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  • Open Access


    Short-Term Wind Power Prediction Based on Combinatorial Neural Networks

    Tusongjiang Kari1, Sun Guoliang2, Lei Kesong1, Ma Xiaojing1,*, Wu Xian1

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1437-1452, 2023, DOI:10.32604/iasc.2023.037012

    Abstract Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation. Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections. For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model, the short-term prediction of wind power based on a combined neural network is proposed. First, the Bi-directional Long Short Term Memory (BiLSTM) network prediction model is constructed, and the bi-directional nature of the BiLSTM network is used to deeply mine the wind… More >

  • Open Access


    Wind Power Prediction Based on Machine Learning and Deep Learning Models

    Zahraa Tarek1, Mahmoud Y. Shams2,*, Ahmed M. Elshewey3, El-Sayed M. El-kenawy4,5, Abdelhameed Ibrahim6, Abdelaziz A. Abdelhamid7,8, Mohamed A. El-dosuky1,9

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 715-732, 2023, DOI:10.32604/cmc.2023.032533

    Abstract Wind power is one of the sustainable ways to generate renewable energy. In recent years, some countries have set renewables to meet future energy needs, with the primary goal of reducing emissions and promoting sustainable growth, primarily the use of wind and solar power. To achieve the prediction of wind power generation, several deep and machine learning models are constructed in this article as base models. These regression models are Deep neural network (DNN), k-nearest neighbor (KNN) regressor, long short-term memory (LSTM), averaging model, random forest (RF) regressor, bagging regressor, and gradient boosting (GB) regressor. In addition, data cleaning and… More >

  • Open Access


    Power Prediction of VLSI Circuits Using Machine Learning

    E. Poovannan*, S. Karthik

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2161-2177, 2023, DOI:10.32604/cmc.2023.032512

    Abstract The difference between circuit design stage and time requirements has broadened with the increasing complexity of the circuit. A big database is needed to undertake important analytical work like statistical method, heat research, and IR-drop research that results in extended running times. This unit focuses on the assessment of test strength. Because of the enormous number of successful designs for current models and the unnecessary time required for every test, maximum energy ratings with all tests cannot be achieved. Nevertheless, test safety is important for producing trustworthy findings to avoid loss of output and harm to the chip. Generally, effective… More >

  • Open Access


    Frequency Control Approach and Load Forecasting Assessment for Wind Systems

    K. Sukanya*, P. Vijayakumar

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 971-982, 2023, DOI:10.32604/iasc.2023.028047

    Abstract Frequency deviation has to be controlled in power generation units when there are fluctuations in system frequency. With several renewable energy sources, wind energy forecasting is majorly focused in this work which is a tough task due to its variations and uncontrollable nature. Whenever there is a mismatch between generation and demand, the frequency deviation may arise from the actual frequency 50 Hz (in India). To mitigate the frequency deviation issue, it is necessary to develop an effective technique for better frequency control in wind energy systems. In this work, heuristic Fuzzy Logic Based Controller (FLC) is developed for providing… More >

  • Open Access


    Gaussian Kernel Based SVR Model for Short-Term Photovoltaic MPP Power Prediction

    Yasemin Onal*

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 141-156, 2022, DOI:10.32604/csse.2022.020367

    Abstract Predicting the power obtained at the output of the photovoltaic (PV) system is fundamental for the optimum use of the PV system. However, it varies at different times of the day depending on intermittent and nonlinear environmental conditions including solar irradiation, temperature and the wind speed, Short-term power prediction is vital in PV systems to reconcile generation and demand in terms of the cost and capacity of the reserve. In this study, a Gaussian kernel based Support Vector Regression (SVR) prediction model using multiple input variables is proposed for estimating the maximum power obtained from using perturb observation method in… More >

  • Open Access


    Forecasting Model of Photovoltaic Power Based on KPCA-MCS-DCNN

    Huizhi Gou1,2,*, Yuncai Ning1

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 803-822, 2021, DOI:10.32604/cmes.2021.015922

    Abstract Accurate photovoltaic (PV) power prediction can effectively help the power sector to make rational energy planning and dispatching decisions, promote PV consumption, make full use of renewable energy and alleviate energy problems. To address this research objective, this paper proposes a prediction model based on kernel principal component analysis (KPCA), modified cuckoo search algorithm (MCS) and deep convolutional neural networks (DCNN). Firstly, KPCA is utilized to reduce the dimension of the feature, which aims to reduce the redundant input vectors. Then using MCS to optimize the parameters of DCNN. Finally, the photovoltaic power forecasting method of KPCA-MCS-DCNN is established. In… More >

  • Open Access


    Power Data Preprocessing Method of Mountain Wind Farm Based on POT-DBSCAN

    Anfeng Zhu, Zhao Xiao, Qiancheng Zhao*

    Energy Engineering, Vol.118, No.3, pp. 549-563, 2021, DOI:10.32604/EE.2021.014177

    Abstract Due to the frequent changes of wind speed and wind direction, the accuracy of wind turbine (WT) power prediction using traditional data preprocessing method is low. This paper proposes a data preprocessing method which combines POT with DBSCAN (POT-DBSCAN) to improve the prediction efficiency of wind power prediction model. Firstly, according to the data of WT in the normal operation condition, the power prediction model of WT is established based on the Particle Swarm Optimization (PSO) Arithmetic which is combined with the BP Neural Network (PSO-BP). Secondly, the wind-power data obtained from the supervisory control and data acquisition (SCADA) system… More >

  • Open Access


    Analysis and Application of the Spatio-Temporal Feature in Wind Power Prediction

    Ruiguo Yu1,2, Zhiqiang Liu1,2, Jianrong Wang1,3, Mankun Zhao1,2, Jie Gao1,3, Mei Yu1,3,*

    Computer Systems Science and Engineering, Vol.33, No.4, pp. 267-274, 2018, DOI:10.32604/csse.2018.33.267

    Abstract The spatio-temporal feature with historical wind power information and spatial information can effectively improve the accuracy of wind power prediction, but the role of the spatio-temporal feature has not yet been fully discovered. This paper investigates the variance of the spatio-temporal feature. Based on this, a hybrid machine learning method for wind power prediction is designed. First, the training set is divided into several groups according to the variance of the input pattern, and then each group is used to train one or more predictors respectively. Multiple machine learning methods, such as the support vector machine regression and the decision… More >

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