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short-term power load forecasting with Hybrid TPA-BiLSTM Prediction Model Based on CSSA

Computer Modeling in Engineering & Sciences 2023年第7期136卷 749-765页

作者： Jiahao Wen Zhijian Wang School of Information Science Guangdong University of Finance and EconomicsGuangzhouChina

Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting,we propose a bidirectional long short-term memory(BiLSTM)neural network model based on the temporal pattern attention(TPA)***,based on the grey relational analysis,datasets similar to forecast day are ***,thebidirectional LSTM layermodels the data of thehistorical load,temperature,humidity,and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network,so that the influencing factors(with different characteristics)can select relevant information from different time steps to reduce the prediction error of the ***,the complex and nonlinear dependencies between time steps and sequences are extracted by the TPA mechanism,so the attention weight vector is constructed for the hidden layer output of BiLSTM and the relevant variables at different time steps are weighted to influence the ***,the chaotic sparrow search algorithm(CSSA)is used to optimize the hyperparameter selection of the *** short-term power load forecasting on different data sets shows that the average absolute errors of short-termpower load forecasting based on our method are 0.876 and 4.238,respectively,which is lower than other forecastingmethods,demonstrating the accuracy and stability of our model.

关键词： Chaotic sparrow search optimization algorithm TPA BiLSTM short-term power load forecasting grey relational analysis

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short-term Wind power Prediction Method Based on Combination of Meteorological Features and CatBoost

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Wuhan University Journal of Natural Sciences 2023年第2期28卷 169-176页

作者： MOU Xingyu CHEN Hui ZHANG Xinjing XU Xin YU Qingbo LI Yunfeng Jilin Province Meteorological Information Network Center Changchun 130062JilinChina

As one of the hot topics in the field of new energy,short-term wind power prediction research should pay attention to the impact of meteorological characteristics on wind power while improving the prediction ***,a short-term wind power prediction method based on the combination of meteorological features and Cat Boost is ***,morgan-stone algebras and sure independence screening(MS-SIS)method is designed to filter the meteorological features,and the influence of the meteorological features on the wind power is ***,a sort enhancement algorithm is designed to increase the accuracy and calculation efficiency of the method and reduce the prediction risk of a single ***,a prediction method based on Cat Boost network is constructed to further realize short-term wind power *** National Renewable Energy Laboratory(NREL)dataset is used for experimental *** results show that the short-term wind power prediction method based on the combination of meteorological features and Cat Boost not only improve the prediction accuracy of short-term wind power,but also have higher calculation efficiency.

关键词： meteorological features short-term power load forecasting Cat Boost wind power

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Study on the Prediction of short-term power load Based on ECGWO-WDESN Combined Model

Study on the Prediction of Short-term Power Load Based on EC...

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第40届中国控制会议

作者： Huiwen Xia Ao Cao Qingyong Zhang College of Automation Wuhan University of Technology

According to the characteristics of power load data with non-linearity and many random factors, a prediction method that combines the Extended Chaos Gray Wolf Algorithm（ECGWO） with the Deep Echo State Network（DESN） and wavelet threshold denoising is proposed. Aiming at the problem of the Gray Wolf Algorithm（GWO） with strong development ability and weak exploration ability, the range constant in Kent chaos theory is extended to a dynamic factor to improve the position update formula of GWO, so as to increase the diversity of individuals and increase the probability of jumping out of local *** then the linear convergence factor of GWO is improved to enhance its exploration ability. In this paper, after denoising the original data with wavelet threshold, DESN is used to predict the denoised data, and ECGWO is used to optimize the parameters of the DESN to obtain the final prediction result. Experimental results show that this method can effectively improve the accuracy of power load forecasting.

关键词： short-term power load forecasting ECGWO wavelet denoising DESN model

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