Energy Consumption Patterns Research Articles

Optimization of wind-solar hybrid microgrids using swarm intelligence algorithms

The study explores the enhancement of wind-solar hybrid microgrids via the use of Swarm Intelligence Algorithms (SIAs). It assesses the efficacy of these algorithms in efficiently managing renewable energy sources, load demands, and battery storage inside the microgrid system. An examination of actual data highlights the influence of environmental elements on the production of electricity, as seen by the diverse wind speeds resulting in power outputs that range from 15 kW at 4 m/s to 30 kW at 7 m/s. This underscores the clear and direct relationship between wind speed and the amount of power created. Likewise, solar irradiance levels demonstrate oscillations ranging from 500 W/m² to 800 W/m², therefore yielding power outputs that include a range of 15 kW to 24 kW, so illuminating the profound impact of solar irradiance on energy capture. The dynamic energy consumption patterns are exposed by the varying load demands, whereby the demand levels oscillate between 20 kW and 28 kW. This highlights the crucial significance of demand variability in determining energy needs. In addition, the data on battery storage reveals a range of charge levels, ranging from 25 kWh to 40 kWh, which underscores its pivotal function in the equilibrium of energy supply and consumption. When evaluating SIAs, it becomes evident that Particle Swarm Optimization (PSO) surpasses both Ant Colony Optimization (ACO) and Genetic Algorithms (GA) in obtaining an impressive 80% renewable energy penetration rate. PSO effectively reduces operating costs by 15%, demonstrating its exceptional proficiency in optimizing microgrid operations. This study provides valuable insights into the intricate interplay among environmental conditions, load demands, battery storage, and algorithmic optimization in wind-solar hybrid microgrids.

Sustainable Energy Management in Tertiary Educational Buildings: A Case Study of Nigeria's Federal Capital Territory

This article presents a case study conducted in Nigeria Federal Capital Territory, examining the current status, challenges, and potential strategies for sustainable energy management in tertiary educational buildings. The research analyzes the energy consumption patterns, prevalent sources, and the diverse appliances used in lecture halls across six (6) selected institutions. Findings reveal lights, fans, projectors, computers, speakers, and microphones as the most combined and essential items for lecture environment as recorded by 25.7% of the respondents and about significant 72.2% of respondents lack awareness regarding their lecture hall's energy consumption, emphasizing an information gap that may hinder the adoption of sustainable energy management practices. Despite this, a considerable proportion 27.5% expresses satisfaction with current energy consumption levels, indicating potential complacency and a need for heightened awareness. Notably, varied perceptions of about 58.2% exist regarding excessive energy usage and the potential for reduction without compromising service quality. The study identifies barriers to implementing sustainable energy management practices, with staffing limitations, complex decision-making processes, and initial costs ranking highest with an Relative Importance Index (RII) of 0.772, 0.759 and 0.762 respectively. Conversely, strong leadership commitment, sustainability education in the curriculum, and environmental regulations emerge as key drivers for fostering sustainable energy management practices with an average RII of 0.791, 0.779 and 0.778 respectively. Proposed strategies for enhancing sustainable energy management include the deployment of smart grid systems, educational initiatives for students and staff, efficient building designs, and the implementation of renewable energy sources. Respondents exhibit a willingness to embrace these strategies, contingent on the availability of necessary resources and support. In conclusion, this research underscores the need for improved data collection on energy consumption, heightened awareness campaigns, and strategic policy interventions to facilitate the transition to sustainable energy practices in educational buildings.

Smart grid power load type forecasting: research on optimization methods of deep learning models

Introduction: In the field of power systems, power load type prediction is a crucial task. Different types of loads, such as domestic, industrial, commercial, etc., have different energy consumption patterns. Therefore, accurate prediction of load types can help the power system better plan power supply strategies to improve energy utilization and stability. However, this task faces multiple challenges, including the complex topology of the power system, the diversity of time series data, and the correlation between data. With the rapid development of deep learning methods, researchers are beginning to leverage these powerful techniques to address this challenge. This study aims to explore how to optimize deep learning models to improve the accuracy of load type prediction and provide support for efficient energy management and optimization of smart grids.Methods: In this study, we propose a deep learning method that combines graph convolutional networks (GCN) and sequence-to-sequence (Seq2Seq) models and introduces an attention mechanism. The methodology involves multiple steps: first, we use the GCN encoder to process the topological structure information of the power system and encode node features into a graph data representation. Next, the Seq2Seq decoder takes the historical time series data as the input sequence and generates a prediction sequence of the load type. We then introduced an attention mechanism, which allows the model to dynamically adjust its attention to input data and better capture the relationship between time series data and graph data.Results: We conducted extensive experimental validation on four different datasets, including the National Grid Electricity Load Dataset, the Canadian Electricity Load Dataset, the United States Electricity Load Dataset, and the International Electricity Load Dataset. Experimental results show that our method achieves significant improvements in load type prediction tasks. It exhibits higher accuracy and robustness compared to traditional methods and single deep learning models. Our approach demonstrates advantages in improving load type prediction accuracy, providing strong support for the future development of the power system.Discussion: The results of our study highlight the potential of deep learning techniques, specifically the combination of GCN and Seq2Seq models with attention mechanisms, in addressing the challenges of load type prediction in power systems. By improving prediction accuracy and robustness, our approach can contribute to more efficient energy management and the optimization of smart grids.

ANALYSIS OF THE PATTERNS AND DETERMINANTS OF HOUSEHOLDS’ COOKING ENERGY DEMAND IN YOBE STATE, NIGERIA

The paper investigates household cooking energy consumption pattern in Yobe State, Nigeria utilizing secondary data sourced from Nigeria’s COVID-19 national longtitudinal phone survey (NLPS, 2020). Descriptive statistics and ordinarily least squares estimation technique were employed in the analysis of the data. The findings indicate that about 66.7% of the households in Yobe are multiple fuel users. Additionally, among the specific fuel users about half (50%) of the users relied on fuelwood for their household needs. With regards to the determinants of energy sources, income, household size and age were found to be positive and statistically significance at 1% for all the energy sources. The influence of income was the most substantial for charcoal. Household size has the most influence on fuelwood. Similarly, Older households seem to consume more of all fuels. The results for area of residence was only positive and and statistically significance at 1% for kerosene demand while Gender does not significantly affect any fuel type demand. The study concludes that socio-economic factors influence cooking energy demand in Yobe State.Hence, recommends that various stakeholders should support economic empowerment, energy education and promote outreach for specific demographics.

The Role of AI in Advancing Sustainable Human Resource Management Practices

Purpose of the Paper: The purpose of this research paper is to explore the potential of artificial intelligence (AI) in promoting sustainable workplaces, specifically in the context of green HR. The motivation behind this study is the growing interest in environmental sustainability and the need for organizations to adopt sustainable practices. The integration of AI in HR practices has the potential to promote sustainability and reduce the carbon footprint of organizations. Problem Identified: The problem addressed in this paper is the limited understanding of the potential of AI in promoting sustainable workplaces, specifically in the HR domain. The paper aims to provide an in-depth analysis of the potential benefits and challenges of incorporating AI in HR practices to promote environmental sustainability. Approach: The approach taken in this study involves a thorough review of relevant literature on AI, green HR, and sustainable workplaces. The paper discusses the role of AI in promoting sustainable workplaces, including identifying patterns of energy consumption and reducing waste through AI-powered energy management systems such as automated lighting and heating systems and smart building technologies. Results: The paper summarizes the benefits of adopting sustainable workplace practices, including reduced costs, improved employee health, and enhanced corporate reputation. It also highlights case studies of companies that have successfully implemented sustainable workplace practices. The challenges of adopting AI for green HR are discussed, including cost, security, and privacy concerns, and the need for skilled AI professionals. The potential impact of AI on job displacement and the need for reskilling and upskilling are also analyzed. Strategies for implementing AI for green HR are presented, including best practices for effective change management, stakeholder engagement, and collaboration with AI vendors and suppliers. The importance of ethics and transparency in AI implementation is also discussed. Conclusion: In conclusion, the paper presents key findings and recommendations for companies seeking to implement AI for green HR. It reflects on the future of AI for sustainable workplaces and potential areas for future research. The study provides valuable insights into the potential of AI in promoting sustainable workplaces and serves as a foundation for further research in this area. Keyword: AI, Human Resource, Sustainable

THE ASSOCIATIONS OF ENERGY CONSUMPTION AND EXPENDITURE WITH SELF-PERCEIVED WELL-BEING AMONG CHINESE ADULTS

Abstract Background and Objectives Households in China have gradually moved to use of cleaner fuels, but the proportion using solid fuels in daily life remains as high as 30%, resulting in approximately 420,000 premature deaths annually. Energy use and its impact on health may differ by age, as less active lifestyle in later life may lead to increased household energy consumption aggravating poor health consequences. The twofold aims of this study are: 1) to examine energy consumption patterns and expenditure between young Chinese (aged 18-59) and older Chinese (aged 60 or above), and 2) to identify how energy consumption patterns and expenditure are associated with self-perceived wellbeing in the two respective age groups. Methods A total of 2,303 valid samples (M age = 51, SD = 16.2) were obtained from Chinese General Social Survey (CGSS) in 2015. Hierarchical linear modelling analyses were employed using HLMs 6.08 for each age group to examine the influence of individual-level factors and cluster-level covariates (i.e., environmental pollution, health care resources, and economic development at provincial level). Results Among the young adults, those using oils, gas, and electricity reported better self-perceived wellbeing than those using solid fuels. Among the older adults, reliance on fuel gas and energy expenditure were associated with better self-perceived wellbeing. Conclusions The harnessing of cleaner energy has the potential to increase subjective wellbeing across age groups. For older adults, the higher energy expenditure might indicate a dormant or isolated lifestyle, which calls for practice interventions to improve social engagement and promote wellbeing.

Leveraging Information Systems, Big Data Analytics, and AI for Energy-Efficient Design of Rural Residences

The integration of Information Systems (IS), Big Data Analytics (BDA), and Artificial Intelligence (AI) has ushered in a new era of energy-efficient design for rural residences. This study delves into the intricate synergy between technology and sustainability, unveiling the transformative potential of these tools in reshaping rural living spaces. The exploration spans from the conceptualization of designs to their real-world implementation, highlighting the pivotal role of IS in facilitating collaborative efforts among stakeholders. The study further uncovers the power of Big Data Analytics in deciphering energy consumption patterns, climatic variations, and occupant behaviours. These insights lay the groundwork for AI-powered simulations that optimize energy efficiency while ensuring occupant comfort. The study underscores the consequences of ineffective design, elucidating how it amplifies energy consumption, escalates environmental impact, and compromises residents' quality of life. In contrast, the integration of IS, BDA, and AI results in energy-efficient residences, marked by reduced energy usage, enhanced indoor comfort, and economic savings. Despite challenges such as limited resources, harsh climates, and technical expertise gaps, innovative solutions in the form of training programs, data privacy protocols, and collaborations emerge as beacons of progress. Looking to the future, emerging trends like smart grids, Internet of Things (IoT) integration, and AI-driven predictive maintenance shape the narrative of rural residences design. Rural communities stand poised for self-sufficiency and sustainability, empowered by the fusion of technology and ecological mindfulness. The recommendations presented in this study offer actionable insights for construction professionals, policymakers, and researchers, emphasizing interdisciplinary collaboration, continuous monitoring, and ongoing training. Future directions include greater investigation of new trends in sustainability, smart grids, and predictive maintenance, which will help rural communities become self-sufficient and environmentally conscientious.

An integrated economic, energy, and environmental analysis to optimize evaluation of carbon reduction strategies at the regional level: A case study in Zhejiang, China

China plays a crucial role in responding to global climate change. Provinces are the main sources of energy consumption and greenhouse gas emissions in China's economic and social development. However, it is still unclear how to achieve dual-carbon goals by formulating and implementing local policies to adapt to climate change. In this study, we take Zhejiang Province in China as the research object, based on the LEAP (Low Emissions Analysis Platform) model to construct four social scenarios under different policies, comprehensively considering regional economic characteristics, population, and energy consumption patterns. The results show that to achieve Zhejiang Province's goal of carbon peaking by 2030 while maintaining steady economic growth, additional measures are required to reduce energy consumption intensity or improve the power generation structure. Otherwise, energy demand will increase to 228.06 million tonnes of coal equivalent and carbon emissions will be 487.76 million tonnes in 2050. Moreover, developing clean energy and promoting CCUS technology can continuously reduce carbon emissions to 293.59 and 210.76 million tonnes respectively. The economic viability of CCUS power generation is contingent upon the development of carbon taxes in the future. Once the growth rate reaches 7.2%, power cost will be 167.77 billion RMB and CCUS will become economically advantageous in 2050.

From Zero Energy to Zero Power Buildings: A new paradigm for a sustainable transition of the building stock

This research introduces an innovative method aimed at evaluating building energy performance within urban environments and communities, departing from the conventional Zero Energy Building (ZEB) concept and introducing the Zero Power Building (ZPB) framework. This approach, overcoming the limitations of the ZEB concept, offers a holistic definition in line with the goal of achieving a Zero-emission Building stock. By employing a power balance assessment coupled with dynamic Key Performance Indicators (KPIs), this methodology surpasses traditional techniques, providing a more accurate assessment of energy consumption and generation patterns. The method was applied to a district case study encompassing seven nearly Zero Energy Buildings (nZEBs), demonstrating its potential efficacy. Findings indicate how the proposed framework can accurately evaluate the integration of energy storage and sharing strategies, increasing the Zero Power target from 29 % to 51 % of annual hours. KPIs were also dynamically examined at the building and district level, introducing the concept of KPI accuracy and identifying pivotal hourly patterns to guide a sustainable transistion. The adoption of the Zero Power Building framework and the utilization of dynamic KPIs offer viable solutions to address conventional limitations in building energy assessments. This approach aids in guiding targeted energy efficiency measures aimed at fostering sustainable and Zero-emission Communities.

Categorization of Indian residential consumers electrical energy consumption pattern using clustering and classification techniques

Data Mining techniques are widely applied in power sector to analyze the electricity consumption pattern(ECP) of consumers for implementing effective demand response management. In this study, the ECP of LV consumers of Karur city is used for evaluation. The dataset consists of five years ECP. In this study, K-means clustering algorithm is used to categorize the data into binary and three classes. The results of the K-means clustering technique is used for developing eight classifier models. The hyperparameters of the classifier models are tuned using grid search optimization technique. The classification process applies hold-out partitioning and stratified 10-fold cross-validation(CV) techniques to evaluate the classifier’s efficiency in predicting a consumer’s ECP. The experimental result shows that the K-means clustering has minimal overlapping of 0.48% and 1.98% for binary and three-class clustering processes. Hence, the K-means algorithm results are used to build this study’s classification model. In the classification process, the experimental results of LR and SVM algorithms have better accuracy in binary and three class categories respectively, for the hold-out partitioning method. Meanwhile, the SVM also has better generalization ability and is validated using stratified 10-fold CV, achieving an accuracy of 99.09% and 98.19% for binary and three classes respectively, in the classification of consumer data.

Dwelling, Habits, and Possessions: Clustering Turkey’s Household Energy Choices through Responsible Consumption and Poverty

Household energy consumption is influenced by a variety of factors, including climate, demographics, and socio-economic conditions. This study is a review based on clustering analysis of data from the 2019 Household Budget Survey in Turkey, and explores the role of dwelling structure, household habits, and possessions as indicators of energy patterns and preferences within distinct household groups contributing to Sustainable Development Goals 1, 7, and 12. The data analysis identifies three distinct clusters with specific features that set them apart. The Urban Majority cluster represents densely populated urban areas where natural gas is a preferred fuel source, and parquet and ceramic tiles are common flooring materials. The Deprived-like Remote Residents cluster portrays areas with less access to natural gas, where wood and coal are frequently used for heating, and the Urban Comfort-Seekers cluster highlights urban areas with high comfort levels, featuring natural gas, floor heating, and various amenities. These clusters reflect energy consumption patterns and social aspects related to poverty and habits. They indicate that Turkey’s energy preferences are closely linked to accessibility, user-friendliness, and affordability. Urban households prioritize comfort, while rural areas are more resource-constrained, and contextual factors like location and possessions play a vital role in energy preferences.

DATA SCIENCE IN ENERGY CONSUMPTION ANALYSIS: A REVIEW OF AI TECHNIQUES IN IDENTIFYING PATTERNS AND EFFICIENCY OPPORTUNITIES

This review critically examines the role of Data Science and Artificial Intelligence (AI) techniques in energy consumption analysis, focusing on their efficacy in identifying patterns and uncovering efficiency opportunities. The primary objective is to assess how AI methodologies are transforming energy consumption analysis, with an emphasis on pattern recognition and optimization of energy efficiency. The study adopts a systematic literature review approach, scrutinizing peer-reviewed articles published between 2015 and 2022. This methodological framework ensures a comprehensive and relevant analysis of current AI applications in the energy sector. Key findings reveal a significant evolution from traditional energy analysis methods to sophisticated AI-driven techniques. AI has proven instrumental in accurately predicting energy consumption patterns, facilitating enhanced decision-making for energy management. The review identifies various AI techniques, including machine learning, deep learning, and predictive analytics, and their specific applications in energy consumption analysis. The study also delves into the technological, economic, and environmental implications of integrating AI in energy analysis, highlighting both the challenges and potential solutions. It underscores the growing trend of AI applications in enhancing energy efficiency and the emerging opportunities therein. This offers a comprehensive overview of current trends and future directions, serving as a guide for industry stakeholders, policymakers, and researchers in harnessing AI for more efficient and sustainable energy consumption analysis.
 Keywords: Artificial Intelligence, Efficiency Optimization, Pattern Recognition, Energy Consumption Analysis.

A hybrid model of self-organizing map and deep learning with genetic algorithm for managing energy consumption in public buildings

Intelligent applications have become essential for effectively managing energy consumption in public buildings. Energy management, especially for critical infrastructure buildings and public buildings, often serves as an example of sustainable practices since it tries to increase energy resilience through backup power systems. These structures were dealing with more difficulties, such as tight budgets, complicated regulations, behavioral issues, and others. Managing energy consumption in buildings is a multifaceted undertaking that entails grappling with non-uniform energy usage and a lack of established design guidelines for implementing energy-efficient and sustainable solutions. Consequently, analyzing energy utilization trends in public buildings and projecting future energy requirements is of paramount importance. This comprehension is indispensable to the identification and acknowledgment of energy consumption patterns in commercial and institutional buildings. Our research is highly significant for optimizing energy usage, reducing environmental impact, and making informed decisions in building management. We emphasize the importance of our predictive capabilities, sustainability, and life cycle assessment (LCA) techniques in achieving these objectives. Our hybrid model improves energy efficiency while also making a significant contribution to the larger objective of meeting sustainability standards in the built environment through thorough analyses of real-world data. The goal of this study is to determine the most sensible classification and forecasting scheme for the energy usage rates of public structures. The goal of this study is to determine the most sensible classification and forecasting scheme for the energy usage rates of public structures. In order to determine the number of energy consumption pattern clusters, a Self-Organizing Map (SOM) model was utilized in conjunction with Principal Component Analysis (PCA). The determination of clustering levels for each structure was executed by utilizing K-means in conjunction with a Genetic algorithm (GA). This approach enabled the identification of optimal clustering levels for the given structures, thereby facilitating effective analysis and interpretation of the data. The GA was specifically employed to determine the optimal centroid points for each cluster, thus optimizing the performance of the fitting model. Cluster analysis pattern extraction has made determining which buildings consume the most energy easier. As intelligent models for predicting energy usage, Convolutional Neural Networks (CNNs) and CNNs paired with a GA have also been used. The application of a GA to modify multiple settings of a CNN was conducted at this stage. The CNN model using a GA shows the method that was used provides higher accuracy and standard error compared to the conventional approach. It achieves a 94.01% accuracy on the training dataset and a 93.74% accuracy on the validation dataset with an error rate of 0.24 and 0.26 respectively. On the training dataset, the accuracy is 89.03% with a standard error of 0.3, whereas on the validation dataset, the accuracy is 88.91% with a 0.33 standard error. This research can help policymakers in the energy sector make better decisions regarding the timing of energy supply and demand for public buildings.

Analyzing Energy Consumption Patterns at Mbarara University of Science and Technology: A Case Study of the Academic Calendar Influence

This research paper presents a concise and factual analysis of energy consumption patterns at Mbarara University of Science and Technology (MUST) based on historical data from the calendar year 2019. The study focused on the interplay between the academic calendar and energy usage, revealing variations in energy demand. Key findings emphasize the necessity for tailored energy management strategies to accommodate these fluctuations. We introduced a linear regression model for precise energy predictions, highlighting the potential for future refinement. The research offers valuable insights into data-driven energy optimization at the institution, with implications for sustainability and cost-effectiveness.

Transitioning towards renewable energy and sustainable storage solutions at remote communities in the Arctic, Case study of Flatey, Iceland

The need for transitioning towards renwable energy and sustainable storage solutions is particularly challenging for remote communities in the Arctic, located far away from the electricity grid. This paper explores the potential for use of renewable energy on the remote island of Flatey, Iceland, which currently relies on two diesel aggregates for power. The primary goal is to assess the feasibility of transitioning from fossil fuels to renewable energy sources, such as solar or wind power, to meet the island’s variable energy demand while reducing its environmental impact. With a year-around population of 5 and with a considerably increased energy consumption during vacation times, due to more population, Flatey’s annual energy consumption is ~ 209.000 kWh, peaking in July at ~ 25.000 kWh. This fluctuation requires an adaptable and resilient energy infrastructure. The paper examines the viability of Flatey as a self-sufficient renewable energy provider. The study considers the island’s energy requirements, consumption patterns, and geographical constraints, while also evaluating technical, economic, and social factors that may influence renewable energy adaption. This paper, thereafter, investigates the feasibility of achieving energy self-sufficiency on the small island of Flatey. Different energy storage options is considered, focusing on battery storage, underground solar power/energy storage, and hydrogen storage.

HUBUNGAN ASUPAN ZAT GIZI MAKRO DENGAN STATUS GIZI ANAK SEKOLAH DASAR DI WILAYAH PESISIR KOTA MATARAM

Nutritional problems can be in the form of macronutrient problems and micronutrient problems. One group that requires attention in consuming food and nutrients is school-aged children. School age children are children aged 6-12 years where they experience the second growth spurt after toddlerhood. School-aged children on the coast are a group that is at high risk of experiencing nutritional status problems. Adequate nutritional intake is very influential for optimal growth, development and children's health. This study aims to determine the relationship between macronutrient intake and the nutritional status of elementary school children in the coastal area of Mataram City. The method used in this research is an analytical survey with a cross sectional design and using a total sampling method. The number of samples used was 140 students in grades IV and V at SDN 18 and 38 Ampenan. Nutritional intake data was obtained through the Recall questionnaire, while nutritional status data was obtained from BMI calculations obtained from measuring height and weight. Research data was processed using the Spearman correlation test with a value of α = 0.05. The test results show the p value = 0.000. Based on these results, it can be concluded that there is a significant relationship between energy, protein, fat and carbohydrate consumption patterns and nutritional status.

Whale optimization algorithm and internet of things for horizontal axis solar tracker-basedload optimization

<p>Renewable solar energy is the future of all other resources because of its reliability and availability all over the earth. Optimization of the energy consumption and utilization of internet of things (IoT) devices deployed in such systems poses significant challenges. Axis tracker panel is the scope for the next decade toincrease the performance of the existing panels. This research focuses on the development of intelligent energy optimization algorithms for IoT devices. The integration of renewable energy sources and IoT devices in solar-microgrid energy systems offers promising solutions for sustainable and efficient energy management. The proposed whale optimization algorithm (WOA) takes into account dynamic factors, including varying energy availability and fluctuating demand patterns, to optimize the overall performance. Leveraging real-time data from IoT sensors and smart meters, the algorithms balance energy generation and consumption, prioritize critical loads, and incorporate energy forecasting techniques to handle fluctuations in renewable energy production. Moreover, they integrate demand response mechanisms and dynamic pricing models to encourage flexible energy consumption patterns and minimize operational costs. The results of this study demonstrate the significant potential of the WOA algorithm in enhancing the sustainability of microgrid energy systems, paving the way for a greener and more reliable energy future.</p>

Smart Energy Meter using IOT

In order to transform the monitoring of energy use, this study presents a Smart Energy Metre (SEM) system that is integrated with Internet of Things (IoT) technology. IoT-enabled sensors are used by the system to gather data on electricity use in real time. With the use of cloud-based analytics and seamless connectivity, the suggested solution gives consumers insight into their patterns of energy consumption. Predictive analytics, anomaly detection, and remote monitoring are important aspects that support well-informed decision-making for efficient resource management. In addition to improving energy efficiency, the deployment of an Internet of Things-based Smart Energy Metre also helps create a more robust and sustainable energy infrastructure.

Personalized PV system recommendation for enhanced solar energy harvesting using deep learning and collaborative filtering

Electricity is a crucial aspect of modern life, and with the increasing population and industrialization, energy demand has risen significantly. A swift transition to renewable energy sources such as wind and solar is essential for saving the planet. Solar energy is one of the most widely used renewable energy solutions, but choosing a PVSC poses a challenging problem that involves considering various factors, such as geographical location and energy consumption patterns. In this study, we investigate the effectiveness of using machine learning techniques to assist users in selecting the most suitable PVSC for their needs. We propose a new framework for PVSC recommendation, which encompasses a PV power forecasting model and a PV configuration recommendation system.We propose two forecasting models, one based on LSTM and the other based on CNN architecture. These two elements are responsible for extracting relevant features from time-series meteorological data, which are then passed to a feed-forward MLP layer that predicts the monthly energy production for different PVSCs. Subsequently, the prediction results are utilized to determine the optimal Residential scale PVSC for households, taking into account location and historical consumption data. The system was trained on simulated data and then tested on both a hold-out simulated test set and a real-world dataset. Our experiments show that the proposed framework is effective for long-term PV power forecasting and PVSC recommendation.

Design of Power Factor Monitoring System Based on Android Application

Electrical energy is an essential resource for human needs. The prolific utilization of electrical devices accounts for high energy consumption patterns. Resistive and inductive loads characterize conventional electrical equipment. In practice, the properties of electrical loads impact energy demand and system efficiency. Thus, power factor correction presents a viable strategy to improve electrical energy efficiency. This research aims to develop an Internet of Things-integrated power factor monitoring system. When connected to Wi-Fi, the system employs a PZEM-004T sensor to monitor current, voltage, power, and power factor measurements from the load in the absence of active monitoring. The ESP32 microcontroller processes the sensor data. Then, control programs running on the microcontroller instruct a relay to engage capacitive banks accordingly. The system displays output metrics on a Liquid Crystal Display and Android application. Experimental results indicate that a single-phase electric motor operates at a baseline power factor of 0.31. However, integration of the factor correction tool detailed herein improves the power factor to 0.98 for the given load.