Intelligent Energy Management System Research Articles

Innovative Strategies for Thermal Energy Optimization and Renewable Energy Integration in Net-Zero-Energy Buildings: A Comprehensive Review

The thermal performance and energy efficiency of buildings are critical factors in achieving sustainable energy systems as energy needs for heating and cooling are expected to represent more than 50% of global final energy consumption. This study analyzes conventional renewable energy systems for heating and cooling in buildings, focusing on strategies for developing net-zero-energy buildings. This review covers the integration of renewable energy, the use of intelligent energy management systems, and the optimization of thermal processes. It also compares various systems based on their advantages and limitations and analyzes emerging trends in the thermal management of buildings in different climate zones. The synthesis of recent literature highlights practical recommendations for achieving high thermal performance in buildings, including the importance of selecting appropriate energy systems based on local climatic conditions, optimizing system efficiency, and taking advantage of new materials and advanced technologies. This review aims to contribute to promoting sustainable construction practices with the integration of renewable energy sources and improving the energy efficiency of buildings.

Intelligent energy management system of hydrogen based microgrid empowered by AI optimization technique

Microgrids powered by hydrogen often face challenges in effectively managing energy over an extended duration due to the intermittent nature of renewable energy sources (RES) and fluctuating loads. This research emphasizes the use of AI technologies, including machine learning to improve the efficiency of intelligent energy management system (IEMS), hydrogen storage, fuel cell consumption, and power system quality improvement. A novel approach integrating Isolation Forest Ensemble (IFE) and Modified Water Wave Optimization (MWWO) has been proposed to enhance energy management and to improve the power quality. This method combines the population size with precision and accuracy. The effectiveness of this study has been assessed against established methods (AVO, PSO & IPSO, MFO, SAG & XGBoost, MILP, and QTLBO) concerning numerous factors including component sizing, economics, reliability, and efficiency of microgrid. The precision level of the proposed technique is approximately 83 %. The primary benefit of this research is the improvement of power quality and cost-efficiency in hydrogen based microgrids, attained through optimized and intelligent energy management system. This article furnishes evidence indicating the superiority of the anticipated approach. This study utilizes MATLAB/Simulink and PyCharm framework for its investigation and projection.

Modeling and control system optimization for electrified vehicles: A data-driven approach

Learning-based intelligent energy management systems for plug-in hybrid electric vehicles (PHEVs) are crucial for achieving efficient energy utilization. However, their application faces system reliability challenges in the real world, which prevents widespread acceptance by original equipment manufacturers (OEMs). This paper begins by establishing a PHEV model based on physical and data-driven models, focusing on the high-fidelity training environment. It then proposes a real-vehicle application-oriented control framework, combining horizon-extended reinforcement learning (RL)-based energy management with the equivalent consumption minimization strategy (ECMS) to enhance practical applicability, and improves the flawed method of equivalent factor evaluation based on instantaneous driving cycle and powertrain states found in existing research. Finally, comprehensive simulation and hardware-in-the-loop validation are carried out which demonstrates the advantages of the proposed control framework in fuel economy over adaptive-ECMS and rule-based strategies. Compared to conventional RL architectures that directly control powertrain components, the proposed control method not only achieves similar optimality but also significantly enhances the disturbance resistance of the energy management system, providing an effective control framework for RL-based energy management strategies aimed at real-vehicle applications by OEMs.

Intelligent energy management and operation efficiency of electric vehicles based on artificial intelligence algorithms and thermal energy optimization

As the global concern for environmental protection and sustainable development intensifies, electric vehicles (EVs), as a representative of clean transportation, are gradually receiving attention. This study aims to explore an intelligent energy management system for electric vehicles based on artificial intelligence algorithms, and focuses on optimizing heat energy utilization to improve the overall operating efficiency of electric vehicles. This paper uses machine learning and deep learning technologies to build intelligent decision-making models, and realizes dynamic optimization of energy demand and heat recovery by analyzing real-time data of electric vehicles under different working conditions. At the same time, a set of thermal management system is designed, which combines the driving state and environmental conditions of the vehicle to optimize the thermal management strategy of the battery and the motor. The experimental results show that the intelligent energy management system based on this research shows good adaptability and stability under different climate conditions, and significantly reduces energy consumption. Therefore, the intelligent energy management system of electric vehicles based on artificial intelligence algorithm and thermal energy optimization effectively improves the operating efficiency of electric vehicles.

An improved microgrid energy management system based on hybrid energy storage system using ANN NARMA-L2 controller

This research describes an intelligent Energy Management System (EMS) for a microgrid application that employs a Nonlinear Autoregressive Moving Average Level 2 (NARMA-L2) artificial neural network (ANN). The hybrid energy resources (PV/WIND), a hybrid energy storage system (HESS) with batteries and supercapacitors (SC), and loads are all integrated into the microgrid. Maximum Power Point Tracking (MPPT) in a photovoltaic (PV) system is based on the Nonlinear Autoregressive with exogenous inputs (NARX) approach. The flowchart, rules, memberships, and scenarios that allocate energy among various components based on battery State of Charge (SoC), PV/wind power, and load factors are described in the article. To validate the EMS's effectiveness, simulation tests under various conditions are presented. Supercapacitors (SCs) reduce DC bus voltage swings, lowering battery stress and extending battery life, it has been demonstrated using various load variations, as well as various sun irradiation and wind speed values under various conditions. The simulation results in the MATLAB/Simulink environment, validate the effectiveness of the proposed approach and produce effective DC BUS stabilizing and maintaining frequency stability of system that shows the importance and efficiency of the control strategy used.

Hybrid PVP/Battery/Fuel Cell Wireless Charging Stations Using High-Frequency Optimized Inverter Technology for Electric Vehicles

The design and integration of intelligent energy management systems in hybrid electric vehicle (EV) charging stations, leveraging industry 4.0 and renewable energy sources, is crucial for advancing sustainability, efficiency, and technological development. The innovative hybrid EV charging station described in this study uses a combination of fuel cells, batteries, and solar panels that run at 14 amps a piece at 240 volts. The system consists of five essential components that work together to transfer power wirelessly: an EV battery bank, a boost converter, an HF inverter, transfer coils, and a power supply. Two crucial phases make up the optimization process. In phase 1, the boost converter’s maximum power point is tracked and optimized to generate the most power possible by varying the duty cycle between 10% and 90%. In phase 2, the HF uses a class ϕ2 inverter at 30 MHz to synchronize with the resonant frequency of wireless power transfer coils. Zero-voltage switching is used by a digital signal processor card to carry out control for effective operations. By utilizing hybrid sources to optimize power transmission, this design improves the sustainability of EV charging options.

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.

The implementation of an optimized neural network in a hybrid system for energy management

In the face of increasing global energy demand and volatile energy prices, many countries are searching for solutions to ensure their energy independence. One of the most popular solutions is to incorporate renewable energy sources in their energy systems. While there are many advantages to integrating renewable energy sources, it is important to note that their intermittent operation can present challenges. Energy storage and smart grid management systems are key solutions to overcome these challenges and ensure sustainable, reliable use of renewable energy sources. In this article, we present an intelligent electrical energy management system for hybrid energy systems. This management system is based on a multi-layer neural network that has undergone an architecture optimization phase to improve the accuracy of real-time energy management and simplify its implementation. The management model that was built demonstrated highly good performance across a range of test circumstances.

Design & Simulation of an Intelligent Energy Management System for Electric Vehicle Charging Stations

The transition towards electric vehicles (EVs) necessitates the development of efficient and reliable charging infrastructure. This paper presents the design and simulation of an EV charging station using MATLAB/Simulink. The proposed charging station incorporates both wired and wireless charging capabilities, aiming to provide a comprehensive solution that meets the diverse needs of EV users. The design process involves creating detailed models of the charging station components, including the power electronics for AC/DC conversion, wireless power transfer modules, and control systems for charge management. The MATLAB/Simulink environment offers robust tools for modeling and simulating these components, allowing for thorough analysis and optimization. Simulation results demonstrate the effectiveness of the charging station design in terms of efficiency, power management, and user convenience. The wired charging system is modeled to ensure compliance with standard charging protocols, optimizing the power flow to achieve minimal charging times while maintaining safety and reliability. The wireless charging system, based on inductive power transfer principles, is evaluated for its alignment sensitivity, power transfer efficiency, and interoperability with different vehicle models. Key performance metrics such as charging efficiency, power quality, and thermal performance are analyzed under various operating conditions. The simulation results indicate that the proposed design can achieve high efficiency and reliable operation, with wireless charging efficiencies exceeding 90% under optimal alignment conditions. Keywords: METLAB, Simulink, AC-DC, Simulation, Wireless Charging etc.

Design of an Intelligent Energy Management System for Electric Vehicle Charging Station

In today’s world there are more outcomes in environmental change due to the overutilization of petroleum products in this manner prompting a genuine effect on the climate. So there is a need for a substitute answer for lessen the consumption of such non – sustainable assets. One such exertion made in the field of Freeways is the advancement of "Solar Freeways" which can be an elective arrangement. Sun oriented streets consolidate various arrangements in one – it can assist us with improving the creation of power utilizing sun based boards, to give a computerized stage to our future country's ventures like Smart Cities, and to work with the arising electric vehicles that supplant the petroleum driven vehicles and substantially more. Motivated by the fact that there are numerous amount of clean and sustainable energy we receive from roadways, the following study puts forward some of the event and application of an innovative charging method for the renewable energy driven electric cars, buses by using the roadway and also implementation of revolutionary nanotechnology along with the latest best in the house power electronics and power system analysis tools. A small scale prototype model was made by our team to attest the working of smart inductive charging process. Our project team was successful to improve the working of the model by improving the use of the preinstalled solar panels and also implement its use on the very concept we are trying to improve. On the vehicle, there will be the use of coils which are experimentally made for the flow of charges that are needed to provide charge to a moving electric vehicle (EV). The detailed strategy is presented in this report. Key Words: Environmental change, Overutilization of petroleum products, Solar Freeways, Renewable energy, Electric vehicles (EVs), Smart Cities, Nanotechnology

Optimizing urban energy management: A strategic examination of smart grids and policy regulations

This research addresses the critical challenges in urban energy management, focusing on load forecasting and optimizing renewable energy sources considering policy regulation. A novel Hybrid Long Short-Term Memory-Artificial Neural Network (LSTM-ANN) learning technique is proposed for accurate load and renewable energy output power forecasting. The hybrid model leverages the strengths of LSTM and ANN, providing a robust solution for capturing temporal dependencies and complex patterns in the data. Additionally, the Bee Algorithm for Optimization (BAO) is employed to handle the nonlinearity and nonconvexity inherent in energy management problems. BAO optimally schedules the operation of various generation units, considering the diverse characteristics of gas turbines, photovoltaic panels, and wind turbines. The effectiveness of the proposed hybrid model and optimization algorithm is demonstrated through comprehensive comparisons, showcasing superior accuracy and convergence speed compared to traditional methods. This research contributes to the advancement of intelligent energy management systems, offering a reliable framework for optimizing renewable energy utilization in real-world applications.

Self‐powered HP‐LED intelligent energy management system with adaptive brightness adjustment

AbstractThe energy harvesting system achieved from one environmental energy channel is facing more and more challenges in meeting the power supply of electronic energy‐consuming devices. In this study, a combined photovoltaic (PV) and Thermoelectric Generator (TEG) energy management system is proposed with intelligent self‐powered technique for high‐power light‐emitting diodes (HP‐LED) lighting. Both the solar energy and waste heat induced by PV and HP‐LED are synchronously converted into electrical energy. Self‐powered by this gathered power, an adaptive light intensity control of HP‐LED is also realized using the established feedback path between ambient light and HP‐LED light. Experimental results show that the supply voltage of the TEG required for startup of the whole system can be decreased as 20.8 mV. Only 10 s is needed to complete the startup process under sufficient light, while 25 s in case of insufficient conditions. This energy management can provide a new technology for future smart home and greenhouse planting.

WAYS TO REDUCE THE IMPACT OF PULSATIONS IN LOCAL POWER SUPPLY ROBOTIC SYSTEMS

An analysis of the changes in the flow of pulsation transforming the energy-dynamic indicators of on-board power electronics systems has shown that there are several options for solving this problem.
 The article outlines the main options for the current problem of pulsation in the energy systems of autonomous electrical systems, such as UAVs, robotic complexes, and other systems. The effectiveness of changes in pulsation is examined, in addition to directly assessing the effectiveness of stagnation strategies and methods. Also in the statistics, the optimal technical solutions for specific operating minds were identified.
 The prospects and future directives have been predicted based on the proposals for further research in the sphere of changes in pulsation in local life systems.
 In the future, it is predicted and supported by the development of technology and innovation in this country.
 To sum up, we can say that the statistics have analyzed the changes in pulsation in robotic local power supply systems, which is one of the key directions in the field of electrical engineering and electronics. Increased attention to this problem is due to the need for assured stability, reliability and greater energy efficiency of electronic systems. Research in this area is aimed at developing more comprehensive methods and strategies for reducing noise and pulsation in electrical systems.
 Assessing the effectiveness of stagnant methods, promoting the development of new technologies and innovation, as well as developing promising directions in this field opens the way to creating more stable and adaptive living systems. Replacement of advanced electronic filters, voltage stabilizers, selection of new materials and components, integration of intelligent energy management systems and optimization of essential energy resources local power supply systems – the whole purpose is to directly conduct further investigations.
 The growing demand for stable and efficient electronic systems is driven by current research and the introduction of new technologies to change pulsation. Technological developments and scientific research in this area open up broad prospects for reducing the power supply and ensuring the stability of the functioning of modern electronic devices.

An Intelligent Web-based Energy Management System for Distributed Energy Resources Integration and Optimization

The integration of renewable energy sources into power distribution systems frequently presents challenges for conventional energy management systems (EMS) due to the unpredictable and unstable characteristics of such energy sources. As a result, novel and cutting-edge solutions are required. This paper presents an intelligent web-based energy management system (iW-EMS) specifically designed to address the integration and optimization of distributed energy resources, as outlined in the proposed approach. The system incorporates a hybrid novel optimization approach that integrates simulated annealing and cone programming to effectively manage the distribution of energy resources and attain optimal outcomes from the proposed EMS. Additionally, it leverages generative AI services to create optimal scenarios based on historical data and real-time information, ensuring adaptability to the dynamic nature of renewable energy generation, providing a user-friendly and flexible web environment for scenario planning. The proposed framework facilitates seamless communication and collaboration among stakeholders involved in renewable energy integration, while also enabling the incorporation of real-world data sources such as weather forecasts and energy consumption patterns into the planning process.

A novel intelligent modeling and prediction of heat energy consumption in smart buildings

The rising cost and demand for energy have prompted several organizations to devise innovative methods for monitoring, managing, and conserving energy. Heat energy load prediction in smart buildings is critical for saving energy as well as for meeting energy demands. As the cost of energy and demand is increasing, people must find some way to control it and save energy. To overcome these issues, an intelligent energy management system needs to be developed to help individuals by predicting heat energy demand ahead of time and analyzing energy use patterns. This paper introduces a data-driven short-term heat energy consumption prediction for smart buildings by developing a hybrid deep learning model considering the capabilities of Convolutional and Recurrent Neural Networks. The proposed model extracts hidden patterns from actual data collected through sensors and also uses hand-crafted features to predict the heat energy demand accurately. The efficiency of the proposed model has been evaluated on real smart heat metering data, a case study in Denmark, having three kinds of buildings: single-family, apartment, and terraced houses. The extensive experimental results have been reported with real-world data by considering various evaluation metrics such as MAE, RMSE, MAPE and R2. The results are promising and outperformed other state-of-the-art methods.

Control Strategy for Building Air Conditioning Cluster Loads Participating in Demand Response Based on Cyber-Physical System

In recent years, with rising urbanization and ongoing adjustments in industrial structures, there has been a growing dependence on public buildings. The load of public buildings gradually becomes the main component of the peak load in summer, among which the load of air conditioning is particularly prominent. To clarify the key problems and solutions to these challenges, this study proposes a multi-objective optimization control strategy for building air conditioning cluster participation in demand response based on Cyber-Physical System (CPS) architecture. In a three-layer typical CPS architecture, the unit level of the CPS achieves dynamic information perception of air conditioning clusters through smart energy terminals. An air conditioning load model based on the multiple parameter types of air conditioning compressors is presented. Then, the system level of the CPS fuses multiple pieces of information through smart energy gateways, analyzing the potential for air conditioning clusters when they participate in demand response. The system of system level (SoS level) of the CPS deploys a multi-objective optimization control strategy which includes the uncertainty of the initial states of air conditioning clusters within the intelligent building energy management system. The optimal model takes into account the differences in the environmental conditions of each individual air conditioning unit within the cluster and sets different operating modes for each unit to achieve load reduction while maintaining temperatures within a comfortable range for the human body. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm based on Pareto frontiers is applied to solve this optimization control strategy and to optimize the operational parameters of the air conditioning clusters. A comparative analysis is conducted with single-objective optimization results obtained using the traditional Particle Swarm Optimization (PSO) algorithm. The case study results indicate that the proposed multi-objective optimization control strategy can effectively improve the thermal comfort of the human body towards the controlled temperatures of air conditioning clusters while meeting the accuracy of demand response. In the solution phase, the highest temperature within the air conditioning clusters is 24 °C and the lowest temperature is 23 °C. Adopting the proposed multi-objective optimization control strategy, the highest temperature is 26 °C and the lowest temperature is 23.5 °C within the clusters and the accuracy of demand response is up to 92%. Compared to the traditional PSO algorithm, the MOPSO algorithm has advantages in convergence and optimization precision for solving the proposed multi-objective optimization control strategy.

DEVELOPMENT AND IMPLEMENTATION OF INTELLIGENT ENERGY MANAGEMENT SYSTEMS AND EDUCATION PROGRAMS TO OPTIMIZE RESOURCE CONSUMPTION ON UNIVERSITY CAMPUSES

The article presents a systematic step-by-step approach to optimizing resource consumption on college campuses through the implementation of intelligent energy management systems (IEMSs) and energy efficiency education programs (3EP). The implementation methodology is discussed, highlighting the stages of evaluation, selection of IEMS, as well as their benefits, functionalities and challenges. We have also highlighted the types, benefits and made a number of recommendations aimed at providing energy resilience to university campuses. The major benefits of this approach are highlighted, including cost reduction, improved energy efficiency, increased awareness and responsibility of students and staff and building a modern and high-performing learning environment.

Mottainai in civil engineering – A message from Japan

This article examines the impact of Japan’s mottainai concept on civil engineering practices, focusing on the waste of materials, financial resources, and harmonisation of structures with the environment. The authors highlight the global challenges of environmental and social imbalances in the context of global warming. The Japanese concept of mottainai is presented as the key to shaping the civil engineering approach. The inefficient use of raw materials was analysed in terms of material waste. A transition to a closed-loop economy is recommended, emphasising the need for resource efficiency. In the context of financial resources, the need for efficient budget allocation and project management is emphasised. The section on harmonising structures with the environment focuses on aesthetics, environment, sustainable mobility, and an integrated approach to urban planning. Civil engineering projects should combine functional efficiency with respect to the environment, and the mottainai concept can be a tool for achieving sustainability in civil engineering. Innovative solutions, such as intelligent energy management systems, are recommended to improve construction efficiency.

Contextual Rule-Based System for Brightness Energy Management in Buildings

The increase in renewable generation of a distributed nature has brought significant new challenges to power and energy system management and operation. Self-consumption in buildings is widespread, and with it rises the need for novel, adaptive and intelligent building energy management systems. Although there is already extensive research and development work regarding building energy management solutions, the capabilities for adaptation and contextualization of decisions are still limited. Consequently, this paper proposes a novel contextual rule-based system for energy management in buildings, which incorporates a contextual dimension that enables the adaptability of the system according to diverse contextual situations and the presence of multiple users with different preferences. Results of a case study based on real data show that the contextualization of the energy management process can maintain energy costs as low as possible, while respecting user preferences and guaranteeing their comfort.

Intelligent energy management system of a smart microgrid using multiagent systems

Intelligent energy management system of a smart microgrid using multiagent systems