Intelligent Energy Management Research Articles

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.

Intelligent-of-things multiagent system for smart home energy monitoring

The proliferation of IoT devices has ushered in a new era of smart homes, where efficient energy management is a paramount concern. Multiagent artificial intelligence-of-things (MAIoT) has emerged as a promising approach to address the complex challenges of smart home energy management. This research study examines MAIoT's components, functioning, benefits, and drawbacks. MAIoT systems improve energy efficiency and user comfort by combining multiagent systems and IoT devices. However, privacy, security, interoperability, scalability, and user acceptability must be addressed. As technology advances, MAIoT in smart home energy management will offer more sophisticated and adaptable solutions to cut energy consumption and promote sustainability. This article describes how energy status and internal pricing signals affect group intelligent decision making and the interaction dynamics between consumers or decision makers. In a multiagent configuration based on the new concept of artificial intelligence-of-things, this intelligent home energy management challenge is simulated and illustrated using software and hardware. Based on sufficient experimental simulations, this paper suggested that residential clients can significantly improve their economic benefit and decision-making efficiency.

Smart Energy Meter with Advanced Billing and Power Theft

The remote energy monitoring system is a meter and IOT software combo that provides amenities for the consumer. Indonesia swiftly embraced smart metering over traditional metering. India still uses the conventional type, which has issues such as electromechanical meter errors, human errors, and processing flaws. Using an ATMEGA 328p and a node MCU Micro Controller, we constructed a power theft detection and invoicing system for this project. It contains tools to monitor power in real-time using Internet of Things technology and is capable of automatically generating power bills. Additionally, in addition to alerting the user when power changes such as undervoltage, overvoltage, or overload occur, our system also looks for instances of power theft. The Power Theft Detection and Power Billing System is a ground-breaking initiative that was created as a result of the energy management industry's unwavering quest of innovation. This project represents the future of intelligent energy management since it is based on the confluence of technology and pragmatism

The impact of radio on the construction of smart cities

In the process of smart cities, radio technology has played an increasingly important role. With the continuous acceleration of the global urbanization process, cities are facing many challenges. Driven by the governments vigorous support and business promotion, wireless communication technology has developed rapidly, and new wireless communication technology has continued to emerge, providing more choices and possibilities for smart city construction. This article reviews and analyzes the actual case analysis, studies the impact of radio technology on the construction of smart cities, while explaining the importance of radio security management. The results showed that the application of radio technology enabled smart cities to achieve functions such as intelligent traffic management, intelligent communication, and intelligent energy management, but there were also problems such as leakage privacy, signal interference, and imbalance in regional development. In the process of future smart city construction, it is necessary to further optimize the safety and popularity of radio, promote the use of radio, narrow the gap between the use of different regions and cities, and enhance the radio management, so that it can better serve the construction of smart cities.

Enhancing grid stability and efficiency in buildings through forecasting and intelligent energy management of distributed energy resources

Enhancing grid stability and efficiency in buildings through forecasting and intelligent energy management of distributed energy resources

Anfis Based Energy Management System for Microgrids

Microgrids are typical low or medium voltage distribution networks that operate on an adaptive system. Which, employing intelligent energy management techniques, govern the power exchange between the main grid, locally distributed generators (DGs), and associated loads. This paper describes and evaluates the intelligent ANFIS based energy management approach for grid integrated microgrid networks. The ANFIS based intelligent controller estimates the power that must be generated by (or) stored in batteries, taking into consideration of power demanded by loads and available battery power by accounting the battery state of charge. To evaluate the power allocation an artificial intelligent based technique i.e., Sugeno-based fuzzy EMS is implemented. The stated fuzzy logic control enhances power distribution within the micro-grid components while minimizing the requirement for power transfer to/from the main grid.

A Comprehensive Study on Net Zero Energy Buildings and Sustainable Construction Materials with Low Carbon Contents

One-third of the world's greenhouse gas emissions come from the building industry, with embodied carbon from the manufacture of building materials accounting for a sizable portion of these emissions. Prior research has mostly focused on examining the carbon emissions of low-rise structures, but it has failed to consider the significant influence that high-rise buildings have on a city's overall carbon emissions. Net-zero energy buildings (NZEBs) combine intelligent energy management strategies with conventional design knowledge to facilitate a sustainable and eco-friendly transition. The NZEB building revolution has the potential to become a long-term structural pillar for structures to come. The purpose of this study is to assess how various design factors and the embodied carbon in high-rise structures relate to one another. Additionally, the goal of this research is to keep annual consumer energy comfort in a home NZEB at its highest level without using any electricity from the electrical grid. In order to achieve long-term climate goals, our work emphasises the socio-economic advantages of adopting NZE buildings and further empowers consumer participation to minimise energy waste and carbon emissions. The findings provide a foundation for constructing high-rise structures in a more ecologically conscious manner to lower the structure's carbon emissions. The evaluation and comparison of the embedded carbon values takes into account the carbon emissions from the transportation and material manufacturing processes.

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

Intelligent Energy Management: Leveraging an Effective Machine Learning for Predictive Appliance Energy Optimization in Smart Homes

In the context of escalating energy demands and the proliferation of smart home technologies, this study introduces a novel approach to energy management using the Random Forest machine learning model. Our research focuses on optimizing household appliance energy use, harmonizing efficiency with user comfort. By analyzing data on appliance usage patterns, environmental conditions, and user preferences, the Random Forest model predicts future energy needs, enabling the intelligent scheduling of appliances to reduce unnecessary consumption. The model’s strength lies in its capacity to unravel complex, non-linear relationships in high-dimensional data typical of household energy usage scenarios. Initial results demonstrate a notable decrease in energy consumption, affirming the model’s efficacy in enhancing energy efficiency without diminishing user convenience. This research not only highlights the potential of machine learning in energy management but also sets a foundation for future exploration into adaptive, real-time energy optimization strategies in smart homes. In this research, we compared our model with other machine learning models, and our model got a good accuracy of 95.71%, while for time series data, we got 99.71%.

An improved energy management strategy for hybrid electric powered aircraft based on deep reinforcement learning

Electric airplanes have attracted a great deal of attentions because of their strategic commercial values and low emissions. One of the essentials for efficient control and management of hybrid electric propulsion aircraft is a well-designed energy management strategy (EMS). A new EMS approach based on deep learning is introduced in this study to realize thrust distribution and power distribution, consequently lowering aircraft fuel consumption. A model of hybrid electric propulsion power system including turbine engines and lithium-ion battery pack is constructed to act as an application environment. An intelligent energy management strategy based on deep reinforcement learning (DRL) is then proposed in this paper. Two DRL agents based on twin delayed deep deterministic policy gradient (TD3) are combined in the hierarchical framework. The multi-agents are trained to handle the energy management problem in this paper. In a finite period of time, the training process is accomplished. After training, the simulation result of the test shows that this DRL-based EMS reduces the equivalent fuel consumption by 2.2% compared with that using the existing method, and preserves 99.9% of the battery state of health (SOH). The battery pack's state of charge (SOC) regresses precisely to the reference value at the end. Particularly, the proposed EMS is able to work properly for different flight profiles and various initial SOCs. The results of this paper provide preliminary supports for the feasibility and superiority of deep reinforcement learning applied to the energy management strategy of series hybrid electric aircraft.

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.

Feasibility assessment of next-generation smart district heating networks by intelligent energy management strategies

Increasing flexibility of energy demand in a district heating network (DHN) results in both more possibility of interactions between the heating network and renewable resources and managing peak loads by shaving and shifting. This paper summarizes a series of tests performed in a large building complex that may drive future operational strategies for DHN. These novel strategies aim to impact selected customers with a specific range of heat consumption intensity to assess achievable flexibilities in heating demand without additional cost. The results show that a reduction in DHN supplied temperature by 6 °C does not significantly influence the heat consumption of customers equipped with radiators and thermostatic valves. However, its reduction by 10 °C, and 20 °C can impact users with higher consumed heat rates of approximately 0.2 and 0.1 [Unit/10min], respectively. Moreover, prolonging test periods until midday, when there is the highest probable outdoor temperature and solar irradiation may avoid sharp energy variations after tests.

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.

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.

Towards a fossil-free urban transport system: An intelligent cross-type transferable energy management framework based on deep transfer reinforcement learning

Deep reinforcement learning (DRL) is now a research focus for the energy management of fuel cell vehicles (FCVs) to improve hydrogen utilization efficiency. However, since DRL-based energy management strategies (EMSs) need to be retrained when the types of FCVs are changed, it is a laborious task to develop DRL-based EMSs for different FCVs. Given that, this article introduces transfer learning (TL) into DRL to design a novel deep transfer reinforcement learning (DTRL) method and then innovatively proposes an intelligent transferable energy management framework between two different urban FCVs based on the designed DTRL method to achieve the reuse of well-trained EMSs. To begin, an enhanced soft actor-critic (SAC) algorithm integrating prioritized experience replay (PER) is formulated to be the studied DRL algorithm in this article. Then, an enhanced-SAC based EMS of a light fuel cell hybrid electric vehicle (FCHEV) is pre-trained by using massive real-world driving data. After that, the learned knowledge stored in the FCHEV's well-trained EMS is captured and then transferred into the EMS of a heavy-duty fuel cell hybrid electric bus (FCHEB). Finally, the FCHEB's EMS is fine-tuned in a stochastic environment to ensure adaptability to real driving conditions. Simulation results indicate that, compared to the state-of-the-art baseline EMS, the proposed DTRL-based EMS accelerates the convergence speed by 91.55% and improves the fuel economy by 6.78%. This article contributes to shortening the development cycle of DRL-based EMSs and improving the utilization efficiency of hydrogen energy in the urban transport sector.

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.

An applied deep reinforcement learning approach to control active networked microgrids in smart cities with multi-level participation of battery energy storage system and electric vehicles

This study proposed an intelligent energy management strategy for islanded networked microgrids (NMGs) in smart cities considering the renewable energy sources uncertainties and power fluctuations. Energy management of active power and frequency control approach is based on the intelligent probabilistic wavelet fuzzy neural network-deep reinforcement learning algorithm (IPWFNN-DRLA). The control strategy is formulated with deep reinforcement learning approach based on the Markov decision process and solved by the soft actor-critic algorithm. The NMG local controller (NMGLC) provides information such as the frequency, active power, power generation data, and status of the electric vehicle's battery energy storage system to the NMG central controller (NMGCC). Then the NMGCC calculates active power and frequency support based on the IPWFNN-DRLA approach and sends the results to the NMGLC. The proposed model is developed in a continuous problem-solving space with two structures of offline training and decentralized distributed operation. For this purpose, each NMG has a control agent (NMGCA) based on the IPWFNN algorithm, and the NMGCA learning model is formulated based the online back-propagation learning algorithm. The proposed approach demonstrates a computation accuracy exceeding 98%, along with a 7.82% reduction in computational burden and a 61.1% reduction in computation time compared to alternative 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.

Enabling intelligent transferable energy management of series hybrid electric tracked vehicle across motion dimensions via soft actor-critic algorithm

Due to the complex driving conditions faced by hybrid electric tracked vehicles, energy management is crucial for improving fuel economy. However, developing an energy management strategy (EMS) is a time-consuming and labor-intensive task, which is challenging to generalize across different driving tasks. To solve this problem and shorten the development cycle of EMSs, this article proposes a novel transferable energy management framework for a series hybrid electric tracked vehicle (SHETV) across motion dimensions. To fully reuse the learned knowledge from longitudinal motion into both longitudinal and lateral motion, this framework merges transfer learning (TL) into the state-of-the-art deep reinforcement learning (DRL) algorithm, soft actor-critic (SAC), to formulate a novel deep transfer reinforcement learning (DTRL) method, with the transfer of both the neural networks and the pre-trained experience replay buffer. Simulation results indicate that the proposed EMS accelerates the convergence speed by 75.38%, enhances the learning ability by 19.05%, and improves the fuel economy by 5.08% compared to the baseline EMS. This article contributes to correlating different energy management tasks and reusing the existing EMS for the rapid development of a new EMS of the hybrid electric tracked vehicle.