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Content introduction
With the growth of energy demand and the increasingly prominent environmental issues, the utilization of renewable energy has attracted more and more attention. Among them, wind energy, as one of the widely available renewable energy sources, has huge potential. However, due to the instability and unpredictability of wind energy, accurate prediction of wind power has become one of the key issues in wind farm operation and power system dispatching.
Time series forecasting is one of the important methods for analyzing wind power, load and other energy-related data. In recent years, deep learning algorithms have achieved remarkable results in the field of time series forecasting. Among them, long short-term memory network (LSTM), as a special recurrent neural network, has strong memory ability and modeling ability of long sequences, and is widely used in time series prediction tasks.
However, the traditional LSTM model has some shortcomings when dealing with time series prediction problems. To overcome these problems, the researchers proposed a method to optimize LSTM based on the quantum particle swarm algorithm (QPSO). This method optimizes the parameters of LSTM by introducing the quantum particle swarm algorithm and improves the prediction performance of the model.
The following are the steps to optimize the LSTM time series forecasting algorithm for wind power, load, etc. based on the quantum particle swarm algorithm:
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Data preprocessing: First, preprocess the original data, including data cleaning, missing value processing, and normalization. These steps can improve the quality and usability of your data.
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Feature extraction: Extract meaningful features from preprocessed data. For time series data such as wind power and load, you can consider extracting time series features, frequency domain features, and statistical features.
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Build LSTM model: Before QPSO optimization, first build a basic LSTM model. The LSTM model consists of multiple LSTM layers and an output layer, where the LSTM layer is used to extract features of sequence data, and the output layer is used to predict future values.
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Quantum particle swarm algorithm optimization: Use the constructed LSTM model as the objective function, use the parameters of the LSTM model as the position of the particles, and use the quantum particle swarm algorithm to optimize the parameters. By iteratively updating the position and velocity of particles, the optimal parameter combination is gradually found.
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Model training and validation: Use the optimized LSTM model to train on the training set and validate on the validation set. Evaluate the performance of your model by comparing the difference between predicted results and actual results.
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Model evaluation and tuning: evaluate and tune the optimized LSTM model. The performance of the model can be evaluated by calculating indicators such as prediction error, root mean square error, and average absolute percentage error, and the model can be tuned based on the evaluation results.
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Forecasting application: Use the optimized LSTM model to predict future wind power, load and other time series data. Based on the prediction results, applications such as wind farm operation scheduling and power system load forecasting can be carried out.
Optimizing LSTM’s wind power, load and other time series forecasting algorithms based on the quantum particle swarm algorithm can improve the forecasting performance and stability of the model. By introducing the quantum particle swarm algorithm, the parameters of the LSTM model can be effectively optimized and the generalization ability and adaptability of the model can be improved. This method has important application value in the fields of time series forecasting such as wind power and load, and can provide effective solutions to problems such as wind farm operation and power system dispatching.
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
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References
[1] Chen Daojun, Gong Qingwu, Jin Chaoyi, et al. Short-term wind power power prediction with support vector regression machine based on parameter optimization of adaptive perturbation quantum particle swarm algorithm [J]. Power Grid Technology, 2013, 37(4):7.DOI:CNKI: SUN:DWJS.0.2013-04-013.
[2] Chen Daojun. Research on short-term wind power power forecasting and grid-connected low-carbon dispatch[D]. Wuhan University, 2013.
[3] Huang Li, Peng Daogang, Gu Liqun, et al. Research on optimal load allocation based on improved quantum particle swarm algorithm [J]. Control Engineering, 2017, 24(7):7.DOI:10.14107/j.cnki.kzgc.150380.