance. The incorporation of Memetic Algorithms facilitates individual learning and problem-specific knowledge transmission, further enhancing the adaptive capabilities of the system. This multifaceted approach underscores the pivotal role of education in fostering urban sustainability, pushing the boundaries of energy conservation security. By proactively responding to emerging threats, the proposed framework contributes significantly to the overarching goal of sustainable and secure urban energy management.

Bio-Inspired Bidirectional Deep Machine Learning for Real-Time Energy Consumption Forecasting and Management

Accurately predicting electrical power demand is crucial to making related forecasts and to effective sustainable energy management. Most relevant state-of-the-art studies deploy models that do not use optimizing parameters and do not incorporate strategies for using forecast results. This study was designed to develop a novel electricity consumption forecasting model, the Symbiotic Bidirectional Gated Recurrent Unit, which integrates Gated Recurrent Unit, Bidirectional Technique, and Symbiotic Organisms Search algorithms. The results of tests on a series of evaluation criteria showed the proposed model performed significantly better than six comparison models when parameter optimization was used. For all three sector datasets, the proposed model generated the most-accurate predictions of all models. In practical terms, when supply is expected to exceed demand, the prediction results may be used to adjust power plant output to reduce wastage. Conversely, when demand is expected to exceed supply, Time-of-Use tariffs may be implemented based on time-of-day and seasonal fluctuations in demand to facilitate reductions in peak usage and level out overall demand.

Fostering second-life applications for electric vehicle batteries: A thorough exploration of barriers and solutions within the framework of sustainable energy and resource management

Electric vehicles (EVs) play a crucial role in achieving Sustainable Development Goals (SDGs), specifically focusing on Goal 7, which ensures access to Affordable and Clean Energy, and Goal 9, promoting advancements in Industry, Innovation, and Infrastructure. However, the growing stockpile of used EV Batteries (EVBs) presents challenges. These EVBs, however, hold potential for second-use applications, aligning with energy conversion, resource optimization, and sustainable energy systems. This study employs a rigorous multi-criteria approach to bridge the research-practice gap by systematically evaluates and prioritizes barriers hindering the widespread adoption of second-use EVBs. The study's overarching objectives are to overcome these barriers, aligning with broader sustainability goals, and to offer insights shaping policies addressing environmental, social, and economic impacts, in coherence with SDGs 8, 11, 12, and 13. The research unfolds through a three-phase approach involving literature reviews, field visits, and expert interviews for barrier identification, theoretical framework development through literature and Delphi studies, and prioritization using the Fuzzy Best-Worst Method (FBWM). Results indicate that addressing Cost and Technological Challenges is pivotal for successful second-use EVB adoption, with proposed solutions involving regulations, recycling, and awareness. The study underscores the circular economy perspective, connecting identified barriers to specific SDGs. The FBWM proves effective in prioritizing barriers, providing actionable insights for policymakers and stakeholders. This research contributes significantly to the existing literature by systematically evaluating second-use EVBs, addressing a crucial research gap, and offering valuable pathways for broad adoption.

Sustainable energy transition in cities: A deep statistical prediction model for renewable energy sources management for low-carbon urban development

This study proposes a novel approach to sustainable energy transition in urban environments, focusing on the prediction and management of renewable energy outputs for low-carbon urban development. We introduce a new intelligent prediction model based on Gated Recurrent Unit (GRU) Networks to forecast the output power of both tidal and biomass units. The GRU model, known for its capability in capturing sequential dependencies, is employed to enhance the accuracy of renewable energy predictions in the context of urban sustainability. To address the interpretability challenges inherent in complex deep learning models, we leverage LIME (Local Interpretable Model-agnostic Explanations). By integrating LIME into our GRU-based prediction model, we enhance transparency and understandability. This approach facilitates the identification of key factors influencing predictions, providing stakeholders and decision-makers with valuable insights for effective sustainable energy management. The proposed hybrid model is applied to the digital model of real tidal and biomass sites in China, allowing for a statistical examination of renewable integration and energy management strategies. The results offer a comprehensive understanding of the dynamics between renewable energy sources and urban development, paving the way for informed decision-making in the pursuit of low-carbon and resilient cities. This research contributes to the broader discourse on sustainable urban development by offering a robust statistical framework for renewable energy integration and management in urban environments.

Research on the Spatiotemporal Characteristics of the Coupling Coordination Relationship of the Energy–Food–Water System in the Xinjiang Subregion

In the Xinjiang region, the sustainable management of water resources, energy, and food is crucial for regional development. This study establishes a coupling evaluation index for energy–food–water (EFW) systems from the perspectives of supply, consumption, and efficiency. Using an integrated EFM-CDD-RDD-CCDM approach, an assessment of the coupling and coordination levels of the EFW systems in 14 cities within Xinjiang was conducted for the period of 2004 to 2020. Additionally, the method of obstacle degree identification was utilized to determine the main barriers affecting the EFW systems. Key findings included the following. (1) In terms of individual system coordination indices, the water resource systems exhibited overall higher coordination (ranging from 0.30 to 0.72) with comparatively minor spatial variability, while the energy (from 0.18 to 0.81) and food (from 0.12 to 0.83) systems showed greater temporal and spatial fluctuations. From 2004 to 2020, improvements were observed in the coordination of food and water resource systems, whereas a decline was noted in the coordination of the energy subsystem. (2) Prior to 2011, the coupling of food–water and energy–food systems showed an upward trend, whereas the energy–water coupling decreased annually by 2.62%, further highlighting the tensions between energy development and water resource constraints in Xinjiang. (3) The comprehensive coupling coordination index of the Xinjiang EFW systems ranged between 0.59 and 0.80; between 2004 and 2020, there was an oscillatory increase. From 2004 to 2016, the coupling and coordination degree across the municipalities generally improved, with the regions on the western side and southern slope of the Tianshan Mountains, the Altai Mountains, and the northwestern edge of the Junggar Basin exhibiting the highest levels, followed by the three prefectures in southern Xinjiang. (4) The EFW obstacle degree posed by the food systems in Xinjiang and its divisions showed a decreasing trend from 2004 to 2020, with the energy system identified as the main factor affecting the coupling and coordination degrees of the EFW systems (increasing by 44% to 52%). Therefore, it is imperative to accelerate the energy transition and optimization in the lead energy development and production areas of Xinjiang. This research provides a scientific basis for Xinjiang’s sustainable development strategies and highlights potential directions for the future optimization of resource management.

Additive Manufacturing: A Paradigm Shift in Revolutionizing Catalysis with 3D Printed Photocatalysts and Electrocatalysts Toward Environmental Sustainability.

Semiconductor-based materials utilized in photocatalysts and electrocatalysts present a sophisticated solution for efficient solar energy utilization and bias control, a field extensively explored for its potential in sustainable energy and environmental management. Recently, 3D printing has emerged as a transformative technology, offering rapid, cost-efficient, and highly customizable approaches to designing photocatalysts and electrocatalysts with precise structural control and tailored substrates. The adaptability and precision of printing facilitate seamless integration, loading, and blending of diverse photo(electro)catalytic materials during the printing process, significantly reducing material loss compared to traditional methods. Despite the evident advantages of 3D printing, a comprehensive compendium delineating its application in the realm of photocatalysis and electrocatalysis is conspicuously absent. This paper initiates by delving into the fundamental principles and mechanisms underpinning photocatalysts electrocatalysts and 3D printing. Subsequently, an exhaustive overview of the latest 3D printing techniques, underscoring their pivotal role in shaping the landscape of photocatalysts and electrocatalysts for energy and environmental applications. Furthermore, the paper examines various methodologies for seamlessly incorporating catalysts into 3D printed substrates, elucidating the consequential effects of catalyst deposition on catalytic properties. Finally, the paper thoroughly discusses the challenges that necessitate focused attention and resolution for future advancements in this domain.

An IoT-Enabled Smart Energy Management System to Improve Energy Efficiency in University Laboratory

Efficient energy management remains a challenge in university laboratories. The aim of this study is to develop and deploy an Internet of Things (IoT) system that can automatically adjust electronic devices to improve the efficiency of power consumption in the university laboratory. An advanced system is urgently needed to support sustainable and effective energy management. A smart IoT-based energy management system can improve energy efficiency, reduce operating costs, and reduce negative environmental impacts. The system enables both human and automated control of lighting and air conditioning using scheduling and occupancy detection. This development-based research uses a product design methodology that includes both software and hardware. The software product in this research is an IoT platform website for managing IoT devices. The essential processes include defining project goals and requirements, designing a system architecture, creating a user experience design, developing and integrating components, and conducting tests. The system is successful in automatically monitoring and controlling electronic devices based on certain parameters such as scheduling and presence detection. The system meets most of the specified functional and non-functional requirements as demonstrated in experiments, although it is somewhat limited by hardware limitations. Ultimately, the system increases the energy efficiency of the laboratory and thus successfully fulfills the research goal. This innovative project could be a blueprint for other smart energy control efforts.

Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste

In the context of escalating energy demands and the quest for sustainable waste management solutions, this paper evaluates the efficacy of three machine learning methods—ElasticNet, Decision Trees, and Neural Networks—in predicting energy recovery from municipal waste across the European Union. As renewable energy sources increasingly dominate the energy production landscape, the integration of Waste-to-Energy (WTE) processes presents a dual advantage: enhancing waste management and contributing to the renewable energy mix. This study leverages a dataset incorporating economic and environmental indicators from 25 European countries, spanning 2013–2020, to compare the predictive capabilities of the three machine learning models. The analysis reveals that Neural Networks, with their intricate pattern recognition capabilities, outperform ElasticNet and Decision Trees in predicting energy recovery metrics, as evidenced by superior performance in key statistical indicators such as R-value, Mean Squared Error (MSE), and Mean Absolute Error (MAE). The comparative analysis not only demonstrates the effectiveness of each method but also suggests Neural Networks as a pivotal tool for informed decision-making in waste management and energy policy formulation. Through this investigation, the paper contributes to the sustainable energy and waste management discourse, emphasizing the critical intersection of advanced technologies, policy considerations, and environmental stewardship in addressing contemporary energy challenges.

Production and assessment of UV-cured resin coated stearyl alcohol/expanded graphite as novel shape-stable composite phase change material for thermal energy storage

Expanded graphite-phase change materials (PCM) structures are reinforced to polymers with various methods to fabricate advanced thermal energy storage materials. However, these methods still suffer from processing time and product efficiency challenges. In this study, the UV-curing method was used to produce shape-stable EG-PCM-reinforced resin composites with fast curing and low process temperature of the resin. The composite material, comprising UV-curable resin (30 %), stearyl alcohol (65 %), and Expanded graphite (5 %), was synthesized. This synthesis aimed to address the limitations of traditional PCMs, such as low thermal conductivity and leakage. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were used to characterize the materials' phase change behavior and thermal stability. Scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR) analyses were conducted to elucidate the microstructure and crystallinity of composite materials. The composites, exhibiting near-perfect impermeability with leakage as minimal as 0.89 %, not only enable the attainment of cooler environments by 2–3 °C under hot air conditions but also demonstrate exceptional thermal stability up to 207 °C, as evidenced by TGA results. Additionally, they offer a remarkable melting enthalpy value of 153.1 J/g. These composites, with their shape-retention ability during phase transitions and high thermal energy storage capacity, are a versatile and efficient option for sustainable energy management. This research contributes to the development of innovative materials for renewable energy integration and reducing carbon emissions.

Ten years of sustainable energy planning and management

With this volume 40 the International Journal of Sustainable Energy Planning and Management can celebrate its 10 years anniversary. In this editorial we take a closer look at the journals impact over its first 10 years of existence and highlight the articles that have made the largest impacts in the scientific community from a citation perspective. The topic of the journal – sustainable energy planning and management – has not decreased in importance over the first ten years of the journal’s existence – and fortunately the results published in the journal has garnered interest and the impact of the journal has significantly increased. In terms of citation, the journals 275 published articles, has received 1484 citations as per March 2024 and nine articles have 40 or more citations in Scopus. CiteScore ranks the journal in the top 10% out of 700+ journals within the topic of Geography, Planning and Development.

Role of microbial electrolysis desalination cell in sustainable water and energy management: Performance assessment of platinum and nickel foam cathodes and simulating integration with reverse osmosis systems

This research introduces the microbial integrated cell (MIC), a bioelectrochemical system that combines microbial electrolysis and desalination for sustainable energy and water purification. Efficiency evaluations of nickel foam and Pt/CC cathodes for hydrogen production and desalination showed nickel foam achieving a 91% desalination efficiency, closely following Pt/CC's 99%, with hydrogen generation rates of 0.96 m3H2/m3/d for Pt/CC cathodes and 0.72 m3H2/m3/d for nickel foam. Despite marginally lower performance, the cost advantages of nickel foam suggest its feasibility for large-scale deployment. Further exploration into MIC integration with reverse osmosis (RO) system aims to enhance the sustainability of the combined system. Simulation outcomes indicate that MIC-RO integration can significantly reduce energy consumption and brine concentration discharged into the sea, highlighting its potential to improve desalination plants' efficiency and environmental impact. This research facilitates the transition of these green technologies to practical applications, optimizing efficiency and environmental benefits.

Advancing Sustainable Energy Management: A Comprehensive Review of Artificial Intelligence Techniques in Building

Advancing Sustainable Energy Management: A Comprehensive Review of Artificial Intelligence Techniques in Building

Smart landscaping design for sustainable net-zero energy smart cities: Modeling energy hub in digital twin

In our study, we introduce an innovative energy management system optimized for energy hubs in net-zero energy smart cities, emphasizing sustainability as a core objective. Our approach intricately integrates distributed generation systems and storage facilities within a digital twin framework, incorporating landscape considerations to promote environmental harmony. By prioritizing sustainability, our strategy aims to minimize operational costs while maximizing energy efficiency and reducing carbon emissions. We employ specialized energy flow algorithms for each grid, designing energy hubs that leverage renewable resources and mobile storage to enhance sustainability. To address complexity, our model utilizes a non-convex mixed-integer nonlinear programming structure and robust adaptive optimization techniques, combining the Gray Wolf and Krill Herd algorithms. Through simulations, we demonstrate significant improvements in power system efficiency, including notable reductions in power loss, peak voltage fluctuations, and temperature variations. Our research underscores the critical role of smart landscaping in advancing sustainable energy management for net-zero energy smart cities, facilitating seamless integration of energy hubs with the surrounding landscape within a digital twin paradigm.