Management Of Energy Resources Research Articles

Multi-objective algorithm for hybrid microgrid energy management based on multi-agent system

In the dynamic landscape of renewable energies, microgrid systems emerge as a promising avenue for fostering sustainable local energy generation. However, the effective management of energy resources holds the key to unlocking their full potential. This study assumes the task of creating a multi-objective optimization algorithm for microgrid energy management. At its core, the algorithm places a premium on seamlessly integrating renewable energy sources and orchestrating efficient storage coordination. Leveraging the prowess of a multi-agent system, it allocates and utilizes energy resources. Through the combination of renewable sources, storage mechanisms, and variable loads, the algorithm promotes energy efficiency and ensures a steady power supply. This transformative solution is underscored by the algorithm's remarkable performance in practical simulations and validations across diverse microgrid scenarios, offering a prevue into the future of sustainable energy utilization.

Power Hardware-in-the-Loop Smart Inverter Testing with Distributed Energy Resource Management Systems

The increasing integration of grid-connected photovoltaic (PV) inverters and inverter-based resource (IBR) systems into the power grid emphasizes the critical need for standardized procedures to ensure their reliability and effective grid support functions. This research is driven by the gap in standardized testing methodologies for smart inverters, which are pivotal for the stability and quality of power in distributed energy systems. We used a Power Hardware-in-the-Loop (PHIL) laboratory setup to conduct a comprehensive analysis of smart inverters within a simulated real-world grid environment. Our approach integrates a Distributed Energy Resource Management System (DERMS) with PHIL testing to evaluate the smart inverter’s performance across various operational modes. The detailed test protocols mimic real-world grid conditions, enabling the examination of the inverter’s dynamic response to grid disturbances, control strategies, and communication protocols. The primary aim of this study is to rigorously test and validate the primary functions of smart inverters, focusing on their impact on overall grid stability and power quality management. This includes advanced features like Volt–VAR, Volt–Watt, dynamic power factor control, and the seamless integration of smart inverters into DERMSs and Advanced Distribution Management Systems (ADMSs). Furthermore, we aim to bridge the current gap in standardized testing procedures, contributing to the establishment of robust standards and operating protocols for smart inverter integration into the grid.

Working from home during COVID-19: boundary management tactics and energy resources management strategies reported by public service employees in a qualitative study

BackgroundIncreased working from home has imposed new challenges on public service employees, while also granting opportunities for job crafting. Grounding on the Job Demands-Resources model and Hobfoll’s Conservation of Resources theory this exploratory research aims to investigate the work-nonwork balance of employees one and a half years after the outbreak of the COVID-19 pandemic. Therefore, the research focus lies on employees’ job crafting strategies to optimize their working from home experience concerning boundary management and energy resource management.MethodsTwelve semi-structured telephone interviews were conducted with public service employees from different sectors in Germany. The experiences were content analyzed using the software MaxQDA and inductive and deductive categories were derived.ResultsBoundary management comprised different strategies such as communicative (e.g., negotiating work time), physical (e.g., going to the garden), temporal (e.g., logging off in between the work day) and behavioral (e.g., prioritizing tasks) strategies. The job crafting strategies regarding energy management included preventing exhaustion (e.g. taking breaks), healthy cooking and energy management in case of sickness (e.g. deciding on sick leave).ConclusionsThis qualitative case study enriches research on job crafting by offering insights on boundary tactics and energy resources management strategies for remote working during the COVID-19 pandemic. The results point out different starting points for employees and decision makers, how a work-nonwork balance, energy management and thus employees’ wellbeing may be increased when working from home in the future.Trial registrationThe study design and methodology were approved by the Ethics Committee of the University of Cologne and the study was prospectively registered (Ref No. 21-1417_1).

Artificial intelligence based succored power prediction in stand alone PV system

ABSTRACT Accurate forecasting of solar power generation is crucial for effective management of renewable energy resources. This literature review emphasises the effectiveness of Artificial Neural Networks (ANNs) in predicting solar energy potential. While traditional ANNs with single hidden layers and predefined neuron configurations have shown success in various applications, they are not suitable for predicting nonlinear time-series databases in the context of this research. The limitations of traditional ANNs in predicting solar power potential necessitate the exploration of alternative approaches. Configuring the structure of traditional ANNs for solar power prediction is a time-consuming process that often requires manual or trial-and-error methods. To address this issue, optimisation techniques are incorporated into the ANN model. Various traditional methods have been explored in this research, but none has achieved satisfactory performance. Drawing from experiences with traditional techniques, a novel Evolution of Cub to Predator (ECP) technique is proposed. The investigation results demonstrate that the incorporation of optimisation techniques yields superior performance compared to traditional ANNs. The proposed ECP technique achieves an impressive prediction accuracy of 97.2%, surpassing existing optimisation techniques. These findings highlight the potential of the ECP technique for accurate solar power prediction and its contribution to optimising renewable energy resource management.

Construction of a recurrent neural network-based electrical load forecasting model for a 110 kV substation: a case study in the Western Region of The Republic of Kazakhstan

Construction of a recurrent neural network-based electrical load forecasting model for a 110 kV substation: a case study in the Western Region of The Republic of Kazakhstan

Long-term solar radiation forecasting based on LSTM and attention mechanism: a case study in Algeria

The growing adoption of photovoltaic (PV) energy in urban areas underscores its capability to meet energy demands effectively. The accurate forecasting of meteorological parameters, particularly global solar radiation, is paramount for the efficient management and utilization of solar energy resources. Solar radiation forecasting methods typically fall into two main categories: cloud imagery combined with physical approaches, and Artificial Intelligence (AI) based methods. Due to the non-stationary nature of solar radiation and the high nonlinearity of atmospheric conditions, conventional forecasting approaches often exhibit poor accuracy. Artificial intelligence based approaches, such as machine learning and deep learning algorithms, are extensively employed in global solar radiation forecasting research, demonstrating impressive accuracy. In this respect, this paper presents a novel approach for long term monthly forecasting of global solar radiation using a Bidirectional Long Short-Term Memory (LSTM) architecture augmented with an attention mechanism that includes a Squeeze and Excitation (SE) block (SE-BiLSTM). The effectiveness of this model is extensively evaluated for long-term monthly solar radiation forecasting using meteorological data collected over a 20-year period (from Jan 2001 to Dec 2020) from the National Aeronautics and Space Administration (NASA). The proposed SE-BiLSTM model is compared with well-established forecasting models including Naïve, Autoregressive Moving Average (ARMA), Multi-layer Perceptron (MLP), LSTM, and Bidirectional LSTM. Through rigorous simulation tests, our model demonstrates superior performance, achieving the lowest mean absolute percentage error (MAPE) of 4.52% and mean absolute error (MAE) of 7.89 kW/m2. This advancement holds significant promise for enhancing solar energy forecasting accuracy and its practical application in renewable energy systems.

THE IMPACT OF SMART GRIDS ON ENERGY EFFICIENCY: A COMPREHENSIVE REVIEW

Smart grids have emerged as a key technology in the quest for energy efficiency and sustainability. This review provides a comprehensive analysis of the impact of smart grids on energy efficiency, highlighting key findings, challenges, and future directions. Smart grids leverage advanced sensing, communication, and control technologies to optimize the generation, distribution, and consumption of electricity. By enabling two-way communication between utilities and consumers, smart grids improve grid reliability, reduce energy losses, and facilitate the integration of renewable energy sources. One of the key findings of this review is the significant impact of smart grids on energy efficiency. Studies have shown that smart grids can reduce energy consumption by enabling real-time monitoring and control of energy usage. This not only helps consumers reduce their electricity bills but also reduces the overall carbon footprint of the electricity sector. However, the deployment of smart grids faces several challenges, including high upfront costs, interoperability issues, and data privacy concerns. Addressing these challenges will be crucial for the widespread adoption of smart grid technologies. Looking ahead, the future of smart grids holds great promise for further improving energy efficiency. Emerging trends such as the Internet of Things (IoT) integration, artificial intelligence (AI) algorithms, and distributed energy resources (DERs) management are expected to drive further innovation in smart grid technologies. In conclusion, smart grids have a transformative impact on energy efficiency, offering significant benefits for consumers, utilities, and the environment. However, addressing challenges such as cost and interoperability will be crucial for realizing the full potential of smart grids. By embracing emerging trends and technologies, stakeholders can further enhance the efficiency and sustainability of the electricity sector.
 Keywords: Impact, Smart Grid, Energy, Efficiency, Review.

Advanced efficient energy management strategy based on state machine control for multi-sources PV-PEMFC-batteries system

This article offers a PV-PEMFC-batteries energy management strategy (EMS) that aims to meet the following goals: keep the DC link steady at the standard value, increase battery lifespan, and meet power demand. The suggested multi-source renewable system (MSRS) is made to meet load demand while using extra power to fill batteries. The major energy source for the MSRS is photovoltaic, and fuzzy logic MPPT is used to guarantee that the PV operates at optimal efficiency under a variety of irradiation conditions. The suggested state machine control consists of 15 steps. It prioritizes the proton exchange membrane fuel cell (PEMFC) as a secondary source for charging the battery when power is abundant and the state of charge (SOC) is low. The MSRS is made feasible by meticulously coordinating control and power management. The MSRS is made achievable by carefully orchestrated control and electricity management. The efficacy of the proposed system was evaluated under different solar irradiance and load conditions. The study demonstrates that implementing the SMC led to an average improvement of 2.3% in the overall efficiency of the system when compared to conventional control techniques. The maximum efficiency was observed when the system was operating under high load conditions, specifically when the state of charge (SOC) was greater than the maximum state of charge (SOCmax). The average efficiency achieved under these conditions was 97.2%. In addition, the MSRS successfully maintained power supply to the load for long durations, achieving an average sustained power of 96.5% over a period of 7.5 s. The validity of the modeling and management techniques mentioned in this study are confirmed by simulation results utilizing the MATLAB/Simulink (version: 2016, link: https://in.mathworks.com/products/simulink.html) software tools. These findings show that the proposed SMC is effective at managing energy resources in MSRS, resulting in improved system efficiency and reliability.

Assessing the Impact of Regulatory Frameworks on the Success of Green Building Projects in Singapore

This paper examines the influence of regulatory frameworks on the success of green building projects in Singapore. By analyzing the Building and Construction Authority’s (BCA) Green Mark Scheme and other related regulations, the study highlights how these frameworks have shaped the environmental sustainability practices within the construction industry. Through a mix of qualitative interviews with industry professionals and quantitative analysis of building performance data, the research demonstrates that regulatory frameworks not only enhance environmental outcomes but also drive innovation in building technologies and practices. The findings suggest that while these regulations have led to significant improvements in energy efficiency and resource management, challenges such as high compliance costs and the need for continuous professional development remain. The paper concludes with recommendations for evolving these frameworks to better support sustainable development in Singapore’s urban landscape.

Evaporator with horizontal water plane stabilization

Evaporation plays an important role in maintaining water balance and managing water resources in reservoirs. This problem is relevant in hot regions with a lack of fresh water. In addition, reliable operation of evaporators plays an important role in preventing overheating and ensuring safe cooling of reactors which is critical to maintaining nuclear safety. The complete control and accurate measurement of the evaporation process provided by the developed device enables efficient water management ensuring the stability of nuclear power plants and minimising environmental risks. Evaporators are used to control the evaporation process in reservoirs. Based on the analysis of existing evaporators and their shortcomings, an evaporator with horizontal plane stabilization in the pan is developed. Due to this function the developed device allows to control the evaporation process with higher accuracy by reducing fluctuations in the water level in the evaporator. The developed device contains an autonomous power supply that uses energy-saving technology based on a solar battery which ensures operation without additional sources. The use of such a power supply increases the evaporator reliability. One of the important advantages of this device is the use of a cellular-based information transmission channel to the control center which allows to transmit the results of evaporation quickly monitoring and make water management decisions accordingly. In addition, the use of a GPS navigation system allows to set or determine the coordinates of the evaporator installation in the reservoir accurately. The developed device can be included in an intelligent energy resource management system; its use will significantly improve the safety and sustainability of nuclear power plants by improving evaporation process control. It will also optimize the nuclear power plants operation, promote efficient management of water resources and ensure safety in the nuclear energy field.

Developing a hybrid technique for energy demand forecasting based on optimized improved SVM by the boosted multi-verse optimizer: Investigation on affecting factors

Electricity demand prediction accuracy is crucial for operational energy resource management and strategy. In this study, we provide a multi-form model for electricity demand prediction in China that based on incorporating of an upgraded Support Vector Machine (SVM) and a Boosted Multi-Verse Optimizer (BMVO). The suggested model is proposed to address the shortcomings of existing prediction approaches, which frequently fail to internment the complicated nonlinear interactions between demand for electricity and the variables that influence it. The improved SVM algorithm incorporates a modified genetic algorithm based on kernel function for enhancing the stability of the model. The BMVO technique is employed to optimize the combined model's weights and increase its generalization effectiveness. The suggested approach is tested by real-world Chinese energy demand data. The findings show that it outperforms existing prediction approaches in terms of reliability and precision. Further, the number of samples chosen affects how well the proposed BMVO linked with the Incremental SVM (ISVM) predicts outcomes. Particularly, when 1735 samples are chosen, the lowest level of Mean Absolute Percent Error (MAPE) was noted. The Root Mean Square Error (RMSE) and MAPE values under the proposed BMVO/ISVM model are reduced by 53.72% and 55.22%, respectively, compared to the Artificial Neural Network (ANN) approach reported in literature. Finally, the suggested model is capable of accurately predicting the electricity demand in China and has the potential to be applied to other energy-demand prediction applications.

The Moderating Effect of Corporate Governance Mechanisms on the Nexus between Sustainability Reporting and the Value of Nigerian-Listed Manufacturing Companies

The study investigates the Moderating Effect of Corporate Governance on the Nexus between Sustainability Reporting (SR) on the Value of listed Manufacturing Companies in Nigeria, precisely the effect of Board Size (BS) and Board Independence (BI) on the nexus subsisting between SR and FV. Secondary data for 36 sampled companies was obtained from the Nigerian Exchange Group (NGX) database for eleven (11) years, 2012–2022, which was analysed using descriptive statistics and a two-step GMM system. Findings therefrom revealed that SR exhibits a positive and significant impact on EVA. At the same time, it was found not to influence Tobin's Q. Further revealed from the analysis was that none of the CG mechanisms introduced had a moderating effect on either of the FV proxies used. The study recommended that management provide clear and concise information on how sustainability initiatives align with the company's financial goals and contribute to value creation over time. These companies should invest in energy efficiency and resource management systems, develop sustainable products and services that meet market demand and align with sustainability goals to reduce their cost of capital. Also, the Financial Reporting Council of Nigeria (FRC) should make a provision requiring companies to appoint a significant number of the board with relevant knowledge in sustainability issues and that there is the need for them to review the Nigerian Code of Corporate Governance, specifically board composition, to include sustainability committees.

Evaluation of LSTM and Wavelet Methods for Wind Speed Forecasting in Bahia, Brazil

The pursuit of sustainable development is intricately linked to the effective management of renewable energy resources, with wind energy emerging as a key player on the global stage. However, the inherent volatility and intermittency of wind patterns pose significant challenges to accurate wind speed forecasting, which is crucial for the stable operation of wind turbines. Our study presents a novel forecasting model that integrates cuttingedge data decomposition techniques with Long Short-Term Memory (LSTM) networks in this context. Our approach leverages advanced methodologies such as Wavelet Transform (WT), Empirical Mode Decomposition (EMD), and Enhanced Empirical Mode Decomposition (EEMD) to segment time series data into distinct high and low-frequency components. These segmented signals are then individually forecasted using Bidirectional LSTM (BiLSTM) networks, with the amalgamation of these predictions providing the final forecast output. Our empirical findings demonstrate that the hybrid model, particularly utilizing EMD and EEMD, exhibits superior performance compared to existing forecasting models in terms of both accuracy and stability. By effectively combining sophisticated data decomposition techniques with state-of-the-art deep learning algorithms, our proposed model offers a robust solution for wind speed forecasting. This facilitates the efficient management of renewable energy resources and advances the cause of sustainable development initiatives worldwide.

Synergistic RB5 dye degradation and oxygen evolution reaction (OER) catalysis by WO3 nano-pellets: mechanistic insights and water remediation applications

Transition metal oxides (TMO), a non-noble element based oxides, establish remarkable potential in the realm of textile wastewater remediation and water splitting due to their sustainable catalytic performance. Highly functional TMO materials, in form of nanostrcutures, are of great interest owing to their robust photo and electro-catalytic activities for instituting sustainable development for the water and energy resource management framework. In the present article, we have reported catalytically active WO3 nano-pellets (NP) synthesized using microwave-assisted for photocatalytic wastewater remediation and electrocatalytic oxygen evolution reaction (OER). Promisingly, WO3 NP abetted degradation of the reactive black 5 (RB5) dye by almost 95 % in short time interval of 60 min in exposer of sun light. Present work includes the strategies to remove and observe the progression of reaction kinetics of dye degradation in neutral and alkaline media. By varying concentration of the catalyst (ranging from 250 mg/L to 1000 mg/L, in the step of 250 mg/L), we have prolifically verified that 750 mg/L turn out optimal dosage for 100% degradation in time interval of 120 min. Besides, WO3 NP has shown highest photocatalytic behaviour at 9 pH. The WO3 NP shows the electrocatalytic OER performance in an alkaline condition (1M KOH) with an overpotential of 418 mV to generate the geometric current density of 10 mA/cm² and Tafel slope of 178 mV dec⁻¹. Owing to robust nature, WO3 NP shows the almost similar photocatalytic response for ten cycles dye degradation and demonstrates the stable electrochemical charge transport for OER at 10 mA/cm2 for 50 hours.

Enhanced complete ensemble EMD with superior noise handling capabilities: A robust signal decomposition method for power systems analysis

AbstractSignal decomposition is crucial in several domains, particularly in the dissection of complex signals present in electrical power systems. Understanding the oscillations and patterns within these signals can significantly influence energy resource management, grid stability, and efficient system operation. This paper presents an advanced enhanced decomposition method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to mitigate the inherent drawbacks of the conventional CEEMDAN and its improved version. Unlike CEEMDAN's generalized noise approach, the proposed method introduces adaptive noise, enhancing target signal noise handling by incorporating a tailored filtering and updating process after each iteration. This leads to more accurate signal decomposition compared to traditional methods. Comprehensive tests were conducted using artificially generated signals characterized by mode mixing, varying frequency oscillations, complex real‐world electrical demand signals, generator axis vibrations and partial discharge signals. The results demonstrate that the proposed method outperforms traditional techniques in two significant aspects. First, it provides superior spectral separation of the intrinsic modes (IMF) of the signal, thereby enhancing decomposition accuracy. Second, it significantly reduced the number of shifting iterations, thereby alleviating the computational load. These advancements have led to a more accurate and efficient framework that is essential for analyzing nonlinear and nonstationary signals.

Optimizing the resource allocation in cyber physical energy systems based on cloud storage and IoT infrastructure

Given the prohibited operating zones, losses, and valve point effects in power systems, energy optimization analysis in such systems includes numerous non-convex and non-smooth parameters, such as economic dispatch problems. In addition, in this paper, to include all possible scenarios in economic dispatch problems, multi-fuel generators, and transmission losses are considered. However, these features make economic dispatch problems more complex from a non-convexity standpoint. In order to solve economic dispatch problems as an important consideration in power systems, this paper presents a modified robust, and effective optimization algorithm. Here, some modifications are carried out to tackle such a sophisticated problem and find the best solution, considering multiple fuels, valve point effect, large-scale systems, prohibited operating zones, and transmission losses. Moreover, a few complicated power systems including 6, 13, and 40 generators which are fed by one type of fuel, 10 generators with multiple fuels, and two large-scale cases comprised of 80 and 120 generators are analyzed by the proposed optimization algorithm. The effectiveness of the proposed method, in terms of accuracy, robustness, and convergence speed is evaluated, as well. Furthermore, this paper explores the integration of cloud storage and internet of things (IoT) to augment the adaptability of monitoring capabilities of the proposed method in handling non-convex energy resource management and allocation problems across various generator quantities and constraints. The results show the capability of the proposed algorithm for solving non-convex energy resource management and allocation problems irrespective of the number of generators and constraints. Based on the obtained results, the proposed method provides good results for both small and large systems. The proposed method, for example, always yields the best results for the system of 6 power plants with and without losses, which are $15,276.894 and $15,443.7967. Moreover, the improvements made in the proposed method have allowed the economic dispatch problem regarding multi-fuel power plants to be solved not only with optimal results ($623.83) but also in less than 35 iterations. Lastly, the difference between the best-obtained results ($121,412) and the worst-obtained results ($121,316.1992) for the system of 40 power plants is only about $4 which is quite acceptable.

Distributed Energy Resources Management System (DERMS) and Its Coordination with Transmission System: A Review and Co-Simulation

Ever-increasing penetration of distributed energy resources (DERs) in the power grids, alongside their numerous benefits, brings new challenges that call for enhanced solutions in the field of control and management of power grids. The majority of the available research have considered either distribution or transmission grids in their studies. In this paper, a comprehensive review of the effects of DERs on the distribution and transmission grids is performed. The focus of this paper is on hierarchical management methods in order to categorize different approaches and highlight the gaps. Moreover, a review is conducted in the field of the newly introduced distributed energy resources management system (DERMS) concept. A DERMS can facilitate the hierarchical energy management procedure due to its functionalities and broad capabilities. Hence, its implementation in energy management and its impact on the power grid will be assessed with the aid of a co-simulation platform that considers both transmission and distribution grids.

Identifying major phases in the use of land, energy and changing landscapes by agrarian societies (7,000 cal BP-Present) in Cantabrian Spain, based on cultural changes and anthropogenic signals

IntroductionEnacting transitions toward more sustainable management and use of land, energy, and natural resources poses multiple challenges for human societies. Such transitions have been a constant throughout human history and therefore there is a need to learn from them and apply that knowledge to current land-use policies and management. Significant human impact on landscape and environment in Cantabrian Spain has been documented in alignment with the Neolithization (ca. 7,000 cal BP). While the classic approach of identifying cultural phases based on historical and archaeological data has been extensively studied, much less is understood on how such phases are dependent upon increasing anthropogenic influence on the environment.MethodsCantabrian Spain is well-known for its long mining history. Key processes historically shaping landscapes in the region include the implementation of mining/metallurgy industries and extraction of forest resources. These historical processes were characterized, respectively using heavy metal pollution contents (Hg, Zn, Cd, As, Ni, REE, Pb, and 206 Pb/207 Pb) and total arboreal pollen percentages in peat bogs, providing global trends of human impact on the environment. These trends were then compared to climate (temperature and precipitation) and natural vegetation evolution modeling through time.ResultsResults show seven phases of major human impact on the environment: (1) the Copper phase ca. 4,400–4,100 cal BP, (2) the Middle Bronze phase ca. 3,500–3,150 cal BP, (3) the Iron phase ca. 2,800–2,500 cal BP, (4) the Roman phase ca. 2,200–1,750 cal BP, (5) the Medieval phase ca. 1,250–1,000 cal BP, (6) the Colonial phase ca. 650–400 cal BP, and (7) the Industrial phase ca. 150 cal BP-Present.DiscussionFour phases are tightly related to substantial changes in land use and subsistence strategies: (1) Production, with the appearance of productive economies during the Neolithic, (2) Specialization, with the appearance of specialized activities and trade during the Middle Bronze phase, (3) Urbanization, with the first urban centers during the Roman phase, and (4) Globalization, with worldwide colonialism and capitalism economies during the Colonial phase.

A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence

Solar energy forecasting is essential for the effective integration of solar power into electricity grids and the optimal management of renewable energy resources. Distinguishing itself from the existing literature, this review study provides a nuanced contribution by centering on advancements in forecasting techniques. While preceding reviews have examined factors such as meteorological input parameters, time horizons, the preprocessing methodology, optimization, and sample size, our study uniquely delves into a diverse spectrum of time horizons, spanning ultrashort intervals (1 min to 1 h) to more extended durations (up to 24 h). This temporal diversity equips decision makers in the renewable energy sector with tools for enhanced resource allocation and refined operational planning. Our investigation highlights the prominence of Artificial Intelligence (AI) techniques, specifically focusing on Neural Networks in solar energy forecasting, and we review supervised learning, regression, ensembles, and physics-based methods. This showcases a multifaceted approach to address the intricate challenges associated with solar energy predictions. The integration of Satellite Imagery, weather predictions, and historical data further augments precision in forecasting. In assessing forecasting models, our study describes various error metrics. While the existing literature discusses the importance of metrics, our emphasis lies on the significance of standardized datasets and benchmark methods to ensure accurate evaluations and facilitate meaningful comparisons with naive forecasts. This study stands as a significant advancement in the field, fostering the development of accurate models crucial for effective renewable energy planning and emphasizing the imperative for standardization, thus addressing key gaps in the existing research landscape.

AI-Powered Energy Management System for Industry

The industrial sector, serving as a linchpin of global economic growth, continually shapes innovation, job creation, and wealth accumulation. However, its indispensable role is juxtaposed with substantial energy consumption, contributing significantly to the world’s energy usage and environmental footprint. In their pursuit of heightened competitiveness and profitability, industries confront a pressing challenge: the judicious management of energy resources. Striking a harmonious equilibrium between cost-efficiency, operational excellence, and environmental sustainability presents a formidable complexity. Compounding this complexity is the reluctance of traditional energy management methodologies to adapt to the dynamic and data-rich landscapes characterizing modern industrial domains. This research paper ventures into the heart of these challenges, exploring the convergence of the industrial sector, energy management, and technological advancements. Within this multifaceted landscape, it scrutinizes the intricacies of contemporary industrial environments, exposing the inherent complexities of responsible energy management. With data-driven technologies and advanced analytics as guiding lights, this study illuminates a transformative path forward—transcending conventional paradigms to forge a new era of efficiency, sustainability, and profitability