Cuckoo Search Research Articles

Soft Switching Technique in a Modified SEPIC Converter with MPPT using Cuckoo Search Algorithm

: MPPT refers to the process of continuously tracking and adjusting the operating point of a photovoltaic (PV) system to maximize the power output from the solar panels. The operating point at which a PV system produces the most power under a specific set of environmental circumstances is known as the maximum power point (MPP). The Maximum power point tracking (MPPT) technique is used to overcome the challenges of PV arrays with variable irradiance levels, which collects the greatest power from the PV array. Standalone PV systems, as well as gridconnected systems, can benefit from a DC-DC converter with MPPT. This study compares the standard perturbation and observation (P&O) technique with the soft-switching method used in the SEPIC converter utilizing the cuckoo search (CS) algorithm using MATLAB/Simulink. Reviewing the simulation results and assessing the SEPIC converter's performance. Background: SEPIC soft-switching converter, the soft-switching technique is applied to the SEPIC converter topology. It typically uses additional components, such as inductor and capacitors, to enable soft-switching operation. These additional components help to control the voltage and current waveforms across the main switching devices, reducing switching losses. Methods: The proposed method uses a soft switching technique to reduce switching losses and to improve efficiency. Soft switching is used in SEPIC (Single Ended Primary Inductor Converter) converters, a type of DC-DC converter, to enhance their performance. The projected solution further addresses the maximum power point tracking (MPPT) issue in PV systems using the Cuckoo Search (CS) method. Results: Soft switching technique is implemented in SEPIC converters to reduce these switching losses. In this system, Zero Voltage Switching (ZVS) involves turning on the switch when the voltage across it is zero. This allows the current to flow through the switch without creating any significant switching losses. CS algorithm can track the MPP quickly and accurately, it exhibits less prone to oscillations, easily monitor the MPP under a variety of weather circumstances. However, it exhibits negligible oscillation in a steady state, which results in significant power savings. Conclusion: The modified SEPIC converter with a soft-switching MPPT cuckoo algorithm is used with a solar-powered system. A prototype comprising a solar panel, a SEPIC converter, a driver, and a controller circuit was created. A soft-switching SEPIC converter with an MPPT cuckoo algorithm for the PV system is implemented, as are the converter operation, converter analysis, and theoretical analysis, and a controller circuit is analysed. The PV system, MPPT controller with tracking algorithm, and PMSM load were used in the system simulation. A 60-watt SEPIC converter prototype is built, and the outcomes are also tested experimentally.

Smart hybrid algorithm for global maximum power point tracking under partial shading conditions

ABSTRACT Maximum power point tracking (MPPT) is a complex task due to the nonlinear behavior of the photovoltaic (PV) array. Moreover, partial shading (PS) is a major problem that significantly decreases the overall system’s efficiency, whereas traditional algorithms are mostly local searches and thus cannot guarantee global optimality. Therefore, Swarm Intelligence (SI) algorithms are developed to circumvent this problem. Existing SI algorithms like Cuckoo Search (CS) and particle swarm optimization (PSO) have been used to address partial shading issues; nevertheless, there is a research gap in developing hybrid methods that combine these two algorithms to enhance MPPT performance under various environmental conditions. This paper fills this research gap by providing a novel hybrid algorithm called Smart Hybrid Algorithm (SHA). The developed optimizer avoids the common disadvantages of conventional MPPT techniques and provides a simple and robust MPPT tracker to handle PS in PV systems effectively. The proposed optimizer features a reset function to restart the search process for the new GMPP at any irradiance change. Multiple performance tests were conducted using 4S, 8S, 10S, 12S, and 4S2P PV array configurations to investigate the performance of our optimizer. The results obtained are compared to those of well-known recent algorithms. The proposed system demonstrates a convergence time of less than one second and a steady state power efficiency exceeding 99.79% across various complex partial shading scenarios. It also offers a convergence time reduction of at least 14% compared to other techniques. The findings demonstrate the robustness and suitability of SHA to handle complex partial shading conditions (CPSCs) and uniform irradiance with high dynamic and steady-state efficiencies and minor transient power fluctuations.

Performance Comparison of Bio-Inspired Algorithms for Optimizing an ANN-Based MPPT Forecast for PV Systems.

This study compares bio-inspired optimization algorithms for enhancing an ANN-based Maximum Power Point Tracking (MPPT) forecast system under partial shading conditions in photovoltaic systems. Four algorithms-grey wolf optimizer (GWO), particle swarm optimization (PSO), squirrel search algorithm (SSA), and cuckoo search (CS)-were evaluated, with the dataset augmented by perturbations to simulate shading. The standard ANN performed poorly, with 64 neurons in Layer 1 and 32 in Layer 2 (MSE of 159.9437, MAE of 8.0781). Among the optimized approaches, GWO, with 66 neurons in Layer 1 and 100 in Layer 2, achieved the best prediction accuracy (MSE of 11.9487, MAE of 2.4552) and was computationally efficient (execution time of 1198.99 s). PSO, using 98 neurons in Layer 1 and 100 in Layer 2, minimized MAE (2.1679) but had a slightly longer execution time (1417.80 s). SSA, with the same neuron count as GWO, also performed well (MSE 12.1500, MAE 2.7003) and was the fastest (987.45 s). CS, with 84 neurons in Layer 1 and 74 in Layer 2, was less reliable (MSE 33.7767, MAE 3.8547) and slower (1904.01 s). GWO proved to be the best overall, balancing accuracy and speed. Future real-world applications of this methodology include improving energy efficiency in solar farms under variable weather conditions and optimizing the performance of residential solar panels to reduce energy costs. Further optimization developments could address more complex and larger-scale datasets in real-time, such as integrating renewable energy sources into smart grid systems for better energy distribution.

An Improved Cuckoo Search Algorithm and Its Application in Robot Path Planning

This manuscript introduces an improved Cuckoo Search (CS) algorithm, known as BASCS, designed to address the inherent limitations of CS, including insufficient search space coverage, premature convergence, low search accuracy, and slow search speed. The proposed improvements encompass four main areas: the integration of tent chaotic mapping and random migration in population initialization to reduce the impact of random errors, the guidance of Levy flight by the directional determination strategy of the Beetle Antennae Search (BAS) algorithm during the global search phase to improve search accuracy and convergence speed, the adoption of the Sine Cosine Algorithm for local exploitation in later iterations to enhance local optimization and accuracy, and the adaptive adjustment of the step-size factor and elimination probability throughout the iterative process to convergence. The performance of BASCS is validated through ablation experiments on 10 benchmark functions, comparative experiments with the original CS and its four variants, and application to a robot path planning problem. The results demonstrate that BASCS achieves higher convergence accuracy and exhibits faster convergence speed and superior practical applicability compared to other algorithms.

Application of improved Jellyfish search algorithm for 9-parameters cell extraction and GMPPT in PV systems

Optimization algorithms are essential tools used to address real-world problems through minimization or maximization processes. In the context of photovoltaic (PV) systems, various optimization algorithms have been employed to extract solar cell parameters using minimization techniques, and to identify the maximum power point (MPP) using maximization approaches. However, under partial shading conditions or during rapidly changing irradiance, many of these algorithms tend to get trapped in local minima, failing to locate the global maximum power point (GMPPT). This shortcoming leads to significant energy losses from PV cells. Similarly, the extraction of solar cell parameters is often compromised by the random search behavior and inadequate exploration and exploitation capabilities of many existing algorithms. The Jellyfish Search Optimization (JSO) algorithm is a recent, parameter-free method developed to tackle a wide range of optimization problems. Despite its innovative design, JSO is prone to premature convergence and exhibits a longer convergence time. To address these limitations, this paper proposes a modified version of the JSO algorithm, which integrates the state-of-the-art features of JSO with the Lévy flight characteristic from the cuckoo search algorithm. This combination aims to achieve a better balance between exploration and exploitation. To validate the effectiveness of the proposed Improved Jellyfish Search Optimization (IJSO) algorithm in PV systems, extensive experiments were conducted. These experiments included benchmarking with complex test functions, extracting cell parameters using various solar cell models, and maximizing energy output from PV systems under different environmental conditions. The results demonstrate that the proposed IJSO algorithm effectively enhances parameter extraction and global maximum power point tracking, making it a promising solution for optimizing energy extraction from PV cells in dynamic conditions.

Power Loss Minimization and Voltage Profile Improvement on Nigeria Distribution Network Using Whale Optimization Algorithm

The power grid has significant power losses, unstable voltage, and large voltage drops during high demand due to its high resistance. One way to reduce power loss is by strategically placing and sizing Distributed Generation (DG) within the distribution network. However, improper installation of DG can increase power loss. To address this issue, various optimization techniques have been used, but finding the best solution and dealing with complex issues has been challenging. It is important to make efforts to optimally place and size DG in the distribution network to minimize power loss. To this end, a research study aimed to minimize power loss and improve the voltage profile on the DG network using the Whale Optimization Algorithm (WOA) was done. Objective functions were formulated and integrated into WOA, and power flow analysis on the standard IEEE 33-bus and 33-bus Ilorin industrial distribution feeder with and without DG was conducted using the forward and backward sweep distribution load flow algorithm. The optimal size and location of DG for power network loss reduction using sensitivity analysis (SA) and the whale optimization algorithm were determined. The results of the power flow analysis indicated that the total active losses and reactive power losses were reduced to varying extents with the integration of DG using both SA and WOA. The validation results showed that WOA is more efficient and provides high-quality solutions in terms of system loss reduction and best placement for DG in power systems compared to the application of SA. Additionally, WOA was found to offer accurate and high-quality solutions for power loss reduction compared to other existing techniques such as Loss Sensitivity Analysis (LSA), Grey Wolf Optimizer (GWO), Adaptive Shuffled Frogs Leaping Algorithm (ASFLA), and One Rank Cuckoo Search Algorithm (ORCSA). Keywords: Optimization, Distributed Generation (DG), Whale Optimization Algorithm (WOA), Sensitivity Analysis (SA)

A Fresh Manner Cuckoo Search Algorithm to Hydrothermal Scheduling in Short Term

The main goal of the short-term hydrothermal scheduling (HS) challenge is to drastically reduce the large fuel cost of producing power by scheduling the hydrothermal energy producers while taking power balance restrictions, the reservoir's storage restrictions, the water's gross discharge, and the thermal power generators' and hydropower plants' operating restrictions into account. Many algorithms have been employed to solve this similar problem, and relevant research have been published in the literature; nevertheless, their scope is limited in terms of the number of 16 iterations required to achieve the solution state and the solution state itself. In order to solve the HS problem, this article suggests applying an improved cuckoo search (CS) algorithm known as the fresh manner cuckoo search (FMCS) algorithm, a modified variant of the conventional CS. The suggested FMCS

Forecasting Daily Air Quality Index and Early Warning System for Estimating Ambient Air Pollution on Road Networks Using Gaussian Dispersion Model with Deep Learning Algorithm

The rapid growth of the vehicle population is a major factor in heavy air pollution and public health issues. Traffic-related air pollutants (TRAPs) on roads are often much higher than ambient values, leading to high exposure levels in vehicles. This research proposes a hybrid forecasting model for early detection and early warning systems (EWS) of road networks during real-world travels. Data is collected from Kannur, Calicut, Palakkad, and Coimbatore using real-time sensors, including surrounding discussion information, activity information, vehicle speed, and stopping events. The study predicts ambient air quality (AAQ) levels on the road network using the Gaussian Dispersion model (GDM) and measures the risk sensitivity of PM10 and PM2.5 in selected regions. This helps formulate powerful prevention strategies and prevent negative health impacts. The air pollution module for predicting concentration has an innovative hybridization model that combines an improved cuckoo search (CS) and differential evolution (DE) algorithm with a stacked LSTM model to increase forecasting accuracy of six major environmental pollution levels. This model predicts the AAQ level and is effective and robust for warning one day before the pollutant event occurs based on the risk level of an ambient air pollutant from the RN.

Leveraging Classifier Performance Using Heuristic Optimization for Detecting Cardiovascular Disease from PPG Signals.

Photoplethysmography (PPG) signals, which measure blood volume changes through light absorption, are increasingly used for non-invasive cardiovascular disease (CVD) detection. Analyzing PPG signals can help identify irregular heart patterns and other indicators of CVD. This research involves a total of 41 subjects sourced from the CapnoBase database, consisting of 21 normal subjects and 20 CVD cases. In the initial stage, heuristic optimization algorithms, such as ABC-PSO, the Cuckoo Search algorithm (CSA), and the Dragonfly algorithm (DFA), were applied to reduce the dimension of the PPG data. Next, these Dimensionally Reduced (DR) PPG data are then fed into various classifiers such as Linear Regression (LR), Linear Regression with Bayesian Linear Discriminant Classifier (LR-BLDC), K-Nearest Neighbors (KNN), PCA-Firefly, Linear Discriminant Analysis (LDA), Kernel LDA (KLDA), Probabilistic LDA (ProbLDA), SVM-Linear, SVM-Polynomial, and SVM-RBF, to identify CVD. Classifier performance is evaluated using Accuracy, Kappa, MCC, F1 Score, Good Detection Rate (GDR), Error rate, and Jaccard Index (JI). The SVM-RBF classifier for ABC PSO dimensionality reduced values outperforms other classifiers, achieving the highest accuracy of 95.12% along with the minimum error rate of 4.88%. In addition to that, it provides an MCC and kappa value of 0.90, a GDR and F1 score of 95%, and a Jaccard Index of 90.48%. This study demonstrated that heuristic-based optimization and machine learning classification of PPG signals are highly effective for the non-invasive detection of cardiovascular disease.

Arithmetic Optimization Algorithm Quantization Optimized With Energy Detection Using Nonparametric Amplitude

ABSTRACTThe spectrum sensing is a major significant task in cognitive radio networks (CRNs) to avoid the unacceptable interference to primary users (PUs). Here, the threshold value determines the effectiveness of spectrum sensing and regarded as a sensing system. The fixed threshold used by the current energy detection‐based spectrum sensing (SS) techniques does not provide sufficient safety for the main users. The threshold is determined by lowering the complete probability of decision error in addition to these guidelines. Therefore, an energy detection using nonparametric amplitude quantization optimized with arithmetic optimization algorithm for enhanced spectrum sensing in CRNs (ED‐NAQ‐AOA‐SS CRN) is proposed in this paper to acquire the ideal threshold for decreasing the total error probability. The proposed method achieves greater probability of detection of 99.67%, 98.38%, 92.34%, and 97.45%, lower settling time of 98.33%, 89.34%, 83.12%, and 88.96%, and lower error rate of 93.15%, 91.25%, 79.90%, and 92.88% compared with existing techniques, like intelligent spectrum sharing and sensing in CRN with adaptive rider optimization algorithm (AROA), a novel technique for spectrum sensing in CRN utilizing fractional gray wolf optimization with the cuckoo search optimization (GWOCS), and adaptive neuro‐fuzzy inference scheme depending on cooperative spectrum sensing optimization in CRNs (ANFIS).

Modeling and optimization of two-stage emergency supply chain network under uncertain environment

In order to formulate an effective emergency supply chain network planning scheme, improve the efficiency of emergency organization rescue and realize the reasonable allocation of resources and materials, considering the uncertainty of supply and demand, a distribution mode combining multiple transportation modes is adopted, and the optimization objectives are to minimize network response time, cost and carbon emissions. A two-stage emergency supply chain mixed integer programming model is constructed. At the same time, an adjustable robust optimization model is constructed based on robust optimization theory to enhance the network's ability to cope with uncertain factors, and the constraints with uncertain parameters are transformed through linear duality theory. In order to improve the solution effect of the model, an optimize cuckoo search (OCS) algorithm is proposed, and a benchmark example is introduced to verify the superiority and applicability of the OCS algorithm in solving multi-objective functions. Finally, the emergency material distribution data during the COVID-19 epidemic in Wuhan is used to study the emergency supply chain network decision-making problem with uncertain parameters, and the effective suppression of the robust control coefficient on uncertain disturbances is proved through sensitivity analysis.

Pipeline Leak Identification and Prediction of Urban Water Supply Network System with Deep Learning Artificial Neural Network

Pipeline leakage, which leads to water wastage, financial losses, and contamination, is a significant challenge in urban water supply networks. Leak detection and prediction is urgent to secure the safety of the water supply system. Relaying on deep learning artificial neural networks and a specific optimization algorithm, an intelligential detection approach in identifying the pipeline leaks is proposed. A hydraulic model is initially constructed on the simplified Net2 benchmark pipe network. The District Metering Area (DMA) algorithm and the Cuckoo Search (CS) algorithm are integrated as the DMA-CS algorithm, which is employed for the hydraulic model optimization. Attributing to the suspected leak area identification and the exact leak location, the DMA-CS algorithm possess higher accuracy for pipeline leakage (97.43%) than that of the DMA algorithm (92.67%). The identification pattern of leakage nodes is correlated to the maximum number of leakage points set with the participation of the DMA-CS algorithm, which provide a more accurate pathway for identifying and predicting the specific pipeline leaks.

RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques

ProblemBreast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process.AimThis study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data’s high dimensionality and complexity.MethodsWe introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model’s performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India.ResultsThe proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls.ConclusionOur findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes.

An EfficientNet integrated ResNet deep network and explainable AI for breast lesion classification from ultrasound images

AbstractBreast cancer is one of the major causes of deaths in women. However, the early diagnosis is important for screening and control the mortality rate. Thus for the diagnosis of breast cancer at the early stage, a computer‐aided diagnosis system is highly required. Ultrasound is an important examination technique for breast cancer diagnosis due to its low cost. Recently, many learning‐based techniques have been introduced to classify breast cancer using breast ultrasound imaging dataset (BUSI) datasets; however, the manual handling is not an easy process and time consuming. The authors propose an EfficientNet‐integrated ResNet deep network and XAI‐based framework for accurately classifying breast cancer (malignant and benign). In the initial step, data augmentation is performed to increase the number of training samples. For this purpose, three‐pixel flip mathematical equations are introduced: horizontal, vertical, and 90°. Later, two pre‐trained deep learning models were employed, skipped some layers, and fine‐tuned. Both fine‐tuned models are later trained using a deep transfer learning process and extracted features from the deeper layer. Explainable artificial intelligence‐based analysed the performance of trained models. After that, a new feature selection technique is proposed based on the cuckoo search algorithm called cuckoo search controlled standard error mean. This technique selects the best features and fuses using a new parallel zero‐padding maximum correlated coefficient features. In the end, the selection algorithm is applied again to the fused feature vector and classified using machine learning algorithms. The experimental process of the proposed framework is conducted on a publicly available BUSI and obtained 98.4% and 98% accuracy in two different experiments. Comparing the proposed framework is also conducted with recent techniques and shows improved accuracy. In addition, the proposed framework was executed less than the original deep learning models.

Cuckoo Search Optimization-Based Influence Maximization in Dynamic Social Networks

Online social networks are crucial in propagating information and exerting influence through word-of-mouth transmission. Influence maximization (IM) is the fundamental task in social network analysis to find the group of nodes that maximizes the influence in the social network. IM has different applications like viral marketing, campaigning, advertising, and so on. Literature has presented various algorithms based on different approaches to address the IM problem, including nature-inspired algorithms. Most of the work focuses on the static social network. The proposed work first employs nature-inspired Cuckoo Search Optimization to solve the IM problem in dynamic networks. The proposed algorithm applies the fuzzy-logic-based technique to optimize the nests. We also perform statistical tests to show the effectiveness of the proposed algorithm with the benchmark algorithms. The experimental results are performed on five datasets and compare the results with the state-of-the-art algorithms. The results show that the proposed algorithm gives better results than the nature-inspired state-of-the-art algorithms.

Novel hybrid data-driven models for enhanced renewable energy prediction

Global power grid management depends on accurate solar energy estimation, yet present prediction techniques frequently suffer from unreliability as a result of abnormalities in solar energy data. Solar radiation projections are affected by variables such as anticipated horizon length, meteorological classification, and power measuring techniques. Therefore, a Solar Wind Energy Prediction System (SWEPS) is proposed as a solution to these problems. It improves renewable energy projections by taking sun trajectories and atmospheric characteristics into account. In addition to using a variety of optimization methods and pre-processing techniques, such as Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Least Absolute Shrinkage Selection Operator (LASSO), and recursive feature addition processes (RFA), complemented by a genetic algorithm for feature selection (GAFS). The SWEPS also makes use of sophisticated machine learning algorithms and Statistical Correlation Analysis (SCA) to find important connections. Neural Network Algorithms (NNA) and other metaheuristic techniques like Cuckoo Search Optimization (CSO), Social Spider Optimization (SSO), and Particle Swarm Optimization (PSO) are adopted in this work to increase the predictability and accuracy of models. Utilizing the strengths of machine learning and deep learning techniques (Artificial Neural Networks (ANN), Decision Trees, Support Vector Machine (SVM), Recurrent Neural Networks (RNN), and Long Short Term Memory (LSTM)) for robust forecasting, as well as meta-heuristic optimization techniques to fine-tune hyper-parameters and achieve near-optimal values and significantly improve model performance, are some of this work contributions to the development of a comprehensive prediction system.

Adaptive feature mode decomposition method for bearing fault diagnosis under strong noise

In practical mechanical equipment operation, bearing vibration signals are challenging to analyze due to their non-stationarity and low signal-to-noise ratio for traditional detection methods. As a new signal decomposition method, the feature mode decomposition (FMD) method has been successfully applied to bearing fault diagnostics. However, FMD’s decomposition efficiency is easily influenced by the input parameter settings, and the efficiency of the decomposed signals is related to the number of initialized filter banks. For this reason, this paper proposes an adaptive parameterized feature mode decomposition method. Firstly, based bearing fault diagnosis method using a cuckoo search algorithm with logarithmic decline of nonlinear inertial weights and random adjustment discovery probability (DWCS), which is used to optimize the three parameters of the FMD. Then, a new approach to finding out the maximum feature fault frequency and its multiplicative feature frequency is proposed, called the feature frequency ratio (FFR), obtained from the envelope spectrum of bearing fault signal. Finally, this paper uses the DWCS based on the maximum FFR to adaptively select the best FMD parameter combination, which is verified by simulation, the results of the proposed AFMD can effectively extract bearing fault features under strong background noise with a signal-to-noise ratio of −15 dB, and actual experiments further verify the superiority of the method, which not only improves the accuracy of fault diagnosis under strong background noise, but also contributes to the overall maintenance and operational efficiency of the mechanical system.

Optimized Manufacturing and Construction Site Machines Maintenance Prediction using Machine Learning and Crayfish Algorithm

Efficient maintenance prediction is critical for ensuring the operational continuity and longevity of industrial machinery. This paper presents a comparative analysis of diverse machine-learning algorithms for the task of machine maintenance prediction. Through rigorous experimentation and evaluation, we assess the performance of algorithms including AdaBoost, Random Forest, Gradient Boosting, and Support Vector Machines (SVM). Additionally, to enhance predictive accuracy, we integrate an optimizer algorithm, Cuckoo Search,into our framework. This optimization technique fine-tunes algorithm parameters, further improving accuracy. Our findings offer valuable insights into optimizing machine maintenance prediction, empowering industries with proactive maintenance strategies to mitigate downtime and enhance productivity.

Data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on multi-head attention mechanism

Accurate and reliable daily reservoir inflow forecast plays an essential role in several applications involving the management and planning of water resources, such as hydroelectric generation, flood control, water supply, and basin ecological dispatching. Runoff usually exhibits strong non-linearity, high uncertainty, and spatial and temporal variability. Existing techniques fail to capture complete dynamics change processes effectively. A data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on a multi-head attention mechanism was developed, referred to as the GWOCS-VMD-CNN-Transformer (GCVCT). First, the model utilize Grey Wolf Optimizer coupled with Cuckoo Search (GWO-CS) algorithms to optimize parameters in variational mode decomposition model (VMD). This approach helps obtain highly correlated intrinsic mode function (IMF) components, enhancing the frequency resolution of the input dataset. The proposed method overcomes the bottleneck of other available methods by decomposing the time series to capture the main long-term and short-term properties of hydrological processes. Second, the convolution neural network and Transformer (CNN-Transformer) are based on a multi-head attention mechanism as the objective predictive method. Finally, six evaluation indicators verify the performance of the proposed approach. The approach’s reliability was evaluated using the historical daily reservoir inflow data from the Xiluodu (XLD) and Wudongde (WDD) reservoirs in the Jinsha River Basin, China. Several single and hybrid models were developed for comparative analysis. The results indicate that the proposed ensemble approach fits better than other developed model methods. The GCVCT model showed excellent performance in forecasting the inflows of XLD and WDD reservoirs, with NSE values of 0.985 and 0.984, respectively. Furthermore, the GCVCT framework forecast capacity for peak inflow was further verified through discussion and analysis of the 48 peak flows during the validation period, consistently outperforming other models in predicting peak flow for both study reservoirs. This framework provides an effective method for the scientific optimal scheduling of hydropower reservoirs, enabling more sustainable and efficient management practices. It also demonstrates the potential of powerful deep-learning models in intelligent hydrological forecasting.

Comprehensive performance analysis and optimization of an ORC-HDH system for power and freshwater production driven by marine exhaust gas

Freshwater serves as a vital energy source for ships, underscoring the paramount importance of research into desalination systems in facilitating diverse and reliable energy supply for maritime vessels. Aiming at numerous waste heat of marine engines and the shortage of freshwater resources on board, this paper designs an innovative waste heat recovery (WHR) system. It includes an organic Rankine cycle, a compression refrigeration cycle, and a humidification-dehumidification (HDH) desalination system to recover waste heat from exhaust gases in cascades, which can effectively produce power, cooling, and freshwater, respectively. A parametric study of the co-generation system in terms of the freshwater production, gained output ratio (GOR), cost per unit of freshwater production, net power output, and per unit of output power cost, are conducted to identify the key operating parameters. A set of operating parameters is determined for the HDH system with a temperature of the humidifier outlet of 358.15 K and a mass flow rate ratio (MR) of seawater to humid air of 4.5. The effect of the relative humidity and temperature of the humid air, as well as the salinity and temperature of the seawater, on the performance of the HDH subsystem are analyzed. And the optimal operating conditions of the co-generation system are obtained through multi-objective cuckoo search optimization. The results demonstrate that the co-generation system can produce an output-power of 1479.8 kW, 500 kW, and 0.49 kg/s for cooling and freshwater production by using the working-fluid R365mfc/Cyclopentane with a mixing ratio of 33:67 under optimal conditions. The GOR and cost per unit of HDH subsystem are 1.62 and 1.70 $/m3, with a capital payback period of 4.18 years. The equivalent output-power of the co-generation system is 9.5 % higher than that of an ORC system.