scholarly journals LightGBM Low-Temperature Prediction Model Based on LassoCV Feature Selection

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Shangqi Duan ◽  
Shuangde Huang ◽  
Wei Bu ◽  
Xingke Ge ◽  
Haidong Chen ◽  
...  
Keyword(s):  
Feature Selection ◽  
Low Temperature ◽  
Prediction Model ◽  
Minimum Temperature ◽  
Power Grid ◽  
Daily Minimum Temperature ◽  
Short Term ◽  
Temperature Prediction ◽  
Data Set ◽  
Daily Minimum

Icing disasters on power grid transmission lines can easily lead to major accidents, such as wire breakage and tower overturning, that endanger the safe operation of the power grid. Short-term prediction of transmission line icing relies to a large extent on accurate prediction of daily minimum temperature. This study therefore proposes a LightGBM low-temperature prediction model based on LassoCV feature selection. A data set comprising four meteorological variables was established, and time series autocorrelation coefficients were first used to determine the hysteresis characteristics in relation to the daily minimum temperature. Subsequently, the LassoCV feature selection method was used to select the meteorological elements that are highly related to minimum temperature, with their lag characteristics, as input variables, to eliminate noise in the original meteorological data set and reduce the complexity of the model. On this basis, the LightGBM low-temperature prediction model is established. The model was optimized through grid search and crossvalidation and validated using daily minimum surface temperature data from Yongshan County (station number 56489), Zhaotong City, Yunnan Province. The root mean square error, MAE, and MAPE of the model minimum temperature prediction after feature selection are shown to be 1.305, 0.999, and 0.112, respectively. These results indicate that the LightGBM prediction model is effective at predicting low temperatures and can be used to support short-term icing prediction.

Research on the Prediction Model of Urban Power Grid Short-Term Load Based on BP Neural Network Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 954-957
Author(s):  
Pei Long Xu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Power Grid ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Short Term ◽  
Network Algorithm ◽  
Neural Network Algorithm

Objective: The paper aims to establish the prediction model of urban power grid short-term load based on BP neural network algorithm. Method: Five factors influencing the urban power grid short-term load are used to establish the neural network model: date type, weather, daily maximum temperature, daily minimum temperature and daily average temperature. Matlab toolbox is used to develop the testing platform through VC++ programming. Result: The variable learning rates are 0.35 and 0.64. With 23410 iterations, the model is converged, and the global error is 0.00032. Conclusion: Through the data comparison and analysis, the relative error is within 5%, thus indicating the model is accurate and effective, and it can be used to predict the change of urban power grid short-term load.

scholarly journals Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days

Machines ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 80
Author(s):  
Yalong Li ◽  
Fan Yang ◽  
Wenting Zha ◽  
Licheng Yan
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Pearson Correlation ◽  
Combination Method ◽  
Random Fluctuation ◽  
Short Term ◽  
Data Set ◽  
Model Based

With the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict wind power. In order to improve the short-term prediction accuracy of regional wind power, this paper proposes a new combination prediction model based on convolutional neural network (CNN) and similar days analysis. Firstly, the least square fitting and batch normalization (BN) are used to preprocess the data, and then the recent historical wind power data set for CNN is established. Secondly, the Pearson correlation coefficient and cosine similarity combination method are utilized to find similar days in the long-term data set, and the prediction model based on similar days is constructed by the weighting method. Finally, based on the particle swarm optimization (PSO) method, a combined forecasting model is established. The results show that the combined model can accurately predict the future short-term wind power curve, and the prediction accuracy is improved to different extents compared to a single method.

scholarly journals Temperature Trend Analysis and Extreme High Temperature Prediction Based on Weighted Markov Model in Lanzhou

2021 ◽  
Author(s):  
Zhiqiang Pang ◽  
Zhaoxu Wang
Keyword(s):  
High Temperature ◽  
Markov Model ◽  
Prediction Model ◽  
Trend Analysis ◽  
High Temperatures ◽  
Minimum Temperature ◽  
Maximum Temperature ◽  
Temperature Prediction ◽  
Real Situation ◽  
Change Trend

Abstract In this study, temporIn this study, temporal trend analysis was conducted on the annual and quarterly meteorological variables of Lanzhou from 1951 to 2016, and a weighted Markov model for extremely high-temperature prediction was constructed. Several non-parametric methods were used to analyze the time trend. Considering that sequence autocorrelation may affect the accuracy of the trend test, we performed an autocorrelation test and carried out trend analysis for sequences with autocorrelation after removing correlation. The results show that the maximum temperature, minimum temperature, and average temperature in Lanzhou have a significant rising trend and show different performances in each season. In detail, the maximum temperature in summer does not have a significant change trend, while the minimum temperature in winter is the most significant rising trend, which leads to more and more ”warm winter” phenomenon. Finally, we construct a weighted Markov prediction model for extremely high temperatures and obtain the conclusion that the prediction results by the model are consistent with the real situation. and show different performances in each season. In detail, the maximum temperature in summer does not have a significant change trend, while the minimum temperature in winter is the most significant rising trend, which leads to more and more ”warm winter” phenomenon. Finally, we construct a weighted Markov prediction model for extremely high temperatures and obtain the conclusion that the prediction results by the model are consistent with the real situation.

scholarly journals Conditional probability of the daily minimum temperature in certain types of circulation on a seasonal basis in the Zywiec Valley in Southern Poland

2017 ◽  
Vol 13 ◽  
pp. 18-24
Author(s):  
Paulina Szczotka
Keyword(s):  
Atmospheric Circulation ◽  
Minimum Temperature ◽  
Extreme Values ◽  
Daily Minimum Temperature ◽  
Synoptic Situation ◽  
Local Conditions ◽  
Daily Minimum ◽  
Vistula River ◽  
Circulation Types ◽  
Average Minimum Temperature

Air minimum temperature is very important for the natural environment and human activity. This paper presents certain aspects related to the variability of daily minimum temperature of air in the winter (XII, I, II) in the Zywiec Valley, in relation to the synoptic situation in the valley. The analysis is based on the results of research carried out at one point node (the grid) obtained from the base Carpat Clim database. The node is located at the bottom of the Zywiec Valley in the period 1961-2010. The study was complemented with a comprehensive analysis of local conditions for atmospheric circulation and temporal variability over a 50 years period. For this purpose, the classification of types of atmospheric circulation  (Niedźwiedź 1981) was used for the upper Vistula river basin. Extreme temperatures included an average minimum temperature of air exceeding the 90th and 95th percentile. The relationship between the extremes of air temperature and atmospheric circulation types was examined by analyzing the frequency of occurrence of extreme values and their conditional occurrence in each particular type of atmospheric circulation.

scholarly journals Short-Term Load Forecasting Method Based on Deep Reinforcement Learning for Smart Grid

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wei Guo ◽  
Kai Zhang ◽  
Xinjie Wei ◽  
Mei Liu
Keyword(s):  
Reinforcement Learning ◽  
Smart Grid ◽  
Prediction Model ◽  
Network Model ◽  
Power Grid ◽  
Load Forecasting ◽  
Model Parameters ◽  
Short Term ◽  
Forecasting Method ◽  
Short Term Load Forecasting

Short-term load forecasting is an important part to support the planning and operation of power grid, but the current load forecasting methods have the problem of poor adaptive ability of model parameters, which are difficult to ensure the demand for efficient and accurate power grid load forecasting. To solve this problem, a short-term load forecasting method for smart grid is proposed based on multilayer network model. This method uses the integrated empirical mode decomposition (IEMD) method to realize the orderly and reliable load state data and provides high-quality data support for the prediction network model. The enhanced network inception module is used to adaptively adjust the parameters of the deep neural network (DNN) prediction model to improve the fitting and tracking ability of the prediction network. At the same time, the introduction of hybrid particle swarm optimization algorithm further enhances the dynamic optimization ability of deep reinforcement learning model parameters and can realize the accurate prediction of short-term load of smart grid. The simulation results show that the mean absolute percentage error e MAPE and root-mean-square error e RMSE of the performance indexes of the prediction model are 10.01% and 2.156 MW, respectively, showing excellent curve fitting ability and load forecasting ability.

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