An adaption scheduling based on dynamic weighted random forests for load demand forecasting

Chen, Mincheng; Yuan, Jingling; Liu, Dongling; Li, Tao

doi:10.1007/s11227-017-2223-3

Cited by 15 publications

(5 citation statements)

References 30 publications

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“…The followers monitor and compete with the discoverers to update the position using formula (10). According to the comparison results of f i and f g , use formula (11) to update the position of the watchers. 5.…”

Section: Ssa-rfr Charging Load Prediction Model Of Evsmentioning

confidence: 99%

“…Among them, the RFR model has the advantages of low generalization error, fast convergence speed, and few adjustment parameters, which can effectively avoid 'overfitting' phenomenon, and is widely used in the field of load prediction. Reference [11] obtained the time factor matching coefficient and calculated the weight by considering the day type and time span, and proposed a load demand forecasting algorithm based on weighted random forest. Reference [12] proposed a novel short-term load forecasting method combined with quantile regression random forest by quantifying the uncertainty of power load.…”

Section: Introductionmentioning

confidence: 99%

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Charging load forecasting of electric vehicles based on sparrow search algorithm‐improved random forest regression model

Wang

Cao

et al. 2023

The Journal of Engineering

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In order to solve the problem that the current charging load forecasting accuracy is not high, it is difficult to simulate the actual charging load distribution of Electric Vehicles (EVs), and it is impossible to reasonably predict the future load, a charging load forecasting model based on Sparrow Search Algorithm (SSA) improved Random Forest Regression (RFR) is proposed. The SSA is used to enhance the ability of global optimization and local exploration. Combined with the advantages of the RFR model, such as low generalization error, fast convergence speed, and few adjustment parameters, the SSA was used to optimize the parameters of the decision tree number and the number of split nodes in the RFR, and the optimal value of the parameters is obtained, so as to obtain the optimal performance of the RFR. Firstly, based on the concept of travel chain and conditional probability distribution, the user's travel habits are described. Monte Carlo simulation method was used to simulate the driving, parking, and charging behaviours of a large number of EVs in different regions, so as to obtain the charging load of EVs in different regions. Then, a charging load forecasting model based on SSA improved RFR is established. Monte Carlo simulation results are used as sample data to predict the charging load of EVs in different regions. Finally, taking a certain area as an example, the experimental results show that the charging load prediction model based on Sparrow Search Algorithm improved Random Forest Regression (SSA‐RFR) can accurately predict the charging load of EVs in different regions, and the charging load of different regional types is obviously different. Compared with the RFR model and other literature models, the SSA‐RFR model has better prediction accuracy, which verifies the feasibility and superiority of SSA‐RFR model in EVs charging load prediction.

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Section: Ssa-rfr Charging Load Prediction Model Of Evsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Charging load forecasting of electric vehicles based on sparrow search algorithm‐improved random forest regression model

Wang

Cao

et al. 2023

The Journal of Engineering

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show abstract

“…In recent years, machine learning and its applications in STLF have been developed rapidly, including artificial neural network (ANN), 4 support vector machine (SVM), 5 random forest, 6 radial basis function network (RBF), 7 etc. Recently, deep learning which is the latest development of machine learning has a huge impact in many fields.…”

Section: Introductionmentioning

confidence: 99%

Short‐term load forecasting based on strategies of daily load classification and feature set reconstruction

Zhao

Liu

et al. 2021

Int Trans Electr Energ Syst

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“…Based on the machine learning theory, the intelligent forecasting model can fit the nonlinear relationship between complex variables, thus improving the prediction effect. Common intelligent prediction methods include support vector machine technology [9], neural network [10,11], random forest [12], etc. However, these methods have strict requirements on the selection of features, requiring an experienced person to manually select the input features.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Short-Term Load Demand Forecast Model Framework Based on Deep Learning

Liu

et al. 2020

Energies

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Ultra-short-term load demand forecasting is significant to the rapid response and real-time dispatching of the power demand side. Considering too many random factors that affect the load, this paper combines convolution, long short-term memory (LSTM), and gated recurrent unit (GRU) algorithms to propose an ultra-short-term load forecasting model based on deep learning. Firstly, more than 100,000 pieces of historical load and meteorological data from Beijing in the three years from 2016 to 2018 were collected, and the meteorological data were divided into 18 types considering the actual meteorological characteristics of Beijing. Secondly, after the standardized processing of the time-series samples, the convolution filter was used to extract the features of the high-order samples to reduce the number of training parameters. On this basis, the LSTM layer and GRU layer were used for modeling based on time series. A dropout layer was introduced after each layer to reduce the risk of overfitting. Finally, load prediction results were output as a dense layer. In the model training process, the mean square error (MSE) was used as the objective optimization function to train the deep learning model and find the optimal super parameter. In addition, based on the average training time, training error, and prediction error, this paper verifies the effectiveness and practicability of the load prediction model proposed under the deep learning structure in this paper by comparing it with four other models including GRU, LSTM, Conv-GRU, and Conv-LSTM.

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An adaption scheduling based on dynamic weighted random forests for load demand forecasting

Cited by 15 publications

References 30 publications

Charging load forecasting of electric vehicles based on sparrow search algorithm‐improved random forest regression model

Charging load forecasting of electric vehicles based on sparrow search algorithm‐improved random forest regression model

Short‐term load forecasting based on strategies of daily load classification and feature set reconstruction

Ultra-Short-Term Load Demand Forecast Model Framework Based on Deep Learning

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