Energy Management Practices Research Articles

Big Data and Machine Learning-Based Iot Models for Sustainable Energy Prediction

Integrating Big Data and Internet of Things (IoT) platforms is the focus of this research, which aims to improve energy management. The problem statement is centered on the potential for development through advanced technologies and the inefficiencies in traditional energy management methods. The objectives are to analyze energy consumption patterns, develop an innovative Home Energy Management System (HEMS) architecture, and offer energy-saving solutions. Synthetic energy consumption data is generated, normalized, and divided into training and testing sets from a methodological perspective. K-nearest neighbors, Decision Trees, Support Vector Regression, and Random Forest are the machine learning models trained and evaluated. The Random Forest model outperforms other models in terms of the accuracy of its predictions of energy consumption. The integration of renewable energy sources with cutting-edge technology to revolutionize energy management practices is the essence of novelty. In conclusion, this investigation underscores the importance of utilizing advanced technologies to promote sustainable energy management, providing practitioners and policymakers with practical insights.

Prediction Model for Electricity Energy Consumption and Pricing Using Xgboosts With L1 Regularization

The increasing global demand for electricity, driven by rapid industrialization, urbanization, and digitalization, necessitates more accurate and efficient predictive models for electricity generation and consumption. Traditional statistical techniques often fall short in incorporating the complex environmental factors influencing energy demand. This study presents an advanced machine learning model, leveraging XGBoost with L1 regularization, to improve the accuracy of electricity consumption and generation predictions. By integrating comprehensive datasets, including economic indicators and weather variables, the model addresses the critical gaps in existing forecasting methods. The research involved rigorous data pre-processing, feature engineering, and model validation using metrics such as RMSE, MAPE, and MAE. The model achieves a RMSE of 2.212 and MAPE of 2.393%, indicating good predictive performance. Effective data cleaning, feature engineering, and the application of PCA contributed to this result. The results demonstrate significant improvements in prediction accuracy, highlighting the potential of the proposed model to enhance energy management practices, optimize grid operations, and support the integration of renewable energy sources. This study provides a robust framework for future research and practical applications, contributing to the advancement of sustainable energy systems and informed policy-making. The findings underscore the importance of integrating diverse data sources and sophisticated machine-learning techniques to meet the growing challenges in the energy sector.

CRISP-DM-Based Data-Driven Approach for Building Energy Prediction Utilizing Indoor and Environmental Factors

The significant energy consumption associated with the built environment demands comprehensive energy prediction modelling. Leveraging their ability to capture intricate patterns without extensive domain knowledge, supervised data-driven approaches present a marked advantage in adaptability over traditional physical-based building energy models. This study employs various machine learning models to predict energy consumption for an office building in Berkeley, California. To enhance the accuracy of these predictions, different feature selection techniques, including principal component analysis (PCA), decision tree regression (DTR), and Pearson correlation analysis, were adopted to identify key attributes of energy consumption and address collinearity. The analyses yielded nine influential attributes: heating, ventilation, and air conditioning (HVAC) system operating parameters, indoor and outdoor environmental parameters, and occupancy. To overcome missing occupancy data in the datasets, we investigated the possibility of occupancy-based Wi-Fi prediction using different machine learning algorithms. The results of the occupancy prediction modelling indicate that Wi-Fi can be used with acceptable accuracy in predicting occupancy count, which can be leveraged to analyze occupant comfort and enhance the accuracy of building energy models. Six machine learning models were tested for energy prediction using two different datasets: one before and one after occupancy prediction. Using a 10-fold cross-validation with an 8:2 training-to-testing ratio, the Random Forest algorithm emerged superior, exhibiting the highest R2 value of 0.92 and the lowest RMSE of 3.78 when occupancy data were included. Additionally, an error propagation analysis was conducted to assess the impact of the occupancy-based Wi-Fi prediction model’s error on the energy prediction model. The results indicated that Wi-Fi-based occupancy prediction can improve the data inputs for building energy models, leading to more accurate energy consumption predictions. The findings underscore the potential of integrating the developed energy prediction models with fault detection systems, model predictive controllers, and energy load shape analysis, ultimately enhancing energy management practices.

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.

Assessing residential sustainable energy autonomous buildings for hot climate applications

This paper introduces an innovative solution for clean energy autonomous building (AB) sustainability, offering a 5Z concept—zero-carbon, zero-energy, zero grid connections, zero energy bills, and zero emission mobility. This paper focuses on fundamental research to design sustainable, energy-ABs striving for self-sufficiency in arid climates, using Kuwait as a case study. The study highlights the importance of stringent engineering AB modeling, renewable technology integration, and clean energy storage. The technical approaches include characterizing non-thermal and thermal demands, achieving net-zero energy generation, and custom sizing renewable energy and energy storage systems (ESS) for electric vehicle (EV) charging points. The research methodology involves local construction with yearly weather and energy profile data to simulate the actual system using ESP-r building model. The study's findings reveal the need for a large-scale energy system, energy demand reduction (EDR) inclusive of heating, cooling, appliances, and EV, with the corresponding changes in local energy production system size, battery capacity, and the number of photovoltaics (PVs). The results show varying EDR levels ranging from base case to 10% and to 50% leading to proportional changes in energy system size, size of battery from 3415 kWh to 1710 kWh and the number of PVs from 362 to 181, which means EDR not only optimizes space but also proves cost-effective. The significant implication of this study, not only bridging the knowledge gap and the lack of how-know, but also making a significant advancement and forward thinking in sustainable green electric home modernization and environmentally friendly transportation. This approach transforms energy management practices for more sustainable cities and societies, reducing emissions in both urban infrastructure and transportation.

Policy Evaluation and Enhanced Operational Strategies for PV-Linked ESS in Korea: Practical Applications and Insights from Real-World Data

With the intensification of the global commitment to renewable energy, South Korea’s rapid expansion in renewable capacity necessitates efficient operational strategies to address the inherent variability of these energy sources. Despite the implementation of policies aimed at integrating energy storage systems (ESS) with renewable generation, such as providing additional renewable energy certificates (RECs), the measures undertaken remain relatively ineffective. Thus, this study evaluates the shortcomings of existing policies and proposes an innovative operational strategy tailored to Korea’s energy landscape. The strategy is implementable immediately using existing facilities, without requiring any new equipment, and facilitates profits of up to 12.45% greater compared with those generated using the original approach. Moreover, the results are nearly as effective as an ideal scenario wherein the PV generation and system marginal price are accurately known. Additionally, the decreased economic feasibility of ESS owing to the discontinuation of subsidies is highlighted and solutions are proposed to mitigate the problem. This underscores the urgent need for improved regulatory measures and enhanced operational strategies. The proposed approach highlights the potential to considerably reduce inefficiencies and operational costs, thereby contributing to more sustainable energy management practices within Korea.

Study Of Energy Management And Efficiency In Industrial Processes: Analysis Of Implementation And Its Impact On Productivity

This study focuses on the implementation and analysis of energy management and efficiency in the context of industrial processes to enhance productivity. Drawing upon literature review on the fundamental concepts of energy management and best practices in industries, this research identifies strategies and methods that can be applied to optimize energy use in manufacturing environments. The research methodology involves gathering data from various sources to evaluate the practical implementation of energy management strategies in manufacturing industries, including pre- and post implementation data analysis. The findings provide deep insights into the effectiveness of specific measures in reducing energy consumption, improving operational efficiency, and their overall impact on productivity. The study also highlights challenges that may arise in implementing energy management strategies, such as initial costs, staff training needs, and technology integration. However, the long-term benefits of efficient energy management include reduced operational costs and environmental sustainability. By combining technical and managerial approaches, this research provides a foundation for better decision-making in adopting sustainable practices in the industrial sector.

Development of an ARIMAX model for forecasting airport electricity consumption in Accra-Ghana: The role of weather and air passenger traffic

The rapid expansion of the aviation industry has significantly increased energy consumption within airport terminals. This study introduces an advanced ARIMAX model to forecast electricity consumption at Kotoka International Airport (KIA) in Ghana by incorporating weather conditions and air passenger numbers. By analysing data from 2019 to 2022, including lockdown periods, the model demonstrates superior predictive accuracy, evidenced by an RMSE of 55,914 kWh, a MAPE of 4.22 %, and an R-squared value of 0.899. Key determinants of energy consumption identified include air passengers and temperature, highlighting their substantial impact on energy use. The ARIMAX model's accuracy underscores its potential for enhancing energy management practices at airports. By integrating variables such as air passenger traffic and weather conditions, the model refines energy demand predictions, supporting more efficient energy use and operational planning. The ARIMAX model proves to be a valuable tool for forecasting energy consumption at KIA, aiding in energy management and sustainability efforts.

INQUEST ON THE CONCEPTION BY HOSPITALITY MANAGEMENT STUDENTS OF THE TERM SUSTAINABILITY IN A STATE UNIVERSITY IN DANAO, CEBU, PHILIPPINES

As people worldwide increasingly acknowledge the importance of sustainable practices to preserve resources and guarantee long-term environmental, economic, and social feasibility, it becomes imperative to understand how aspiring professionals in the hospitality industry perceive and interpret this concept. This study examines the understanding of the concept of sustainability among Hospitality Management students at a state university in Danao, Cebu. A descriptive-correlational approach was used to determine the respondents' demographic profile and degree of understanding regarding sustainability. Also, this study explores potential variations in student perceptions based on sex and academic strands. Forty-nine (49) first-year students were selected randomly to join the collection data and treated the data using frequency distribution, percentage, weighted mean, and ANOVA. The findings reveal that respondents perceived a high understanding of water and waste management. Also, there is a significant difference in how students from different genders and academic backgrounds conceptualize sustainability, particularly concerning energy and waste management practices. These results offer valuable insights into tailoring sustainability education within hospitality programs to address potential knowledge gaps and equip future professionals with a comprehensive understanding of sustainable practices in the industry.

The prospects of zero energy building as an alternative to the conventional building system in Bangladesh (A review)

Energy consumption in commercial and residential buildings worldwide accounts for about one-third of the world’s energy and one-quarter of greenhouse gas emissions. If current trends continue, by 2025, buildings worldwide will be the largest consumers of global energy, using as much power as the transportation and industrial sectors combined. Recent studies have found that improving energy efficiency in buildings is the least costly way to reduce a large quantity of carbon emissions. By changing energy management practices and instituting technologies that enhance energy efficiency, building owners and managers can reduce energy consumption by up to 35%. However, energy efficiency efforts in buildings alone cannot address future demand for more energy by this sector. To achieve breakthrough solutions to this problem, it is evident that a coordinated effort in a whole-building systems approach that emphasizes the necessity of integrating renewable on-site or distributed generation and energy efficiency is required to design the buildings of the future. Several International Energy Agency (IEA) countries have adopted a vision of so-called ‘net zero energy buildings’ (NetZEBs) as the long-term goal of their energy policies. This NetZEB is very new in Bangladesh and it started building green buildings which will lead to NetZEB shortly. However, Bangladesh has to comply with the IEA and must accept the zero-energy building concept.

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

Design and deployment of a novel decisive algorithm to enable real-time optimal load scheduling within an intelligent smart energy management system based on IoT

Consumers routinely use electrical devices, leading to a disparity between consumer demand and the supply side a significant concern for the energy sector. Implementing demand-side energy management can enhance energy efficiency and mitigate substantial supply-side shortages. Current energy management practices focus on reducing power consumption during peak hours, enabling a decrease in overall electricity costs without sacrificing usage. To tackle the mentioned challenges and maintain system equilibrium, it is essential to develop a flexible and portable system. Introducing an intelligent energy management system could pre-empt power outages by implementing controlled partial load shedding based on consumer preferences. During a demand response event, the system adapts by imposing a maximum demand limit, considering various scenarios and adjusting appliance priorities. Experimental work, incorporating user comfort levels, sensor data, and usage times, is conducted using Smart Energy Management Systems (SEMS) integrated with cost-optimization algorithms.

Effect of IoT Integration in Energy Management System and Grid Responsiveness on Energy Efficiency and Cost Reduction in Jakarta Government Buildings

This study investigates the effect of IoT integration in energy management systems (EMS) and network responsiveness on energy efficiency and cost reduction in DKI Jakarta government buildings. A quantitative analysis was conducted using structural equation modeling (SEM) with Partial Least Squares (PLS) 3. Survey questionnaires were administered to building managers, energy engineers, and facility maintenance personnel, while energy consumption data were obtained from relevant authorities. The results demonstrate a significant positive impact of both IoT integration and network responsiveness on energy management outcomes. Specifically, Energy Management System (EMS) and Grid Responsiveness were found to significantly influence both Cost Reduction and Energy Efficiency within government buildings. These findings have important implications for policymakers, building managers, and stakeholders involved in energy management decision-making. By leveraging advanced energy management technologies and practices, DKI Jakarta government buildings can achieve significant cost savings, improve energy efficiency, and advance environmental sustainability.

A Roadmap for the Implementation of Building Energy Efficiency Codes Compliance in Tripoli-Lebanon: Key Actions in the Familiarity Stage

Objective: The objective of this study is to provide policy recommendations for strengthening governance and institutional capabilities to ensure effective climate action and sustainable development in Tripoli-Lebanon and to propose a multi-staged implementation framework for T-BEEC, focusing on familiarity, with the aim o To facilitate Lebanon's transition towards sustainable energy sources by implementing an effective Building Energy Efficiency Code (T-BEEC) in Tripoli. Theoretical Framework: The study employs environmental science, urban planning, and socio-economic theories, focusing on policy adoption and the diffusion of technological innovations to enhance urban energy efficiency and sustainability. Method: A mixed-methods approach is adopted, incorporating both qualitative and quantitative research methods. Data collection involved semi-structured interviews with key personnel at the Tripoli Municipality Permit Office and thematic analysis of the barriers and facilitators to BEEC implementation. Comparative case studies from different geopolitical regions provide a broader perspective on the challenges and strategies of energy code compliance. Results and Discussion: Findings emphasize the critical need for infrastructure and resource optimization, including vehicle maintenance and replacements, spatial layout reorganization, and the prompt repair of computers and printers, to ensure operational efficiency. Additionally, the pursuit of diverse financial support sources highlights the reliance on municipal fees, international aid, NGO contributions, and educational partnerships to overcome financial and resource constraints Research Implications: This study impacts urban energy management theory and practice, providing key insights for policymakers and planners in similar settings. Originality/Value: This study outlines a unique approach to implementing energy codes during Tripoli's familiarity period, enhancing urban sustainability and policy effectiveness.

The potential impact of digital economy on energy poverty in the context of Chinese provinces

This study focused on exploring the impact of the digital economy (DE) on energy poverty (EP) across Chinese provinces from 2004 to 2018, motivated by the critical need to understand how technological advancements in the digital sector influence energy accessibility and sustainability. Conducted against the backdrop of global digital transformation, the research aimed to dissect the nuanced ways in which the DE contributes to mitigating EP, employing dynamic panel threshold and indirect effect models to capture both the direct and nuanced, and intermediate effects of digital progress on energy deprivation. Key findings revealed a significant reduction in EP attributed to the advancements in DE, with the most notable improvements observed in Eastern China where strategic energy policies and management practices enhanced the positive impacts of digitalization. The study highlighted the DE's role in improving energy access, efficiency, and environmental sustainability, although it also pointed out the potential for regressive effects in areas with lower levels of technological advancement. These findings are of substantial value as they offer empirical evidence of the DE's capacity to alleviate EP, underlining the importance of integrating digital strategies into energy policy planning. The research provides critical insights for policymakers, stakeholders in the energy sector, and scholars interested in the synergies between digital innovation and energy security, suggesting that leveraging digital technologies could accelerate efforts towards achieving sustainable energy access and combating energy poverty in China and potentially in other contexts facing similar challenges.

Renewable energy supply chain in Malaysia: Fostering energy management practices and ecological performance

The trade-off between environmental issues and economic outcomes has challenged manufacturing firms to manage the renewable energy supply chain (RESC). The Malaysian manufacturing industry has been challenged for its capacity to contribute to renewable energy development and reliance on imported components. The renewable energy supply chain has risks for uncertainty, higher costs, longer lead times, and disruption. Prior studies uncover the impact of fostering energy management practices (EMPs) and ecological performance (EC) in the renewable energy supply chain under a natural resource-based view (RBV). The government has provided the incentive to drive the successful implementation of energy management to obtain clean energy and business sustainability. This study contributes to developing a theoretical model that examines the intervening effect of the RESC on EMPs and EC. This study analysed the responses of 129 manufacturing firms to understand energy management practices. The findings show that energy auditing positively relates to RESC and that government incentives have impacted energy management practices. Management commitment and energy knowledge directly affected manufacturing firms’ ecological performance. The results show that manufacturing firms can best design energy management, green strategy, and competitiveness for business sustainability. Theoretical, policy and managerial implications are discussed.

Investigating Barriers to the Adoption of Energy Management Practices for Sustainable Construction Projects: SEM and ANN Approaches

This research addresses the critical challenges hindering the integration of Energy Management Practices (EMPs) within the construction industry, impeding its progress toward sustainability. Recognizing the pivotal role of EMPs in fostering sustainable practices, this study aims to fill a notable research gap by conducting a meticulous survey involving 100 industry professionals. Through the application of Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analyses, this study provides a comprehensive exploration of the intricate barriers and their interrelated dynamics within the construction sector. The findings reveal significant financial obstacles, including higher initial costs and limited financing options, underscoring the need for interventions to alleviate financial constraints. Additionally, policy and regulatory challenges, such as limited government incentives and shifting energy management rules, are identified, highlighting the necessity for stable and supportive regulatory environments to foster EMP adoptions. This research provides unique insights into the barriers hindering EMP adoption within the construction sector. The implications of this study extend beyond EMP adoption, offering a foundation for advancing sustainable practices in the construction industry. The insights gained can inform both academic research and practical decision-making, contributing to the ongoing discourse on sustainability in construction. Doi: 10.28991/CEJ-2024-010-04-015 Full Text: PDF

ANALYSIS OF DAILY ENERGY USAGE BY WORKERS IN A TOBACCO PRODUCTION FACILITY

This study analyzes the daily energy consumption patterns of workers in a tobacco production enterprise. Data was collected over a specified period to determine the average energy usage and identify any significant fluctuations or trends. The results provide valuable insights into energy management practices, highlighting areas for potential optimization and efficiency improvements within the enterprise. The findings aim to inform strategic decision-making and promote sustainable energy practices in the tobacco production sector.

Green building energy: Patents analysis and analytical hierarchy process evaluation

In the dynamic sphere of building energy systems, this study explores advancements in energy integration, storage technologies, management practices, and occupant behavior, assessing sustainable energy practices, including emerging technologies like fuel cells and energy storage systems. It underscores the significance of efficient energy management, considering both renewable and conventional energy mechanisms. The study comprises four key strata: (i) a thorough literature review of recent energy trends, (ii) a comparative study of global energy patents using the World Intellectual Property Organization (WIPO) database, (iii) a comprehensive analysis of building-energy patents, and (iv) expert-guided Analytic Hierarchy Process (AHP) evaluation. These realms encompass five primary sources: (i) energy-efficient building design, (ii) intelligent building automation, (iii) optimizing energy systems integration, (iv) energy storage, and (v) energy management and optimization. Findings reveal energy storage's dominance, with water energy storage and emerging hydrogen technology leading the trajectory. Global energy patent scrutiny underscores China, the United States, and Japan as influential players in optimizing energy markets. The research shapes energy futures, identifies gaps, and drives sustainable energy practices within the built environment, serving as a compass for policymakers and researchers.

Optimizing Energy Consumption in Agricultural Greenhouses: A Smart Energy Management Approach

Efficient energy management is crucial for optimizing greenhouse (GH) operations and promoting sustainability. This paper presents a novel multi-objective optimization approach tailored for GH energy management, aiming to minimize grid energy consumption while maximizing battery state of charge (SOC) within a specified time frame. The optimization problem integrates decision variables such as network power, battery power, and battery energy, subject to constraints based on battery capacity and initial energy, along with minimum and maximum energy from the battery storage system. Through the comparison of a smart energy management system (EMS) with traditional optimization algorithms, the study evaluates its efficiency. Key hyperparameters essential for the optimization problem, including plateau time, prediction time, and optimization time, are determined using the ellipse optimization method. Treating the GH as a microgrid, the analysis encompasses energy management indicators and loads. A simulation conducted via Simulink in MATLAB software (R2021b) demonstrates a significant enhancement, with the smart EMS achieving a more than 50% reduction in the objective function compared to conventional EMS. Moreover, the EMS exhibits robust performance across variations in the load power and irradiation profile. Under partial shading conditions, the EMS maintains adaptability, with a maximum objective function increase of 0.35553%. Aligning the output power of photovoltaic (PV) systems with real-world conditions further validates the EMS’s effectiveness in practical scenarios. The findings underscore the efficiency of the smart EMS in optimizing energy consumption within GH environments, offering promising avenues for sustainable energy management practices. This research contributes to advancing energy optimization strategies in agricultural settings, thereby fostering resource efficiency and environmental stewardship.