Energy Efficiency Of System Research Articles

Enhancing HVAC multivariable system performance through hybrid modeling and direct Nyquist Array control

This study explores a closed-loop multivariable control system for HVAC systems. A hybrid distributed parameter-lumped model was developed using MATLAB’s System Identification Application, resulting in a transfer function matrix. The Direct Nyquist Array (DNA) control strategy was applied, dividing the HVAC system into two SISO loops, each equipped with PID controllers. Stability analysis confirmed system stability according to the Nyquist criterion. Simulation of the system under DNA control showed improved performance over the open-loop setup, with faster response times of nearly 1 s and overshoots between 7% and 15%, maintaining thermal comfort and occupant satisfaction. The system effectively managed ambient heat transfer variations, maintaining desired airflow rate and air temperature. The closed-loop setup exhibited robustness against disturbances, quickly returning to steady-state values. Settling times ranged from 1 to 2 s with minimal impact on HVAC performance. Overall, the closed-loop multivariable DNA control with SISO PID controllers proved reliable for enhancing energy efficiency and thermal comfort in HVAC systems. Practical application This study provides a comprehensive framework for multivariable control in HVAC systems, significantly benefiting built environment professionals by enhancing energy efficiency and occupant comfort. By applying advanced control strategies, the research offers practical solutions for optimizing HVAC performance, reducing operational costs, and ensuring sustainability in building management. The detailed analysis and methodologies presented can be directly implemented to improve system reliability and responsiveness, thereby supporting professionals in creating smarter, more resilient, and environmentally friendly built environments.

Enhancing thermal energy and exergy harvesting in concentrated solar power systems using graphene-ZrO2/water hybrid nanofluid: An experimental study

Recent research has witnessed a significant increase in studies investigating the impact of nanofluids on enhancing the efficiency of sustainable energy systems. According to the details that were provided, the present study aims to enhance the heat and exergy efficiency of parabolic trough solar collectors (PTSCs) by using a water-based hybrid nanofluid with dispersed Graphene-ZrO2 at various volume concentrations (water, 0.05 %, 0.075 %, 0.1 %, and 0.125 %) as the working fluid. This research employs the hybrid nanofluid to improve heat transfer characteristics within the C.S.P. system, enhancing thermal energy extraction and exergy utilization. The proposed nanofluid underwent Fourier transform infrared spectroscopy (FTIR) analysis to determine its transmission spectrum across various frequencies. X-ray diffraction analysis was also conducted to elucidate the structural characteristics of two identical water and hybrid working fluids. The maximum thermal efficiency of 68.7 % was achieved for the PTSC system by adding 0.125 % of ZrO2 nanoparticle concentration with a mass flow rate of 3 L/min. Exergy efficiency was found for maximum volume concentration compared to distilled water fluid. Experimental results indicate significant improvements in thermal energy (68.72 %) and exergy harvesting efficiency (16.7 %), demonstrating the potential of graphene-ZrO2/Water hybrid nanofluids as a viable solution for enhancing the performance of C.S.P. systems.

A heat-measurement-free strategy for Economic Model Predictive Control of hydronic radiators

Cost-efficient measurement of room-level heat output from hydronic radiators is a major barrier to large-scale implementation of Economic Model Predictive Control (EMPC) in residential space heating for demand-side management. This paper therefore presents a novel EMPC strategy for hydronic radiators that relies on measurements of radiator pipe temperatures as a proxy for the radiator heat output, thus eliminating the need for costly flow-based meters at each radiator in a building. Simulation-based experiments indicate that the proposed proxy-based EMPC matches the performance of its heat-based counterpart. The proxy-based EMPC achieved a 16.6 % cost reduction compared to the heat-based EMPC's 16.8 %, with no comfort violations in both cases. Furthermore, the strategy shows resilience towards uncertainties in the user-estimated radiator exponent and maximum heating capacity. The proposed EMPC scheme also allows system operators to fine-tune the balance between cost savings and return temperatures using the proxy's upper limit. The findings presented in this paper suggest that the proposed proxy-based EMPC scheme provides a practical pathway for broader applications of EMPC in hydronic-based space heating with the prospect of unlocking significant load shifting potential, cost savings for end-users, and enhanced efficiency in individual and collective energy systems.

Comprehensive Task Optimization Architecture for Urban UAV-Based Intelligent Transportation System

This paper tackles the problem of resource sharing and dynamic task assignment in a task scheduling architecture designed to enable a persistent, safe, and energy-efficient Intelligent Transportation System (ITS) based on multi-rotor Unmanned Aerial Vehicles (UAVs). The addressed task allocation problem consists of heterogenous pick-up and delivery tasks with time deadline constraints to be allocated to a heterogenous fleet of UAVs in an urban operational area. The proposed architecture is distributed among the UAVs and inspired by market-based allocation algorithms. By exploiting a multi-auctioneer behavior for allocating both delivery tasks and re-charge tasks, the fleet of UAVs is able to (i) self-balance the utilization of each drone, (ii) assign dynamic tasks with high priority within each round of the allocation process, (iii) minimize the estimated energy consumption related to the completion of the task set, and (iv) minimize the impact of re-charge tasks on the delivery process. A risk-aware path planner sampling a 2D risk map of the operational area is included in the allocation architecture to demonstrate the feasibility of deployment in urban environments. Thanks to the message exchange redundancy, the proposed multi-auctioneer architecture features improved robustness with respect to lossy communication scenarios. Simulation results based on Monte Carlo campaigns corroborate the validity of the approach.

Impact of conservation agriculture and weed management on carbon footprint, energy efficiency, and sustainability in maize-wheat cropping systems

This study assesses the impact of conservation agriculture (CA) and weed management practices on the productivity, profitability, energy use, and carbon sustainability of a maize-wheat cropping system. Fifteen treatment combinations, incorporating five tillage methods (conventional-till maize and wheat [CT-CT], conventional-till maize and zero-till wheat [CT-ZT], zero-till maize and zero-till wheat [ZT-ZT], zero-till maize and zero-till plus residue in wheat [ZT-ZTR], and zero-till plus residue maize and wheat [ZTR-ZTR]) and three weed management practices (recommended herbicide [H-H], integrated weed management [IWM-IWM], and hand weeding [HW-HW]), were tested in a strip plot design. The results showed that ZTR-ZTR significantly increased system productivity (6.90 Mg ha−1), net returns, and benefit-cost ratio (BCR; 2.69), compared to CT. However, CT systems had 173 % higher energy use efficiency (EUE) and 170 % higher energy intensity (EI) than CA systems. CA-based treatments used about 15 % direct renewable energy and 85 % indirect renewable energy, while CT systems used 15–20 % direct non-renewable energy and 85–86 % indirect non-renewable energy. Among weed management practices, recommended herbicides (H-H) led to the highest productivity (6.71 Mg ha−1), EUE (9.80), net returns (3003 US$ ha−1), and BCR (3.08), but also higher carbon footprints. This underscores the balance between productivity, energy efficiency, and carbon in agriculture.

Numerical Analysis of Leading-Edge Roughness Effects on the Aerodynamic Performance of a Thick Wind Turbine Airfoil

The aerodynamic performance of wind turbine airfoils is crucial for the efficiency and reliability of wind energy systems, with leading-edge roughness significantly impacting blade performance. This study conducts numerical simulations on the DU 00-W-401 airfoil to investigate the effects of leading-edge roughness. Results reveal that the rough airfoil exhibits a distinctive “N”-shaped lift coefficient curve. The formation mechanism of this nonlinear lift curve is primarily attributed to the development of the trailing-edge separation vortex and variations in the adverse pressure gradient from the maximum thickness position to the trailing-edge confluence. The impact of different roughness heights is further investigated. It is discovered that when the roughness height is higher than 0.3 mm, the boundary layer can be considered fully turbulent, and the lift curve shows the “N” shape stably. When the roughness height is between 0.07 mm and 0.1 mm, a transitional state can be observed, with several saltation points in the lift curve. The main characteristics of different flow regimes based on different lift curve segments are summarized. This research enhances the understanding of the effects of leading-edge roughness on the aerodynamic performance of a thick wind turbine airfoil, and the simulation method for considering the effect of leading-edge roughness is practical to be applied on large-scale wind turbine blade to estimate the aerodynamic performance under rough leading-edge conditions, thereby supporting advancements in wind turbine technology and promoting the broader adoption of renewable energy.

A preference adjustable capacity configuration optimization method for hydrogen-containing integrated energy system considering dynamic energy efficiency improvement and load fast tracking

The new hydrogen-containing integrated energy system proposed in this paper has complementary coupling of multiple energy forms such as electricity, gas, cold, heat, and hydrogen. For the same type of load demand, equipment with different energy input forms can be dispatched for joint supply. However, different equipment differ in dynamic response speed and energy utilization efficiency, and the reasonable configuration of equipment capacity will directly affect the overall dynamic characteristics and energy efficiency level of the system. Therefore, this paper first proposes an evaluation index that can comprehensively characterize the dynamic energy efficiency and load tracking ability of energy equipment. In addition, a preference adjustable capacity configuration optimization method based on utopian point tracking is proposed with the two optimization objectives of this indicator and the system's equivalent annualized investment cost, and the solution complexity is reduced through segmented linearization of the objective. Simulations presented here show that the capacity configuration optimization method proposed in this paper has a positive effect on improving the dynamic energy efficiency and load tracking ability of the new hydrogen-containing integrated energy system, and can also meet the preference setting needs of different investment entities for capacity configuration optimization goals.

A novel geothermal system combined with fuel cell and hydrogen generation to store clean sustainable energy storage

This paper presents a thermo-economic assessment of a novel geothermal integrated system enhanced by a fuel cell. The proposed system leverages the advantages of geothermal energy resources, featuring a sustainable and environmentally friendly solution for power generation and district heating applications. Integrating a fuel cell and thermoelectric within the geothermal system aims to improve overall system performance and energy efficiency by converting waste heat into electricity. A detailed mathematical model of the integrated system is developed, incorporating various performance parameters, component efficiencies, and thermo-economic considerations. The model is applied to simulate the system's performance under different operating conditions and parameters. Additionally, sensitivity analyses are conducted to evaluate the effects of key design variables on the system's performance, cost, and environmental impacts. The results indicate that the newly developed system generates 95594 kW of electricity with a total exergy destruction rate of 4616 kW. The electricity cost rate also obtained 0.309 $/kWh. The energy efficiency of the introduced system is 24.08 % and the exergy efficiency is 26.7 %. Comparison with the basic system represents that energy efficiency shows a 15.71 % improvement.

Hybrid feedback control of PMSG-based WECS with multilevel flying-capacitor inverter enhanced by fractional order extremum seeking

This paper proposes an original hybrid control strategy for a three-phase multilevel flying capacitor inverter (FCI) used in wind energy conversion system (WECS). A state feedback law, computed using a Lyapunov function and an invariance principle, is designed to manage grid current control and capacitor voltage balancing, guaranteeing the asymptotic stability of the closed-loop system. Additionally, a fractional-order extremum seeking control (FOESC) algorithm for Maximum Power Point Tracking (MPPT) is introduced, which optimizes power capture by adjusting turbine speed from the DC side without requiring a detailed system model (model-free approach). The use of fractional-order calculus leads to improved convergence and more seamless performance while preserving the robust characteristics of the system. The proposed control strategies are validated through simulation, demonstrating improved transient response and stability under varying wind conditions and parameter uncertainties. These results highlight the potential of advanced control algorithms to enhance the efficiency and reliability of wind energy systems, contributing to global efforts to achieve sustainable energy goals.

Joint Optimization-Based QoS and PAPR Reduction Technique for Energy-Efficient Massive MIMO System

Massive multiple input multiple output (MIMO) is an innovative wireless communication technology that significantly enhances the capacity and efficiency of data transmission in modern networks. Massive multiple input multiple output, despite benefits, faces difficulties in managing numerous antennas affecting signal quality, peak-to-average power ratio (PAPR), and energy efficiency due to its scale and complexity. However, overcoming these complexities is vital for unlocking massive multiple input multiple output’s potential in high-quality, energy-efficient wireless communication. A proposed joint optimization-based technique aims to address these issues in massive MIMO systems. In phase 1, joint optimization with quality of service maximizes capacity via power distribution, employing the hybrid spider wasp Fick’s law algorithm. In phase 2, zero-forcing with symbol-level linear precoding, especially the stacked convolutional sparse bidirectional long short-term memory autoencoder (SCS–BiLSTMAE) network, efficiently reduces peak-to-average power ratio, collaborating for reduced bit rate error and peak-to-average power ratio through adaptive mapping. Refining the model involves precise hyperparameter tuning using the enhanced influencer buddy optimization algorithm. The proposed framework not only demonstrates exceptional performance, but the metrics also underscore the model's effectiveness in addressing diverse challenges, establishing it as a robust solution for quality enhancement, peak-to-average power ratio reduction, and energy efficiency. Additionally, the proposed model attains 28.14% greater system capacity than existing methods.

Comprehensive performance evaluation of HVAC systems integrated with direct air capture of CO2 in various climate zones

Direct Air Capture (DAC) is a rapidly evolving technology that extracts CO2 directly from ambient air. This study presents a comprehensive performance evaluation of integrating DAC in HVAC systems, which can reduce indoor CO2 concentration and improve energy efficiency of HVAC systems. The DAC equipment is modeled in Modelica based on isotherm and thermodynamic equations, and pressure drop curves of the CO2 sorbent described in literature. The model is validated with data from the literature, and then integrated into a typical HVAC system available in Modelica Buildings library. The HVAC system is a Variable Air Volume (VAV) with reheater system for a one-floor office building with standard ASHRAE 2006 control sequences. Demand control ventilation strategies are designed to reduce the outdoor air flowrates when indoor CO2 concentrations are lower than the threshold, which is to maximize the benefits of integrating DAC. Four cases are proposed to assess the impacts of integrating DAC and DCV in HVAC systems in 8 different ASHRAE climate zones in the USA. The results show that by integrating DAC unit into the HVAC system, the average indoor CO2 concentration can be significantly reduced by over 45 % against the baseline without a DAC unit. By integrating DCV, 0.39–21.66 % of annual energy savings and 226–9539 kg carbon emissions reduction are observed across different climate zones. The highest energy savings are found to be achieved with cold climatic conditions while the lowest energy savings occur with favorable weather.

Sustainable hybrid energy system’s reliability optimization by solving RRAP-CM with integration of metaheuristic approaches

PurposeThis research introduces an innovation strategy aimed at bolstering the reliability of a renewable energy resource, which is hybrid energy systems, through the application of a metaheuristic algorithm. The growing need for sustainable energy solutions underscores the importance of integrating various energy sources effectively. Concentrating on the intermittent characteristics of renewable sources, this study seeks to create a highly reliable hybrid energy system by combining photovoltaic (PV) and wind power.Design/methodology/approachTo obtain efficient renewable energy resources, system designers aim to enhance the system’s reliability. Generally, for this purpose, the reliability redundancy allocation problem (RRAP) method is utilized. The authors have also introduced a new methodology, named Reliability Redundancy Allocation Problem with Component Mixing (RRAP-CM), for optimizing systems’ reliability. This method incorporates heterogeneous components to create a nonlinear mixed-integer mathematical model, classified as NP-hard problems. We employ specially crafted metaheuristic algorithms as optimization strategies to address these challenges and boost the overall system performance.FindingsThe study introduces six newly designed metaheuristic algorithms. Solve the optimization problem. When comparing results between the traditional RRAP method and the innovative RRAP-CM method, enhanced reliability is achieved through the blending of diverse components. The use of metaheuristic algorithms proves advantageous in identifying optimal configurations, ensuring resource efficiency and maximizing energy output in a hybrid energy system.Research limitations/implicationsThe study’s findings have significant social implications because they contribute to the renewable energy field. The proposed methodologies offer a flexible and reliable mechanism for enhancing the efficiency of hybrid energy systems. By addressing the intermittent nature of renewable sources, this research promotes the design of highly reliable sustainable energy solutions, potentially influencing global efforts towards a more environmentally friendly and reliable energy landscape.Practical implicationsThe research provides practical insights by delivering a comprehensive analysis of a hybrid energy system incorporating both PV and wind components. Also, the use of metaheuristic algorithms aids in identifying optimal configurations, promoting resource efficiency and maximizing reliability. These practical insights contribute to advancing sustainable energy solutions and designing efficient, reliable hybrid energy systems.Originality/valueThis work is original as it combines the RRAP-CM methodology with six new robust metaheuristics, involving the integration of diverse components to enhance system reliability. The formulation of a nonlinear mixed-integer mathematical model adds complexity, categorizing it as an NP-hard problem. We have developed six new metaheuristic algorithms. Designed specifically for optimization in hybrid energy systems, this further highlights the uniqueness of this approach to research.

Iterative Root-Finding Algorithm for Accurate Parameter Extraction of Solar Photovoltaic Cells

The performance of photovoltaic models depends significantly on the accuracy of their parameters, which are determined by the chosen method and objective function. Extracting these parameters accurately under different environmental conditions is essential to enhance reliability, accuracy, and minimize system costs. In this research, a novel technique is proposed for extracting the electrical parameters of the solar cell single diode model, including saturation current, serial resistance, parallel resistance, and ideality factor. To overcome the challenges posed by the chaotic behavior of the I-V curve equation, an improved Iterative Root-Finding algorithm is introduced. This algorithm acts as an optimization tool, increasing the likelihood of obtaining highly accurate solutions by minimizing the quadratic error between experimental and theoretical characteristics in a shorter time frame. The numerical and experimental results demonstrate the effectiveness of this approach in solar module modeling, showing squared errors approaching zero. This study opens new possibilities for improving the accuracy and reliability of photovoltaic models, leading to more efficient solar energy systems.

Optimized Tiny Machine Learning and Explainable AI for Trustable and Energy-Efficient Fog-Enabled Healthcare Decision Support System

The Internet of things (IoT)-based healthcare decision support system plays a crucial role in modern medicine, especially with the rise in chronic illnesses and an aging population necessitating continuous remote health monitoring. Current healthcare decision support systems struggle to deliver timely and accurate decisions with minimal latency due to limited real-time healthcare data and inefficient computational resources. There is a critical need for an optimized, energy-efficient machine learning model that reliably supports remote health monitoring within IoT and fog computing environments. Our study proposes an Optimized Tiny Machine Learning (TinyML) and Explainable AI (XAI) binary classification model for a trustable and energy-efficient healthcare decision support system, leveraging fog computing to optimize performance. The fog-based approach improves response times and enhances bandwidth usage, addressing critical needs such as reduced latency, higher bandwidth utilization, and decreased packet loss. To further improve efficiency, we incorporate the innovative mLZW data compression technique, significantly enhancing data communication efficiency and reducing response time to critical health alerts. However, limited real-time healthcare data records challenge machine learning classification performance. By implementing a TinyML algorithm, our system demonstrates superior performance to other machine learning models. The proposed optimized TinyML model achieves an impressive F1 score of 0.93 for health abnormalities detection, emphasizing its robustness and effectiveness. This paper highlights the potential of TinyML and XAI in delivering robust, trustworthy, and energy-aware healthcare solutions, making significant contributions toward effective remote health monitoring and decision support in fog-enabled IoT networks.Graphical abstract

Cyclic mechanical response of LPBF Hastelloy X over a wide temperature and strain range: Experiments and modelling

Additive manufacturing (AM) of high-temperature alloys through processes such as laser powder bed fusion (LPBF) has gained significant interest and is rapidly expanding due to its exceptional design freedom, which enables the fabrication of complex parts that contribute to the increased efficiency of aerospace and energy systems. The materials produced through this process exhibit unique microstructures and mechanical properties, which necessitate dedicated study and characterization. In this context, our research focuses on the experimental characterization of the isothermal cyclic viscoplastic mechanical response of Hastelloy X (HX) over the temperature range of 22 to 1000 °C and at various strain rates, addressing a current gap in the literature. Recognizing the need for material models that can accurately represent the cyclic mechanical response of LPBF HX across a broad temperature range, we developed a robust extension of the viscoplastic isotropic-kinematic hardening Chaboche model, intended for applications in the thermomechanical simulation of the LPBF process for the analysis of residual stress and distortion, as well as for assessing the mechanical integrity of LPBF components. The extension involves expressing the entire set of model parameters explicitly with analytical functions to account for their temperature dependence. Consequently, the model includes a relatively large number of parameters to represent the isotropic-kinematic hardening viscoplastic response of the alloy over a wide temperature range, and hence to overcome the endeavor of its systematic calibration, a dedicated calibration approach was introduced. The model ultimately demonstrated its capability to precisely represent the isothermal response of the alloy over the examined temperatures and strain rates. To evaluate the model’s predictiveness for non-isothermal conditions, out-of-phase thermomechanical cyclic experiments were also conducted as independent benchmark tests, where the model’s predictions were fairly consistent with the experimental results. As a part of this study, the derived material model has been integrated into the UMAT subroutine, complete with an analytical derivation of the consistent Jacobian matrix.

An Investigation on Handling IoT Big Data with RBSEE Architecture

Internet of Things (IoT) devices have resulted in an unprecedented surge in data generation, presenting both opportunities and challenges in data management and analytics. This paper investigates the feasibility and effectiveness of utilizing the RBSEE (Resource-Based Storage, Edge Computing, and Elasticity) architecture for handling IoT Big Data. Firstly, the paper elucidates the fundamental concepts of IoT and Big Data, highlighting the unique characteristics and challenges associated with managing large volumes of heterogeneous data streams generated by IoT devices. Subsequently, it introduces the RBSEE architecture, which integrates resource-based storage, edge computing, and elasticity to address the scalability, latency, and resource constraints inherent in IoT environments. The architecture's ability to distribute data storage and processing tasks across edge devices and cloud infrastructure while dynamically adapting to fluctuating workloads makes it particularly suitable for handling IoT Big Data. In any typical IoT ecosystem, real-time and instantaneous handling of request and dataset is pivotal and a fundamental property. This aims to propose energy efficient distributed system architecture for persuasive, privacy-preserved and real time handling of data generated from IoT devices.

Microprocessor Control of the Electric Drive of Variable Radiation Installation and Ensuring of Operation Reliability

Introduction. The introduction of energy-saving technologies has become particularly relevant in recent years, when energy prices have been constantly increasing. This is particularly important for greenhouse farms, where the main condition for growing plants indoors is the creation of artificial climatic conditions: temperature, humidity, and lighting.Problem Statement. Given the light and the dark stages of photosynthesis, it becomes possible to use a variable light field in the plant-growing photoculture, which is created by light installations with variable radiation. The development of an energy-saving electric drive and automated control systems for the parameters of artificial climatic conditions when growing plants indoors results in reducing energy consumption and increasing the reliability of the technological process in greenhouses of agro-industrial complex.Purpose. Implementation of energy-saving variable illumination irradiating systems with an automated control system for greenhouse farms.Materials and Methods. A lighting installation with rotation of the irradiator in the vertical and horizontal planes with a discrete electric drive and microprocessor control has been used.Results. The current state of variable irradiating systems with energy-saving radiation sources has been analyzed. The use of a discrete electric drive with an automated control system has been proposed. The installation with a DNAT-400 radiation source and a ZHO-01 reflector enables irradiating an area of up to 100 m2. The maximum exposure to photosynthetically active radiation is 30 W/m2, and the irradiation irregularity does not exceed 20%. At the same time, electricity savings reach about 10 thousand kWh per season. The yield and quality of products remain unchanged.Conclusions. The results of the research have confirmed the feasibility of practical implementation of variable irradiation system for energy efficiency of greenhouse operations.

Enabling Sustainable Energy Communities in ASEAN: A Systematic Approach to P2P Energy Transactions on Matakus Island Using IoT and Blockchain

Abstract This paper addresses the need for energy diversification and sustainable development in the ASEAN region by proposing a decentralized peer-to-peer (P2P) energy trading system. PowerChain, a peer-to-peer energy trading platform, is tailored as the designated medium for implementation. PowerChain harnesses the capabilities of blockchain technology and utilizes MQTT and HTTP protocols to ensure security, transparency, and user-IoT interaction. A case study was conducted on Matakus Island, a rural area in Indonesia with high solar power generation potential and a suitable demographic for the study. Currently reliant on diesel-based power generation, the island serves as an ideal location to evaluate the feasibility and benefits of transitioning to a RE-based P2P energy trading system. Based on simulations using 2 types of load and generation from peer providers, it was found that unused energy for type 1 was 815.9 kWh/year and for type 2 was 821.05 kWh/year. So the total unused energy from 10 peer providers on Matakus Island that can be transacted on the Powerchain platform is 1,636.95 kWh/year. This energy can meet the needs of Matakus Island for 93.7 days.In the platform simulation, three experiments were carried out, namely, single type transactions, multiple type transactions, and rejection of energy requests. From these three experiments, Powerchain was able to perform well, where the seller was successfully selected based on the closest distance, and if the closest selection could not be fulfilled, the seller would come from several nearby buyers. And when a selected peer is rejected, the existing algorithm can search for and select a replacement peer.The integrated approach presented in this study demonstrates the viability of using IoT and blockchain technologies to transform the energy market structure and accelerate the transition towards cleaner and more efficient energy systems in ASEAN.

A novel load allocation strategy based on the adaptive chiller model with data augmentation

Model-based load allocation strategy is an impactful solution to enhance energy efficiency of multiple-chiller system. Its performance is heavily dependent on the accuracy of chiller model. Data-driven model is a pretty-good solution. However, in real multiple-chiller system, the range of operation condition in historical data is commonly narrow, so it is challenging to develop an accurate data-driven model of chiller throughout full range of operation condition. In this paper, data augmentation algorithm is presented to generate the data outside of historical data, which is based on conditional generative adversarial network (CGAN) and elastic weight consolidation algorithm (EWC). Combined historical data and generated data, augmented training dataset is set up and updated by online operation data. Trained by online updated augmented training dataset periodically, adaptive chiller model is set up. Based on adaptive chiller model, a novel load allocation strategy presented for multiple-chiller system. The proposed strategy is verified by field test in multiple-chiller system. The results show that adaptive chiller model, with the aid of data augmentation algorithm, is more accurate. The proposed strategy can achieve 5.03 % energy saving compared with fixed set-point strategy, and the EER of proposed strategy is 6.27 % higher than that of fixed set-point strategy.

Performance of Spectral and Energy Efficiency in Cell-Free Massive MIMO-Aided URLLC System With Short Blocklength Communications

Performance of Spectral and Energy Efficiency in Cell-Free Massive MIMO-Aided URLLC System With Short Blocklength Communications