Accurate wind power prediction plays a vital role in ensuring the safe operation of wind power connected to the grid. To improve the prediction accuracy of wind power, a short-term wind power prediction model (CEEMDAN...
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ISBN:
(纸本)9783031723551;9783031723568
Accurate wind power prediction plays a vital role in ensuring the safe operation of wind power connected to the grid. To improve the prediction accuracy of wind power, a short-term wind power prediction model (CEEMDAN-ICOA-GRU) based on a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) combined with improved coati optimization algorithm(ICOA) to optimize Gated Recurrent Unit (GRU) is proposed in this paper. First, CEEMDAN was used to decompose the original wind power data, reduce its volatility, and reduce the lag of the forecast curve. Then, to solve the problem that the traditional coati optimization algorithm(COA) is prone to locally optimal solutions, chaotic sequences are added to improve the initial population distribution to be more uniform. Finally, ICOA is used to optimize the hyperparameters of GRU to obtain the optimal prediction model, which is used to predict different sub-sequences, and the prediction results are superimposed to obtain the final prediction results. To verify the validity of the model, a large number of experiments were conducted using the data set of a wind power plant in Turkey in 2022. The results show that CEEMDAN-ICOA-GRU can effectively improve the accuracy of wind power prediction.
Fault tolerance is the network's capacity to continue operating normally in the event of sensor failure. Sensor nodes in wireless sensor networks (WSNs) may fail due to various reasons, such as energy depletion or...
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Fault tolerance is the network's capacity to continue operating normally in the event of sensor failure. Sensor nodes in wireless sensor networks (WSNs) may fail due to various reasons, such as energy depletion or environmental damage. Sensor battery drain is the leading cause of failure in WSNs, making energy-saving crucial to extending sensor lifespan. Fault-tolerant protocols use fault recovery methods to ensure network reliability and resilience. Many issues can affect a network, such as communication module breakdown, battery drain, or changes in network architecture. Our proposed FT-RR protocol is a WSN routing protocol that is both reliable and fault-tolerant;it attempts to prevent errors by anticipating them. FT-RR uses Bernoulli's rule to find trustworthy nodes and then uses those pathways to route data to the base station as efficiently as possible. When CHs have greater energy, they construct these pathways. Based on the simulation findings, our approach outperforms the other protocols concerning the rate of loss of packet, end-to-end latency, and network lifespan.
Building energy consumption (BEC) prediction is crucial in efficient energy management. This paper proposes an optimized hybrid prediction model that combines a support vector regression (SVR), a newly evolved coati o...
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Building energy consumption (BEC) prediction is crucial in efficient energy management. This paper proposes an optimized hybrid prediction model that combines a support vector regression (SVR), a newly evolved coati optimization algorithm (COA), and a recursive feature elimination with cross-validation (RFECV) implemented on an hourly new dataset. The SVR is selected based on the experimentations conducted in this work that outperform other models. The COA is used for optimizing the hyperparameters of SVR, and RFECV is used to optimize the dataset. The SVR COA performs better than the Harris hawk optimization and Gray wolf optimization. Later, the optimized SVR is implemented on the optimized dataset, showing better accuracy and faster prediction compared with the default SVR, SVR with feature elimination, and optimized SVR models. The error metrics MAE, MAPE, RMSE, and R2 are used for the model evaluation. The testing accuracy improved by 12.34%, 10.52%, 17.02%, and 0.09%, respectively, compared to the default model.
Tensegrity structures, characterized by enhanced stiffness, slender struts, and superior buckling resistance, have found wide-ranging applications in fields such as engineering, architecture, art, biology, and robotic...
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Tensegrity structures, characterized by enhanced stiffness, slender struts, and superior buckling resistance, have found wide-ranging applications in fields such as engineering, architecture, art, biology, and robotics, attracting extensive attention from researchers. The form-finding process, a critical step in the design of tensegrity structures, aims to discover the self-equilibrated configuration that satisfies specific design requirements. Traditional form-finding methods based on force density often require repeated steps of eigenvalue decomposition and singular value decomposition, making the process complex. In contrast, this paper introduces a new intelligent form-finding algorithm that uses the force density method and combines the coati optimization algorithm with Graph Neural Networks. This algorithm avoids the complex steps of eigenvalue and singular value decomposition and integrates the physical knowledge of the structure, making the form-finding process faster and more accurate. In this algorithm, various force densities are initially randomized and input into a trained Graph Neural Networks to predict a fitness function's value. Through optimizing the constrained fitness function, the algorithm determines the appropriate structural force density and coordinates, thereby completing the form-finding process of the structure. The paper presents seven typical tensegrity structure examples and compares various form-finding methods. The results of numerical examples show that the method proposed in this paper can find solutions that align with the super-stable line more quickly and accurately, demonstrating its potential value in practical applications.
Challenges in the operation of power systems arise from several factors such as the interconnection of large power systems, integration of new energy sources and the increase in electrical energy demand. These challen...
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Challenges in the operation of power systems arise from several factors such as the interconnection of large power systems, integration of new energy sources and the increase in electrical energy demand. These challenges have required the development of fast and reliable tools for evaluating the operation of power systems. The load margin (LM) is an important index in evaluating the stability of power systems, but traditional methods for determining the LM consist of solving a set of differential-algebraic equations whose information may not always be available. Data-Driven techniques such as Artificial Neural Networks were developed to calculate and monitor LM, but may present unsatisfactory performance due to difficulty in generalization. Therefore, this article proposes a design method for Physics-Informed Neural Networks whose parameters will be tuned by bio-inspired algorithms in an optimization model. Physical knowledge regarding the operation of power systems is incorporated into the PINN training process. Case studies were carried out and discussed in the IEEE 68-bus system considering the N-1 criterion for disconnection of transmission lines. The PINN load margin results obtained by the proposed method showed lower error values for the Root Mean Square Error (RMSE), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) indices than the traditional training Levenberg-Marquard method.
The primary aim of this article is to propose an effective dengue disease monitoring system by integrating deep learning models with Internet of Things (IoT) and fog computing. In this context, the disease related par...
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The primary aim of this article is to propose an effective dengue disease monitoring system by integrating deep learning models with Internet of Things (IoT) and fog computing. In this context, the disease related parameters are collected using IoT devices and further, the reports are securely transmitted to the healthcare facilities utilizing fog computing. In this article, the misdiagnosis error is reduced utilizing attention based long short term memory (ALSTM) network with coati optimization algorithm (COA). The Attention model allocates higher weight to significant parameters and pay additional consideration while training the model. Therefore, the conventional LSTM network is combined with a self-attention mechanism for enabling this model to focus on relevant parts of the input sequences for precise dengue disease prediction. The Attention Model differs from the conventional model since it delivers higher amount of data into the decoder and improves additional stages to the attention decoder before making its output. Furthermore, the COA is integrated with the ALSTM network for optimal hyper-parameter selection. The ALSTM-COA model evaluates the dengue related parameters like nausea, vomiting, skin rash, joint pain, muscle pain, soft bleeding, temperature of water-sites, humidity of water-sites, and so forth, for timely dengue disease diagnosis and clinical decision-making. The experiments performed on a real time dataset state that the ALSTM-COA model achieves significant prediction results by utilizing different performance measures like recall, accuracy, fall-out, miss rate, training time, and testing time. The ALSTM-COA model obtains an accuracy of 99.27%, latency of 0.03 s, and time complexity of 8 s for 500 health records, which are superior to the comparative deep learning models.
Electricity generation in Islanded Urban Microgrids (IUMG) now relies heavily on a diverse range of Renewable Energy Sources (RES). However, the dependable utilization of these sources hinges upon efficient Electrical...
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Electricity generation in Islanded Urban Microgrids (IUMG) now relies heavily on a diverse range of Renewable Energy Sources (RES). However, the dependable utilization of these sources hinges upon efficient Electrical Energy Storage Systems (EESs). As the intermittent nature of RES output and the low inertia of IUMGs often lead to significant frequency fluctuations, the role of EESs becomes pivotal. While these storage systems effectively mitigate frequency deviations, their high costs and elevated power density requirements necessitate alternative strategies to balance power supply and demand. In recent years, substantial attention has turned towards harnessing Electric Vehicle (EV) batteries as Mobile EV Energy Storage (MEVES) units to counteract frequency variations in IUMGs. Integrating MEVES into the IUMG infrastructure introduces complexity and demands a robust control mechanism for optimal operation. Therefore, this paper introduces a robust, high-order degree of freedom cascade controller known as the 1PD-3DOF-PID (1 + Proportional + Derivative-Three Degrees Of Freedom Proportional-Integral-Derivative) controller for Load Frequency Control (LFC) in IUMGs integrated with MEVES. The controller's parameters are meticulously optimized using the coati optimization algorithm (COA) which mimics coati behavior in nature, marking its debut in LFC of IUMG applications. Comparative evaluations against classical controllers and algorithms, such as 3DOF-PID, PID, Reptile Search algorithm, and White Shark Optimizer, are conducted under diverse IUMG operating scenarios. The testbed comprises various renewable energy sources, including wind turbines, photovoltaics, Diesel Engine Generators (DEGs), Fuel Cells (FCs), and both Mobile and Fixed energy storage units. Managing power balance in this entirely renewable environment presents a formidable challenge, prompting an examination of the influence of MEVES, DEG, and FC as controllable units to mitigate active power imbalance
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