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Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system
Signal processing Image processing Path planning Cellular automaton Drone
Content introduction
As energy demand continues to grow, the use of renewable energy sources is becoming increasingly important. Among them, wind energy, as a widely available renewable energy, has received widespread attention. However, the instability and uncertainty of wind speed bring challenges to the utilization of wind energy. Therefore, accurate prediction of wind speed is crucial for efficient utilization of wind energy resources.
In the field of wind speed prediction, methods based on machine learning have been widely used. Among them, Support Vector Machine (SVM) is a commonly used machine learning algorithm with good prediction performance. However, the traditional SVM method has certain limitations when dealing with nonlinear and non-stationary signals. In order to overcome these problems, an improved algorithm based on SVM – Least Squares Support Vector Machine (LSSVM) was proposed.
However, the LSSVM method still has some problems, such as being sensitive to initial parameters and easily falling into local optimality. In order to further improve the accuracy and stability of wind speed prediction, this paper proposes a wind speed prediction algorithm based on CEEMD (Complete Ensemble Empirical Mode Decomposition) combined with Slime Mold Algorithm (SMA) to optimize LSSVM – CEEMDAN-SMA-LSSVM .
First, the CEEMDAN method is applied to the decomposition of original wind speed data to decompose nonlinear and non-stationary signals into a series of intrinsic mode functions (Intrinsic Mode Functions, IMFs). Then, the SMA algorithm was introduced to optimize the parameter selection and model training of LSSVM by simulating the behavior of slime mold. Finally, the optimized LSSVM model is used to predict wind speed.
The process of CEEMDAN-SMA-LSSVM algorithm is as follows:
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Enter raw wind speed data.
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The CEEMDAN method is used to decompose the original data and obtain a series of IMFs.
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Use the SMA algorithm to optimize LSSVM parameters, including selecting appropriate penalty factors, kernel functions and model parameters.
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The optimized LSSVM model is used to train IMFs.
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Predict the test data and obtain the wind speed prediction results.
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Evaluate the accuracy and stability of forecast results.
Through the CEEMDAN-SMA-LSSVM algorithm, we can overcome the limitations of traditional methods in processing nonlinear and non-stationary signals and improve the accuracy and stability of wind speed prediction. Experimental results show that the algorithm has good performance in wind speed prediction.
In summary, wind speed prediction is of great significance in wind energy utilization. This paper proposes a wind speed prediction algorithm based on CEEMD combined with the slime mold algorithm to optimize LSSVM. This algorithm can effectively overcome the limitations of traditional methods and improve the accuracy and stability of wind speed prediction. It is believed that this algorithm will provide strong support for the effective utilization of wind energy resources and play an important role in the field of renewable energy.
Part of the code
%% Clear environment variables</code><code>warning off % Close alarm information</code><code>close all % Close open figure window</code><code>clear % Clear variables</code><code>clc % clear command line</code><code>?</code><code>%% import data</code><code>res = xlsread('dataset.xlsx');</code><code>?</code><code>%% divide the training set and test set</code><code>temp = randperm(357);</code><code>?</code><code>P_train = res(temp(1: 240), 1: 12)';</code><code>T_train = res(temp(1: 240), 13)';</code><code>M = size(P_train , 2);</code><code>?</code><code>P_test = res(temp(241: end), 1: 12)';</code><code>T_test = res(temp(241 : end), 13)';</code><code>N = size(P_test, 2);</code><code>?</code><code>%% data normalization</code><code>[p_train, ps_input] = mapminmax(P_train, 0, 1);</code><code>p_test = mapminmax('apply', P_test, ps_input);</code><code>t_train = ind2vec(T_train) ;</code><code>t_test = ind2vec(T_test );
Operation results?
References
[1] Li Xiao. Wind power power prediction based on lifting wavelet and least squares support vector machine [D]. Shanghai Jiao Tong University, 2015.
[2] Luo Wei, Xi Huayong. Rainfall prediction based on least squares support vector machine [J]. People’s Yangtze River, 2008, 39(19):3.DOI:10.3969/j.issn.1001-4179.2008.19.011.
[3] Gao Wei. Research on short-term forecasting of wind power power based on least squares support vector machine [D]. Huazhong University of Science and Technology, 2014. DOI: 10.7666/d.D611958.
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1 Improvements and applications of various intelligent optimization algorithms
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics site selection, cargo space optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, Container ship stowage optimization, water pump combination optimization, medical resource allocation optimization, facility layout optimization, visible area base station and drone site selection optimization
2 Machine learning and deep learning
Convolutional neural network (CNN), LSTM, support vector machine (SVM), least squares support vector machine (LSSVM), extreme learning machine (ELM), kernel extreme learning machine (KELM), BP, RBF, width Learning, DBN, RF, RBF, DELM, XGBOOST, TCN realize wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health status prediction, water body Optical parameter inversion, NLOS signal identification, accurate subway parking prediction, transformer fault diagnosis
2. Image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image splicing, image fusion, image enhancement, image compressed sensing
3 Path planning
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), UAV three-dimensional path planning, UAV collaboration, UAV formation, robot path planning, raster map path planning , multimodal transportation problems, vehicle collaborative UAV path planning, antenna linear array distribution optimization, workshop layout optimization
4 UAV application
UAV path planning, UAV control, UAV formation, UAV collaboration, UAV task allocation, and online optimization of UAV safe communication trajectories
5 Wireless sensor positioning and layout
Sensor deployment optimization, communication protocol optimization, routing optimization, target positioning optimization, Dv-Hop positioning optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI positioning optimization
6 Signal processing
Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, EMG signal, EEG signal, signal timing optimization
7 Power system aspects
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration
8 Cellular Automata
Traffic flow, crowd evacuation, virus spread, crystal growth
9 Radar aspect
Kalman filter tracking, track correlation, track fusion
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