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The improvement of load forecasting accuracy is an important issue in the scientific optimization of power systems. The availability of accurate statistical data and a suitable scientific method are necessary for a perfect prediction of future occurrences. This research deals with the use of a regression forecast model (Support Vector Machine, SVM)...
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... any learning method depends on a driven data which is based on the multi-disciplinary ideas that combine with the facility of a computer to perform an expected action on other datasets [16]. Fig. 1 provides a schematic flowchart of any simple learning model [17]. Science and Applications, Vol. 9, No. 11, 2018 301 | P a g e www.ijacsa.thesai.org Vladimir proposed one of the most famous artificial intelligent learning machines and named it as Support Vector Machines (SVM) [18]. Basically, the SVM optimization equations can be ...
Citations
... Before the application of the wind turbine, the authors validated the modification via 28 pinch mark functions. Separated types of modifications by making a series, parallel, and embedding combination between more than AI methods; have been examined in electrical engineering especially the wind turbine control (Anantha Krishnan and Senthil Kumar, 2022;Bhanu and Pappa, 2017;Jaber et al., 2018; Jaikhang and Tunyasrirut, 2019; Kamarzarrin and Refan, 2020; Kim et al., 2013;Maroufi et al., 2020;Perng et al., 2014;Poultangari et al., 2012;Qiao et al., 2011;Shan et al., 2022;Sheikhan et al., 2013;Sun et al., 2020). The contributing parameters of each participating optimization method can be estimated as a function of simple case parameters. ...
The exploitation of nature to convert energy to electrical power is the most important rule in power generation. Wind energy is one of the most important of those energies that are widely available, and its use does not affect the environment significantly compared to fossil energy. On the other hand, and in recent times, researchers have made great efforts in the field of intelligent control andoptimization, which has led to great leaps in the development of these sciences. In this paper, a detailed study is proposed for filling the gaps and conducting an updating state-of-arts of the last pitch control methods in the wind turbine systems. The review is conducted by comparing the key requirements related to control, complexity, stability and speed rangeability. Furthermore, a new classification for the general controller is introduced according to the techniques. Several recommendations for future research related to the control and technical evaluation of wind energy are presented. In sum, the appropriate classification of such important issues and identification of their advantages and drawbacks may greatly contribute to find better solutions.
... One of the main challenges is that the optimization process can be timeconsuming and computationally intensive, depending on the complex problems [4] . Additionally, the optimization process can be hampered by the availability of data, as it requires accurate and up-to-date information in order to identify the most efficient way to identify the solution [5][6][7][8] . Generally, intelligent methods are inspired by nature, whether a physical phenomenon or the movement of a primitive or highly evolving being [9][10][11][12][13] . ...
p>The rapid growth of distributed generation (DG) units has necessitated their optimization to address the increasing complexity of power grids and reduce power losses. The need for optimization of distributed generation (DG) units has been growing rapidly over the past few years. To minimize such losses, the optimal allocation of DG units needs to be correctly identified and applied. On the other hand, Garra Rufa optimization (GRO) is a mathematical optimization technique that is used to determine the high effective and efficient way to solve very complex problems to achieve optimal results. In this work, Garra Rufa optimization is used to identify the optimal placement and size of DG units in order to meet specific power loss requirements. A comparison between genetic algorithm (GA), particle swarm optimization (PSO), and GRO is done using MATLAB to validate the proposed method. The comparison shows that GRO is better than the other methods in DG allocation, especially in more than two DGs. The optimization techniques are evaluated using the IEEE standard power system case, specifically the 30-bus configuration.</p
... Advanced models will be able to improve predictive performance. Currently, models for load forecasting can be divided into two categories: statistical methods (EPA-based, MRV-based, linear regression, etc. [21]) and machine learning-based methods (SVM [22], neural network [23], deep learning [24], etc.). Jaber et al. [22] used the SVM-PSO method to predict the energy consumption of electrical appliances in an urban power grid. ...
... Currently, models for load forecasting can be divided into two categories: statistical methods (EPA-based, MRV-based, linear regression, etc. [21]) and machine learning-based methods (SVM [22], neural network [23], deep learning [24], etc.). Jaber et al. [22] used the SVM-PSO method to predict the energy consumption of electrical appliances in an urban power grid. The hybrid PSO and SVM methods are proposed to forecast non-linear load data. ...
... In the past decades, power load forecasting has been extensively studied. By analyzing the characteristics of the power load curve, researchers have obtained various features of power load [22]. For shore power load prediction, various types of ships, and working states of electrical equipment also impact power load [19]. ...
Load forecasting of shore power (SP) plays an important role in the power decision-making of the electrical grid due to docked ships are necessary to plug into the electrical grid. However, obtaining a large amount of labeled data on docked ships is time-consuming, presenting a challenge for Shore Power Load Forecasting. Additionally, multiple raw information entries for docked ships could lead to feature redundancy. To address these issues, we proposed a novel three-stage load forecasting method which includes attributive feature selection, semi-supervised learning (SSL) method for the mean of load distribution prediction, and a transformer-based model for variance prediction. Firstly, Firefly Algorithm (FA) is adopted to extract representative attribute features of docked ships to deal with the feature redundancy. Next, the selected feature set and label set are divided into two parts: a few labeled data and a large amount of labeled data. And we propose a Π-model-based SSL method to predict the load distribution. Finally, we propose a transformer-based model to predict the variance of load distribution. Our model takes into account all historical load data of each docked ship for context learning. Further, we consider that the attribute features would also affect the variance prediction, so the latent features of the Π-model are served as the initial condition which concatenates historical load data. We evaluated our model using 328 power load data from various ships that berth at Zhenjiang Port with shore power, totaling approximately 21,521 hours. The experiments prove the accuracy and efficiency of our proposed model, producing promising forecasting results.
... Advanced models will be able to im-prove predictive performance. Currently, models for load forecasting can be divided into two categories: statistical methods (EPA-based, MRV-based, linear regression, etc. [24]) and machine learning-based methods (SVM [25], neural network [26], deep learning [27], etc.). Jaber et al. [25] used the SVM-PSO method to predict the energy consumption of electrical appliances in urban power grid. ...
... Currently, models for load forecasting can be divided into two categories: statistical methods (EPA-based, MRV-based, linear regression, etc. [24]) and machine learning-based methods (SVM [25], neural network [26], deep learning [27], etc.). Jaber et al. [25] used the SVM-PSO method to predict the energy consumption of electrical appliances in urban power grid. The hybrid PSO and SVM methods are proposed to forecast non-linear load data. ...
Shipping plays an important role in transporting goods, but it also brings air pollution such as nitrogen and sulfur compounds. Meanwhile, shore power can provide daily work power for ships calling at ports, and it can reduce the pollution of the port. Load forecasting of shore power plays an important role in power decision-making. In this work, we proposed a load forecasting model based on the Transformer and the conditional generation. The Firefly Algorithm (FA) was designed to extract the represented condition features. The proposed shore power transformer (SP-T) method adopts the probability distribution of the target load as the prediction results. The two sub-models, SP-T-1 and SP-T-2, were used to predict the Gaussian distribution parameters μ and σ, respectively. It allowed decision-makers to set the confidence level to obtain the range of predicted values according to the actual situation. To evaluate the proposed model, we used 328 power load data (about 18568.4 hours) of different ships that berth at Zhenjiang Port with shore power. The experimental results showed that SP-T can effectively predict the load data of shore power, with an average P50RMSE was 46.997 and an average P90RMSE was 34.822.
