Improved Energy Management Research Articles

Management of Hybrid Hydropower and Floating Solar Systems at Num Ngum 1 in Lao PDR

The integration of a hybrid hydro-floating solar power (HPP-FPV) system is covered in this study with the goal of improving energy management and producing more electricity. The production capacity of the hybrid system is almost twice as high as that of the original HPP system, demonstrating its potential for higher energy generation. Because it forecasts weather and modifies electricity generation accordingly. The analysis highlights how crucial it is to make sure that the hybrid system's electricity generation doesn’t go beyond the current power grid's maximum transmission capacity. This is essential to preserving the consistency and caliber of the power that is provided. The research's overall goal of increasing the output of renewable energy through creative system integration and practical management techniques is captured in the abstract.

Application of Non-Parametric and Forecasting Models for the Sustainable Development of Energy Resources in Brazil

To achieve Sustainable Development Goal 7 (SDG7) and improve energy management efficiency, it is essential to develop models and methods to forecast and enhance the process accurately. These tools are crucial in shaping the national policymakers’ strategies and planning decisions. This study utilizes data envelopment analysis (DEA) and bootstrap computational methods to evaluate Brazil’s energy efficiency from 2004 to 2023. Additionally, it compares seasonal autoregressive integrated moving average (SARIMA) models and autoregressive integrated moving average (ARIMA) forecasting models to predict the variables’ trends for 2030. One significant contribution of this study is the development of a methodology to assess Brazil’s energy efficiency, considering environmental and economic factors to formulate results. These results can help create policies to make SDG7 a reality and advance Brazil’s energy strategies. According to the study results, the annual energy consumption rate is projected to increase by an average of 2.1% by 2030, which is accompanied by a trend of GDP growth. By utilizing existing technologies in the country, it is possible to reduce electricity consumption costs by an average of 30.58% while still maintaining the same GDP value. This demonstrates that sustainable development and adopting alternatives to minimize the increase in energy consumption can substantially impact Brazil’s energy sector, improving process efficiency and the profitability of the Brazilian industry.

Improved fuzzy energy management strategy for fuel cell vehicles based on IPIO

In the energy management of fuel cell vehicles (FCEV), the traditional fuzzy logic strategy has the problems of strong subjectivity and average vehicle economy. This paper proposes a composite fuzzy energy management strategy with the goal of improving the life of the auxiliary energy source power battery, using an improved pigeon swarm optimization algorithm (IPIO) to update the fuzzy membership function, while ensuring that the power battery works in a suitable range for a long time and reducing equivalent hydrogen consumption quantity. This paper conducts secondary development on the existing ADVISOR model to establish a simulation model of the FCEV hybrid system, and conducts simulation experiments under NEDC and CLTC-P operating conditions. The results show that IPIO's improved energy management strategy can charge more than twice as fast as the power following strategy when the initial SOC is low, and can reach the appropriate SOC range faster, which can extend the life of the power battery; when the initial SOC is high, the charging speed is more than twice that of the power following strategy. In this case, the equivalent hydrogen consumption of IPIO's improved composite fuzzy energy management strategy was reduced by 9.09% and 9.09% respectively in the two working conditions compared with the before improvement 11.8%, which significantly reduced the hydrogen consumption and improved the economy of hydrogen fuel cell vehicles.

Modeling of Energy Usage Intensity Based on West Java Region Conditions Using Dynamic Systems Approach

Abstract West Java is a region in Indonesia experiencing significant population growth and economic activity, leading to a substantial increase in energy demand and presenting challenges for sustainable energy resource management. Energy is fundamental to economic and social activities, and efficient, sustainable management is essential for balancing economic growth with environmental preservation. Modeling energy usage intensity is crucial for understanding the dynamics of energy consumption and its influencing factors. This study aims to develop a model that accurately represents energy consumption patterns in West Java using a dynamic systems approach. The study utilizes Causal Loop Diagrams (CLD) and Stock Flow Diagrams (SFD) to systematically map and model the intricate dynamics of the energy system, providing a comprehensive understanding of the interactions and influences among various factors. The model was validated by comparing its outputs with historical data and calculating a Mean Absolute Percentage Error (MAPE). This enhances confidence in the analyses and recommendations derived from the model, making it a valuable tool for understanding and influencing energy usage patterns in West Java. In conclusion, this study not only provides deep insights into energy consumption in West Java but also offers practical approaches for improved energy management, supporting sustainable development and environmental protection in the region.

Integrating autoencoder and decision tree models for enhanced energy consumption forecasting in microgrids: A meteorological data-driven approach in Djibouti

At this time, as the world and nations move to reduce the use of fossil fuels, research is oriented toward improving the energy consumption of people and buildings. Recent methods, mainly computing techniques such as deep learning, are proposed in the literature. This paper proposes a model that integrates the autoencoder with the decision tree model using six months of meteorological data, including solar radiation, wind speed, temperature, and other meteorological data. This study aims to advance the energy consumption prediction of loads connected to the microgrid. A case study of a microgrid park of a campus in Djibouti is presented to test the proposed model. Autoencoders were exploited to extract the main features of the meteorological data. Then the decision tree is proposed to predict the energy consumption using the resulting encoded features. The evaluation of the training of the autoencoder model gave a favorable result with a mean squared error of 0.002 and an R2 value of 0.99. Hyperparameter tuning scenarios facilitated the exploration of the decision tree model. Ensemble decision trees performed better than individual trees in this model, achieving a mean absolute error of 1.4 % and an R2 value of 0.997. It showed that hyperparameter tuning improved the results due to the best architecture fit for the decision tree. Moreover, the proposed model was validated by comparing it with the literature on KNN, SVR, and MLP models commonly used in microgrid energy management. The autoencoder decision tree outperformed other compared methods, achieving an explained variance of 0.997 and an MAE of 1.7 % in the standard decision tree regressor scenario. These results demonstrated the capabilities of machine learning and weather data. Combining an autoencoder and the decision tree model will open a new door to energy management improvement.

A Hybrid Machine Learning Approach: Analyzing Energy Potential and Designing Solar Fault Detection for an AIoT-Based Solar–Hydrogen System in a University Setting

This research aims to optimize the solar–hydrogen energy system at Kangwon National University’s Samcheok campus by leveraging the integration of artificial intelligence (AI), the Internet of Things (IoT), and machine learning. The primary objective is to enhance the efficiency and reliability of the renewable energy system through predictive modeling and advanced fault detection techniques. Key elements of the methodology include data collection from solar energy production and fault detection systems, energy potential analysis using Transformer models, and fault identification in solar panels using CNN and ResNet-50 architectures. The Transformer model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and an additional variation of MAE (MAE2). Known for its ability to detect intricate time series patterns, the Transformer model exhibited solid predictive performance, with the MAE and MAE2 results reflecting consistent average errors, while the MSE pointed to areas with larger deviations requiring improvement. In fault detection, the ResNet-50 model outperformed VGG-16, achieving 85% accuracy and a 42% loss, as opposed to VGG-16’s 80% accuracy and 78% loss. This indicates that ResNet-50 is more adept at detecting and classifying complex faults in solar panels, although further refinement is needed to reduce error rates. This study demonstrates the potential for AI and IoT integration in renewable energy systems, particularly within academic institutions, to improve energy management and system reliability. Results suggest that the ResNet-50 model enhances fault detection accuracy, while the Transformer model provides valuable insights for strategic energy output forecasting. Future research could focus on incorporating real-time environmental data to improve prediction accuracy and developing automated AIoT-based monitoring systems to reduce the need for human intervention. This study provides critical insights into advancing the efficiency and sustainability of solar–hydrogen systems, supporting the growth of AI-driven renewable energy solutions in university settings.

Advances in fuzzy control systems for energy management in smart homes

The drive towards energy efficiency and sustainability has spurred technological innovations for managing energy consumption in residences, with fuzzy control systems standing out. These systems, based on fuzzy logic, offer adaptive and intuitive solutions to optimize energy use in smart homes, dealing with uncertainties and imprecisions in environmental conditions and occupant preferences. Fuzzy logic allows for refined control of devices such as lighting, heating, ventilation, and air conditioning (HVAC), adjusting in real-time to variations in conditions and user behaviors. Recent studies have demonstrated the effectiveness of these systems in various applications. Kontogiannis, Bargiotas, and Daskalopulu (2021) developed a fuzzy control system to recommend optimal energy consumption values based on environmental data, using the Mamdani approach and decision tree linearization. Keshtkar and Arzanpour (2017) introduced an autonomous thermostat combining supervised learning with wireless sensors and dynamic electricity pricing, adjusting temperatures and maintaining user comfort. Ain et al. (2018) proposed a Fuzzy Inference System that incorporates humidity and internal temperature variations to optimize energy consumption without sacrificing comfort. Additionally, Khalid et al. (2019) presented an energy management controller for smart grids using fuzzy logic and heuristic optimization techniques, improving load management and reducing costs and consumption. Collotta and Pau (2015) explored the integration of IoT technology with fuzzy-based systems using Bluetooth Low Energy (BLE) to manage energy consumption in home automation networks. These studies highlight that fuzzy control systems are crucial for optimizing energy consumption in smart homes, balancing efficiency and comfort, with ongoing advancements promising continuous improvements in energy management.

A new representation learning based maximum power operation towards improved energy management integration with DG controllers for photovoltaic generators using online deep exponentially expanded RVFLN algorithm

To incorporate Local Energy Management System (LEMS) based Tertiary Controller (TC) operation with multiple Photovoltaic based Distributed Generations (PV-DGs) level Primary Controller/ PC: Independent DG Controllers (IDGC) more adequately, a new representation learning (Symmetrical Input-Output weight Encoded Data Compression/ SIODC, with Exponential Expansion based Random Vector Functional Link Network towards Softmax layer’s Control Reference Estimation/ ExRVFLN-SoCRE) based MPPT controller is proposed in this paper in terms of Secondary Controllers (SCs). A new Hybrid LEMS (HyLEMS) is presented towards the proposed Deep Neural Network based SCs (i.e. DNN-SCs) in a distributed (SCdist), as well as centralized (SCcent) manner, to ease the computational, and communication requirements. To investigate that multiple PV-DGs based duty cycle (kth instant Control References/CRs estimation) controllers are considered here with auxiliary Battery Energy Storage Systems (BESS), and AC utility, integrated to Common DC feeder in terms of DC-DC converter dynamics. To avoid Control Reference Estimation Error (CREE) due to initial randomization, optimization subroutines are incorporated for SIODC by generalized semi-supervised learning, and for ExRVFLN-SoCRE by Lagrange multiplier weighted, rms based cost function. The proposed control performance is verified in MATLAB Simulink® based average modeling, and validated through dSPACE DS1104 based RTI with multi-PV (emulators) test-bench as well.

Tier-specific energy benchmarking for disparate schools in developing South Africa

The increasing impacts of human-induced climate change in developing countries have spurred government policies, activism, and sustainability research aimed at reducing energy consumption. Understanding the electricity usage of buildings is crucial to cutting carbon emissions and achieving cost savings. This study addresses the challenge of establishing realistic and relevant energy benchmarks for educational institutions in developing countries, specifically focusing on the Western Cape, South Africa. Schools in this region exhibit significant differences in energy intensities but are currently assessed using the same reference standard. A top-down analysis was performed using descriptive statistics to develop energy performance benchmarks tailored to unique patterns of energy consumption in schools. Data from 31 less affluent schools were collected using smart meters to ensure accuracy. The proposed reference benchmarks, ranging from 12 to 37kWh/m2 per year, are significantly lower than the existing 60kWh/m2 per year benchmark, demonstrating substantial potential for energy savings. This nuanced benchmarking approach accounts for seasonal and term variations in energy usage, providing a more accurate comparison across schools. The research introduces a novel, context-sensitive benchmarking method that extends beyond existing standards by incorporating these variations. It underscores the importance of localized benchmarks for achieving school energy efficiency, contributing to environmental preservation and financial savings. The proposed benchmarks offer a robust framework for policymakers, standard bureaus, and education departments to craft energy efficiency policies that drive progress in the education sector. By addressing the unique energy usage patterns of schools, this approach facilitates targeted interventions, leading to improved energy management and sustainability.

Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model’s superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system’s ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.

Development of A Man-Machine Interface for Managing Photovoltaic Energy: Applied Study

Objective: Develop and evaluate a human-machine interface (HMI) that integrates advanced monitoring, forecasting, and management functionalities for photovoltaic solar energy systems, aiming to optimize energy production and operational efficiency. Theoretical Framework: This study is based on concepts of modeling and simulation, solar energy management, and problem-solving methodologies such as Soft System Methodology (SSM). Method: An applied approach was adopted using modeling, simulation, and statistical analysis techniques. The research included a bibliographic review in scientific databases, a case study, and SSM to organize and solve complex problems. 121 digital solar energy platforms in Brazil were analyzed to define the interface requirements. The interface was developed with React JS, Axios, Bootstrap v5, Apache Echarts, HTML, CSS, JavaScript, and Python libraries for forecasting models. Results and Discussion: The interface, named "Solar Smart Manager," enables efficient monitoring and management of energy production using critical data such as temperature, time of day, and solar irradiation. Tests in a real operational environment demonstrated improvements in energy management, incident response, and preventive maintenance. The functionality of validating solar radiation incidence data represents a significant contribution to the energy sector, promoting sustainability and innovation. Research Implications: The practical and theoretical implications of this research provide insights into the efficient and optimized management of photovoltaic solar energy systems, contributing to a better understanding and optimization of available solar resources. Originality/Value: This study contributes to the literature by developing an innovative interface that improves operational efficiency and solar energy management. The relevance and value of this research are evidenced by its positive impact on the energy sector, promoting sustainability and innovation.

Sustainable energy strategies among small and medium-scale enterprises: The mediating role of business associations

Small and medium-scale enterprises increasingly show greater interest in managing their environmental footprints in response to shifting customer preferences. Beyond the mixed results in the literature exploring environmental concerns from an organisational perspective, questions related to firm strategy and energy efficiency have yet to be definitively determined. This study investigated the mediating role of business associations in the relationship between firm strategy and energy efficiency among small and medium-scale enterprises. Using binary logit regression, data from over 20,000 firms across 39 countries were analysed. Our analysis demonstrates that business strategy positively impacts climate-friendly energy generation, energy management, and energy consumption improvements. Furthermore, business associations were found to significantly mediate these relationships. A comparative analysis revealed some differences across economic regions, with the impact of business strategy being more pronounced in the Middle East and Africa compared to other regions of the world. The present findings suggest that improving small and medium-scale enterprises’ affiliations to business associations is a promising approach to increasing their adoption of sustainable energy practices. The study findings further advance the understanding of the firm strategy-energy efficiency nexus, and have implications for policymaking in environmental performance enhancement.

Advancing Electricity Consumption Forecasts in Arid Climates through Machine Learning and Statistical Approaches

This study investigated the impact of meteorological factors on electricity consumption in arid regions, characterized by extreme temperatures and high humidity. Statistical approaches such as multiple linear regression (MLR) and multiplicative time series (MTS), alongside the advanced machine learning method Extreme Gradient Boosting (XGBoost) were utilized to analyze historical consumption data. The models developed were rigorously evaluated using established measures such as the Coefficient of Determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The performance of the models was highly accurate, with regression-type models consistently achieving an R2 greater than 0.9. Additionally, other metrics such as RMSE and MAPE demonstrated exceptionally low values relative to the overall data scale, reinforcing the models’ precision and reliability. The analysis not only highlights the significant meteorological drivers of electricity consumption but also assesses the models’ effectiveness in managing seasonal and irregular variations. These findings offer crucial insights for improving energy management and promoting sustainability in similar climatic regions.

Understanding Carbon Footprint in Sustainable Land-Based Marine Aquaculture: Exploring Production Techniques

In aquaculture, it is crucial to understand and mitigate the carbon footprint for sustainable production. As demand for seafood increases, various production techniques compete for an eco-friendly status. This review examines the carbon footprint of various land-based marine aquaculture systems, highlighting their environmental impact. Through exploring innovations and best practices, it navigates the complexities of reducing emissions and promoting carbon sequestration. Some proposals for this purpose are based on diversification through low-trophic-level species, the preservation of high-carbon sequestration sites, polyculture, organic aquaculture and improvements in nutrition, feeding, waste and energy management. In this sense, some land-based aquaculture systems are progressively adapting and updating their zootechnical procedures. Recirculating Aquaculture Systems (RASs) offer interesting advantages such as water conservation, pollution reduction and biosecurity. Integrated Multi-Trophic Aquaculture systems (IMTAs) aim to address two major issues in aquaculture: efficient water usage and the environmental impact of effluents, which are rich in organic particles and dissolved nutrients from undigested food and feces; hence, these systems involve cultivating multiple species (polyculture). Biofloc Technology (BFT) is based on the formation of bioflocs in a culture medium. These systems can enhance feeding efficiency and waste management, thus optimizing nutrient utilization and minimizing environmental impact, achieved through reduced water and fertilizer usage. Traditional (extensive) aquaculture systems operate with minimal input of feed and chemicals, relying heavily on the natural productivity of the ecosystems; thus, the need for manufactured feed, the environmental impact associated with feed production and the transportation and overall costs are significantly reduced. Overall, while RASs, BFT and extensive systems in general offer significant sustainability benefits, IMTA’s holistic approach to ecosystem management and nutrient recycling makes it, in our estimation, the most effective method in terms of ecological footprint in aquaculture. However, its quantitative evaluation is extremely complex, and there is currently a lack of references about its global carbon footprint. Therefore, further research and development are required, as well as collaboration and knowledge-sharing among stakeholders.

Business Models on the Energy Market in the Era of a Low-Emission Economy

In the energy market, we observe a dynamic development of innovative business models that take into account various aspects related to the direction of zero-emission economic growth. Companies are intensifying their efforts in utilizing renewable energy sources, implementing significant photovoltaic projects, and advancing technologies related to wind and hydrodynamic energy. Within this trend, microgrids become a crucial element, enabling efficient management of local energy sources. Contemporary energy companies also focus on innovative digital technologies, harnessing the potential of the Internet of Things (IoT) and artificial intelligence (AI). These tools allow for precise monitoring and optimization of energy consumption, resulting in increased operational efficiency. The expansion of subscription-based energy services encompasses not only traditional energy deliveries but also new aspects, such as intelligent management of home energy installations or the provision of advisory services on energy conservation. This approach emphasizes the customer as a partner in sustainable energy usage. Hybrid energy models, integrating diverse energy sources, constitute a key element in the transformation of the sector. The combination of photovoltaic, wind, and traditional power plants allows for flexible adaptation to changing conditions and maintains stability in the energy supply. In the face of a changing energy landscape, companies consistently strive for sustainable practices, implementing strategies that not only reduce their carbon footprint but also contribute to improving efficiency, ecology, and the decentralization of the energy system. Adapting to these dynamic changes becomes not only a challenge but also an opportunity to create a more sustainable energy future. The objective of this research is to analyze key business models in the energy market and identify their impact on operational efficiency and market competitiveness. The main results indicate significant improvements in energy management and sustainability through the adoption of these models.

Steam & Power Dashboard

Abstract— The development of the Steam and Power Dashboard represents a significant advancement in energy management practices for industrial plants. By integrating data from steam and power systems, the dashboard offers real-time monitoring and analysis capabilities, allowing plant operators to identify inefficiencies and make informed decisions to optimise energy consumption. The research study delves into the design and implementation of the dashboard, highlighting its user-friendly interface and its ability to provide actionable insights for improving plant performance. Furthermore, the impact of the Steam and Power Dashboard on energy efficiency, cost savings, and greenhouse gas emissions is a key focus of the research. The findings demonstrate that the dashboard is effective in enabling plant operators to make data-driven decisions that lead to significant improvements in energy management practices. These improvements not only result in cost savings for the plant but also contribute to reducing the plant’s environmental impact by lowering greenhouse gas emissions. Overall, this research contributes to the advancement of sustainable energy strategies in industrial settings by showcasing the benefits of utilising innovative technologies, such as the Steam and Power Dashboard, to improve energy management practices and drive towards a more sustainable future. IndexTerms – – Python, SQL, EDA

Optimal sizing and energy management strategy for an office building with photovoltaic, heat pump, thermal tank, and electric vehicle under time-of-use tariff

The increasing demand for sustainable energy solutions is driving the integration of various renewable energy technologies. Integrating electric vehicle batteries, photovoltaics, heat pumps, and thermal tanks into building energy systems offers a promising approach to enhancing energy flexibility and efficiency. However, research on such integrated energy systems is still in its infancy. Optimizing the configuration and operation of the system may lead to significant cost savings and improved energy management. A coupled model and simulation-based optimization approach are used to optimize the thermal tank capacity and key operational parameters to minimize the annual total cost. The results show that the integrated energy system reduces the annual total cost by 36.35% and monthly electricity bills by up to 69.26% compared to the base system. As for energy flexibility, only 13.91% of electricity demand is supplied by the grid during peak periods, and the monthly load coefficient, load cover ratio, and photovoltaic self-consumption ratio of the integrated energy system are significantly improved. This study demonstrates the economic and operational benefits of integrating various renewable energy technologies into building energy systems and provides new insights into their design and optimization.

A Numerical Analysis Based Internet of Things (IOT) and Big Data Analytics to Minimize Energy Consumption in Smart Buildings

The new wave of performant technology devices generates massive amounts of data. These devices are used in cities, homes, buildings, companies, and more. One of the reasons for digitalizing their tasks is that over the past few years, there has been an interest in reducing carbon emissions and increasing energy efficiency to create a friendly ecosystem and protect nature. One of which granted the explosion of data. After deploying these new devices, a significant increase in the use of the other face of energy to implement the components of the new devices was noticed. Above all, the interconnection of these intelligent devices is the central concept of the Internet of Things (IoT). This domain has widened the possibilities for the interconnection of building management systems (also named Smart Grids) and devices for better energy management. Furthermore, its potential is realized only after organizing and analyzing a large amount of data. Real-time management and maintenance of big data are critical to improving energy management in buildings. The benefits of big data analytics go beyond savings on electricity bills. It can provide comfort for building users and extend the life of building equipment, enhancing the value of commercial buildings. Intelligent interconnection of a building’s technical installations (lighting, heating, hot water, photovoltaic installations, etc.) not only allows for connected management of this equipment but also meets high energy efficiency criteria that indicate an increase in comfort and energy savings. With building automation, the technical installations of a building interact optimally. In this article, we will simulate an intelligent building based on the Cisco packet tracer software. To better manage the energy consumption of our project, we will focus on the processing of data in real-time, especially since we will have a massive amount of data generated by the sensors.

Machine Learning and image analysis towards improved energy management in Industry 4.0: a practical case study on quality control

With the advent of Industry 4.0, Artificial Intelligence (AI) has created a favorable environment for the digitalization of manufacturing and processing, helping industries to automate and optimize operations. In this work, we focus on a practical case study of a brake caliper quality control operation, which is usually accomplished by human inspection and requires a dedicated handling system, with a slow production rate and thus inefficient energy usage. We report on a developed Machine Learning (ML) methodology, based on Deep Convolutional Neural Networks (D-CNNs), to automatically extract information from images, to automate the process. A complete workflow has been developed on the target industrial test case. In order to find the best compromise between accuracy and computational demand of the model, several D-CNNs architectures have been tested. The results show that, a judicious choice of the ML model with a proper training, allows a fast and accurate quality control; thus, the proposed workflow could be implemented for an ML-powered version of the considered problem. This would eventually enable a better management of the available resources, in terms of time consumption and energy usage.

Performance analysis of drone sqadron optimisation based MPPT controller for grid implemented PV battery system under partially shaded conditions

In recent years, growing consumer demand for sustainable energy is driving down costs and paving the way for more clean energy in the future. Since these energies are sporadic, and the load pattern does not correlate to their generation pattern, a battery storage system is required. Operating dispersed alternative energy sources, connected to the grid in this situation makes energy control an unavoidable task. The difficulty is brought on by things like intermittent sources, daytime costs, solar panel and battery size restrictions, and limitations in battery charging and discharging rates. Moreover, integrating energy storage devices into the power system network is one of the recommendations made in this work to increase the performance and dependability of these systems. Compared to the existing works reported in the literature, this study offers enhanced control techniques for hybrid Photovoltaic and Battery Energy Storage System under fluctuating atmospheric scenario. Here, an innovative approach called Drone Squadron Optimisation, which tracks the Global Maximum Power Point, is used get maximum power. Further, suggested approach is tested with various partial shading instances and the uniform irradiance condition to validate it. This research also investigates photovoltaic models and the status of the battery storage device for improved energy management in the system. Finally, the proposed algorithm assists in quickly identifying the potential benefits of a grid-connected, battery-powered rooftop solar system.